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integration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Zenodo Tozer, Douglas; Bracey, Annie M.; Fiorino, Giuseppe E.; Gehring, Thomas M.; Giese, Erin E. Gnass; Niemi, Gerald J.; Wheelock, Bridget A.; Ethier, Danielle M.;Study Area and Design We conducted our study in coastal wetlands throughout the entire Great Lakes basin (see Figure 1 in Tozer et al.). We selected coastal wetlands using a stratified, random sampling protocol (Uzarski et al. 2017, 2019). Further details regarding the study design are in Burton et al. (2008). The sampling universe was all coastal wetlands greater than 4 ha in size with a permanent or periodic surface-water connection to an adjacent Great Lake or their connecting river systems (Uzarski et al. 2017). We stratified our selection of wetlands for the study by 1) wetland hydrogeomorphic type (riverine, lacustrine, barrier protected; Albert et al. 2005), 2) region (northern or southern; Danz et al. 2005), and 3) lake (i.e., the watershed of 1 of the 5 Great Lakes). We sampled approximately 20% of all wetlands in each stratum each year, so that nearly all coastal wetlands within the Great Lakes basin meeting the selection criteria were sampled at least once every 5 years. In addition, we resampled 10% of wetlands between years according to a rotating panel design. Sampled wetlands were dominated by emergent, herbaceous vegetation and shallow water ( 250 m apart to avoid double counting individuals. We surveyed each point count location twice per year, at least 15 days apart, between 20 May and 10 July, which was the peak breeding period for marsh birds in the study area. Surveys took place either in the morning (30 min before sunrise to 4 h after sunrise) or the evening (4 h before sunset to 30 min after sunset), with 1 or both of the 2 surveys being in the morning each year (Tozer et al. 2017). We conducted surveys only when there was no precipitation and wind was < 20 km/h (Beaufort 3 or less). Each point count survey lasted 10 min, consisting of an initial 5-min passive listening period followed by a 5-min call broadcast period. The call broadcast period was intended to increase detections of secretive species by eliciting auditory responses and was composed of 30 sec of vocalizations followed by 30 sec of silence for each of the following: 1) Least Bittern, 2) Sora, 3) Virginia Rail, 4) a mixture of American Coot and Common Gallinule, and 5) Pied-billed Grebe, in that order. We trained observers so they thoroughly understood the field protocols and we required each observer to pass an aural and visual bird identification test in order to collect data. CWMP bird surveys were 15 min in duration from 2011 to 2018 but were reduced to 10 min from 2019 to 2021 (Tozer et al. 2017). To accommodate changes in survey protocol, we filtered the data to only include birds detected in the first 10 min of point counts from 2011 to 2018. For a detailed description of the sampling protocol visit greatlakeswetlands.org/Sampling-protocols. Response Variable The response variable for each species was the maximum number of individuals observed during either of the 2 surveys at each point count location in each year (Tozer 2020, Hohman et al. 2021). We viewed these counts as indices of true density, meaning our modeled values estimated relative abundance (e.g., Thogmartin et al. 2004). We assumed that variation in species-specific detection was uncorrelated with the predictors in our models, including year. This was sufficient in our case because our objective was to quantify relative differences and changes in abundance and not to quantify actual density. Our assumption was warranted because our data were collected using standardized methods designed to reduce heterogeneity in detection, e.g., observer training and testing, as well as restrictions on survey date, time of day, and wind (Conway 2011, Uzarski et al. 2017). It was further justified by other long-term, broad-scale studies of birds based on point counts conducted using similar standardized approaches, which found no differences in year or covariate effects based on counts that were adjusted or unadjusted for detection (Etterson et al. 2009, Zlonis et al. 2019). We note that long-term (1996–2013) marsh-breeding bird monitoring data collected throughout the developed, southern portion of the Great Lakes basin showed no systematic trends in detectability over time for 14 of 15 (93%) species (Tozer 2016). We also found no trends in detectability across years for all of the species in our dataset (see Supplemental Material Figure S1 in Tozer et al.), meaning that differences in detection did not bias our estimates of annual abundance indices or trends. Therefore, we did not adjust for detectability, which has been supported, for instance, by Hutto (2016) and Johnson (2008). The dataset consisted of 8,120 surveys completed at 1,962 point count locations in 792 coastal wetlands in 599 watersheds (defined by Forsyth et al. [2016]) over 11 years (2011–2021; see Figure 1, 2 and Supplemental Material Table S1 in Tozer et al.). There were 2.2 ± 1.6 (mean ± SD) point count locations per wetland (range: 1–8) and 1.3 ± 0.9 wetlands per watershed (range: 1–9). In total, we analyzed 18 species: 1) American Bittern, 2) American Coot, 3) Black Tern, 4) Common Gallinule, 5) Common Grackle, 6) Common Yellowthroat, 7) Forster's Tern, 8) Least Bittern, 9) Marsh Wren, 10) Mute Swan, 11) Pied-billed Grebe, 12) Red-winged Blackbird, 13) Sandhill Crane, 14) Sedge Wren, 15) Sora, 16) Swamp Sparrow, 17) Virginia Rail, and 18) Wilson's Snipe. We chose these species because they were of conservation interest in the Great Lakes region (e.g., Bianchini and Tozer 2023) and regularly nested or foraged in Great Lakes coastal wetlands. We attempted to model abundance and trends for Trumpeter Swan (Cygnus buccinator) and Yellow-headed Blackbird (Xanthocephalus xanthocephalus), but data were too sparse for the models to converge. We considered some regions of our study area to be out of range for some species. We accounted for this by dividing our study area into 10 regions and dropped any of them from species-specific analyses if naive occupancy was < 5% (Supplemental Material Table S2). By excluding out-of-range point count locations, we reduced the number of zero counts and focused our analysis on point count locations where zero counts were most likely to represent legitimate absences. As a result, the number of marsh-breeding bird species for which we quantified abundance and trends varied by lake due to uneven species occurrences across the study area: Superior (n = 10), Ontario (n = 12), Erie (n = 16), Huron (n = 16), and Michigan (n = 17). The CWMP bird survey data are available by request at greatlakeswetlands.org. Environmental Predictors We included the following environmental predictors in our models, which were known to influence abundance of marsh-breeding birds in the Great Lakes: 1) percent local wetland cover within 250 m of point count locations (as a proxy for wetland size; e.g., Studholme et al. 2023), 2) detrended, standardized Great Lakes water levels (to avoid correlation with year; e.g., Hohman et al. 2021, Denomme-Brown et al. 2023), 3) percent urban land cover in the surrounding watershed (e.g., Rahlin et al. 2022), and 4) percent agricultural land cover in the surrounding watershed (e.g., Saunders et al. 2019). The land cover predictors were static covariates (i.e., they were the same for all years), whereas detrended, standardized Great Lakes water level was a dynamic covariate (i.e., it varied annually). Land cover and water-level information at finer spatial and temporal scales would have been preferred, but such data were unavailable. Nonetheless, it is reasonable to assume that the land cover and water-level data we used provided useful approximations of the true values, particularly at the watershed scale (e.g., Michaud et al. 2022). Percent local wetland cover was based on the coastal wetland layer built by the Great Lakes Coastal Wetland Consortium (Burton et al. 2008, Uzarski et al. 2017), and percent urban and agricultural land cover were from Host et al. (2019) with watersheds defined by Forsyth et al. (2016); all of these data are available at glahf.org/data. We used ArcGIS 10.8.1 to overlay CWMP sample points onto the land cover layers and extracted the relevant predictors for each point (see Figure 3 in Tozer et al.). Yearly water levels were from the National Oceanic and Atmospheric Administration (noaa.gov). We used the mean yearly water level from May to July since these months overlapped with our survey period. We detrended water levels from year by using the residuals from a line of best fit for each lake, given that water levels generally increased in all lakes over the course of the study. Water levels were also standardized across lakes by dividing the annual value for each lake by the long-term mean (2011–2021) for each lake, given the reference value is the same for all lakes (International Great Lakes Datum 1985). Our detrended, standardized lake levels therefore represent water levels without being confounded with year (see Figure 4 in Tozer et al.). The environmental predictors were not correlated (-0.2 < r < 0.2; see Supplemental Material Figure S2 in Tozer et al.). Statistical Modeling We fit models in a Bayesian framework with Integrated Nested Laplace Approximation (INLA) using the R-INLA package (Rue and Martino 2009) for R statistical computing (version 4.2.0; R Core Team 2022). For each species, we modeled the expected (predicted mean) number of individuals per point count location in each Great Lake in each year, as well as the trend in these values across years in each lake, and then pooled the lake-specific trends to obtain Great Lakes-wide estimates. We included spatial structure in the models using an intrinsic conditional autoregressive (iCAR) structure (Besag et al. 1991), which allowed for information on relative abundance to be shared across lakes sharing basin boundaries. By accounting for this spatial structure in counts, the model allowed abundance and trend information to be shared among adjacent lakes (as described below), which improved estimates for lakes with limited sample sizes (Bled et al. 2013) and reduced the amount of spatial autocorrelation in model residuals (Zuur et al. 2017). We modeled counts уi,j,t using the maximum number of individuals observed at a point count location within a given wetland (j), lake (i), and year (t). The expected counts per lake within a given year µi,t for each of the 18 species took the form: log(µit) = αi + τiΤi,j,t + κj + ρj + уi,t + β1Wj + β2Lj + β3Uj + β4Ai where α = random lake intercept; T = year, indexed to 2021; τ = random lake slope effect; κ = random wetland effect; ρ = random wetland type effect; and у = random lake-year effect. Environmental predictors included: W = percent local wetland cover within 250 m; L = detrended, standardized water level; U = percent urban land cover in the surrounding watershed; and A = percent agricultural land cover in the surrounding watershed. The random lake intercept (αi) had an iCAR structure, where values of αi came from a normal distribution with a mean value related to the average of adjacent lakes. The random lake intercept also had a conditional variance proportional to the variance across adjacent lakes and inversely proportional to the number of adjacent lakes. We modeled the random lake slopes (τi) as spatially structured, lake-specific, random slope coefficients for the year effect, using the iCAR structure, with conditional means and variances as described above. We incorporated spatial structure into the random lake slopes (τi) to allow for information about year effects to be shared across neighboring lakes, and to allow year effects to vary among lakes. We transformed year (T) such that the maximum year was 0, and each preceding year was a negative integer. This scaling meant that the estimates of the random lake intercepts (αi) could be interpreted as the lake-specific expected counts (i.e., index of abundance) during the final year of the time series. We accounted for differences in relative abundance among wetlands (κ) and wetland types (ρ) with an independent and identically distributed (idd) random effect. To derive an annual index of abundance per lake, we included a random effect per lake-year (у) with an idd, and combined these effects with α and τ. Β1, β2, β3, and β4 were given normal priors with mean of zero and precision equal to 0.001. We scaled the spatial structure parameters α and τ such that the geometric mean of marginal variances was equal to one (Sørbye and Rue 2014, Riebler et al. 2016, Freni-Sterrantino et al. 2018), and priors for precision parameters were penalized complexity (PC) priors, with parameter values UPC = 1 and PC = 0.01 (Simpson et al. 2017). We also assigned precision for the random wetland, wetland type, and lake-year effects with a PC prior with parameter values previously stated. In general, the weakly informed priors used here tend to shrink the structured and unstructured random effects towards zero in the absence of a strong signal (Simpson et al. 2017). We validated distributional assumptions with simulation to ensure models could handle the large number of zero counts for some species. The abundance of most species was modeled using a zero-inflated Poisson (ZIP) distribution. Common Grackle and Red-winged Blackbird, which were more frequently detected compared to the other species, better fit a negative binomial distribution, and Common Yellowthroat better fit a Poisson distribution. We further validated models by visually inspecting 1) the fit versus raw counts; 2) residuals versus predictors; and 3) the estimate for Ф, the dispersion parameter (Zuur and Ieno 2016). Our visual inspections of fit versus raw counts suggested models were not overfit and were able to capture the variation of the raw counts. In general, residuals versus fit values behaved randomly around the zero line and residuals appeared to behave randomly with each predictor, suggesting the models fit well. The dispersion statistics were around 1 for all species, ranging lowest for Common Yellowthroat (0.72) and highest for Mute Swan (3.38), suggesting some residual under and over dispersion, respectively. Mute Swan had some high counts (outliers) which may have contributed to this. Following model analysis, we computed posterior estimates of trends (τ) and associated credible intervals for the full extent of the study area (i.e., by pooling lake-specific trends) using lake watershed size to calculate area-weighted averages (Link and Sauer 2002). References Albert, D. A., D. A. Wilcox, J. W. Ingram, and T. A. Thompson (2005). Hydrogeomorphic classification for Great Lakes coastal wetlands. Journal of Great Lakes Research 31:129–146. Besag, J., J. York, and A. Mollié (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43:1–20. Bianchini, K., and D. C. Tozer (2023). Using Breeding Bird Survey and eBird data to improve marsh bird monitoring abundance indices and trends. Avian Conservation and Ecology 18(1):4. Bled, F., J. Sauer, K. Pardieck, P. Doherty, and J. A. Royle (2013). Modeling trends from North American breeding bird survey data: a spatially explicit approach. PLoS ONE 8, e81867. Burton, T. M., J. C. Brazner, J. J. H. Ciborowski, G. P. Grabas, J. Hummer, J. Schneider, and D. G. Uzarski (Editors) (2008). Great Lakes Coastal Wetlands Monitoring Plan. Developed by the Great Lakes Coastal Wetlands Consortium, for the US EPA, Great Lakes National Program Office, Chicago, IL. Great Lakes Commission, Ann Arbor, Michigan, USA. Conway, C. J. (2011). Standardized North American marsh bird monitoring protocol. Waterbirds 34:319–346. Danz, N. P., R. R. Regal, G. J. Niemi, V. J. Brady, T. Hollenhorst, L. B. Johnson, G. E. Host, J. M. Hanowski, C. A. Johnston, T. Brown, J. Kingston, and J. R. Kelly (2005). Environmentally stratified sampling design for the development of Great Lakes environmental indicators. Environmental Monitoring and Assessment 102:41–65. Denomme-Brown, S. T., G. E. Fiorino, T. M. Gehring, G. J. Lawrence, D. C. Tozer, and G. P. Grabas (2023). Marsh birds as ecological performance indicators for Lake Ontario outflow regulation. Journal of Great Lakes Research 49:479–490. Etterson, M. A., G. J. Niemi, and N. P. Danz (2009). Estimating the effects of detection heterogeneity and overdispersion on trends estimated from avian point counts. Ecological Applications 19:2049–2066. Forsyth, D. K., C. M. Riseng, K. E. Wehrly, L. A. Mason, J. Gaiot, T. Hollenhorst, C. M. Johnston, C. Wyrzykowski, G. Annis, C. Castiglione, K. Todd, et al. (2016) The Great Lakes hydrography dataset: consistent, binational watersheds for the Laurentian Great Lakes basin. Journal of the American Water Resources Association 52:1068–1088. Freni-Sterrantino, A., M. Ventrucci, and H. Rue (2018). A note on intrinsic conditional autoregressive models for disconnected graphs. Spatial and Spatio-temporal Epidemiology 26:25–34. Hohman, T. R., R. W. Howe, D. C. Tozer, E. E. Gnass Giese, A. T. Wolf, G. J. Niemi, T. M. Gehring, G. P. Grabas, and C. J. Norment (2021). Influence of lake-levels on water extent, interspersion, and marsh birds in Great Lakes coastal wetlands. Journal of Great Lakes Research 47:534–545. Host, G. E., K. E. Kovalenko, T. N. Brown, J. J. H. Ciborowski, and L. B. Johnson (2019). Risk-based classification and interactive map of watersheds contributing anthropogenic stress to Laurentian Great Lakes coastal ecosystems. Journal of Great Lakes Research 45:609–618. Hutto, R. L. (2016). Should scientists be required to use a model-based solution to adjust for possible distance-based detectability bias? Ecological Applications 26:1287–1294. Johnson, D. H. (2008). In defense of indices: the case of bird surveys. Journal of Wildlife Management 72:857–868. Link, W. A., and J. R. Sauer (2002). A hierarchical analysis of population change with application to Cerulean Warblers. Ecology 83:2832–2840. Michaud, W., J. Telech, M. Green, B. Daneshfar, and M. Pawlowski (2022). Sub-indicator: land cover. In State of the Great Lakes 2022 Technical Report. Published by Environment and Climate Change Canada and U.S. Environmental Protection Agency. R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Rahlin, A. A., S. P. Saunders, and S. Beilke (2022). Spatial drivers of wetland bird occupancy within an urbanized matrix in the upper midwestern United States. Ecosphere 13, e4232. Riebler, A., S. H. Sørbye, D. Simpson, and H. Rue (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research 25:1145–1165. Rue, H., S. Martino, and N. Chopin (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society Series B (Statistical Methodology) 71:319–392. Saunders, S. P., K. A. L. Hall, N. Hill, and N. L. Michel (2019). Multiscale effects of wetland availability and matrix composition on wetland breeding birds in Minnesota, USA. Condor 121:duz024. Simpson, D., H. Rue, A. Riebler, T. G. Martins, and S. H. Sørbye (2017). Penalising model component complexity: a principled, practical approach to constructing priors. Statistical Science 32:1–28. Sørbye, S. H., and H. Rue (2014). Scaling intrinsic Gaussian Markov random field priors in spatial modeling. Spatial Statistics 8:39–51. Studholme, K. R., G. E. Fiorino, G. P. Grabas, and D. C. Tozer (2023). Influence of surrounding land cover on marsh-breeding birds: implications for wetland restoration and conservation planning. Journal of Great Lakes Research 49:318–331. Thogmartin, W. E., J. R. Sauer JR, and M. G. Knutson (2004). A hierarchical spatial model of avian abundance with application to Cerulean Warblers. Ecological Applications 14:1766–1779. Tozer, D. C. (2016). Marsh bird occupancy dynamics, trends, and conservation in the southern Great Lakes basin: 1996 to 2013. Journal of Great Lakes Research 42:136–145. Tozer, D. C. (2020). Great Lakes Marsh Monitoring Program: 25 years of conserving birds and frogs. Birds Canada, Port Rowan, Ontario, Canada. Tozer, D. C., C. M. Falconer, A. M. Bracey, E. E. Gnass Giese, G. J. Niemi, R. W. Howe, T. M. Gerhing, and C. J. Norment (2017). Influence of call broadcast timing within point counts and survey duration on detection probability of marsh breeding birds. Avian Conservation and Ecology 12(2):8. [Tozer et al.] Tozer DC, Bracey AM, Fiorino GE, Gehring TM, Gnass Giese EE, Grabas GP, Howe RW, Lawrence GJ, Niemi GJ, Wheelock BA, Ethier DM. Increasing marsh bird abundance in coastal wetlands of the Great Lakes, 2011–2021, likely caused by increasing water levels. Ornithological Applications. Uzarski, D. G., D. A. Wilcox, V. J. Brady, M. J. Cooper, D. A. Albert, J. J. H. Ciborowski, N. P. Danz, A. Garwood, J. P. Gathman, T. M. Gehring, G. P. Grabas, et al. (2019). Leveraging a landscape-level monitoring and assessment program for developing resilient shorelines throughout the Laurentian Great Lakes. Wetlands 39:1357–1366. Uzarski, D. G., V. J. Brady, M. J. Cooper, D. A. Wilcox, D. A. Albert, R. P. Axler, P. Bostwick, T. N. Brown, J. J. H. Ciborowski, N. P. Danz, J. P. Gathman, et al. (2017). Standardized measures of coastal wetland condition: implementation at a Laurentian Great Lakes basin-wide scale. Wetlands 37:15–32. Zlonis, E. J., N. G. Walton, B. R. Sturtevant, P. T. Wolter, and G. J. Niemi (2019). Burn severity and heterogeneity mediate avian response to wildfire in a hemiboreal forest. Forest Ecology and Management 439:70–80. Zuur, A. F., and E. I. Ieno (2016). A protocol for conducting and presenting results of regression-type analyses. Methods in Ecology and Evolution 7:636–645. Zuur, A. F., E. I. Ieno, and A. A. Saveliev (2017). Beginner's guide to spatial, temporal and spatial-temporal ecological data analysis with R-INLA. Volume I: Using GLM and GLMM. Highland Statistics, Newburgh, United Kingdom. Wetlands of the Laurentian Great Lakes of North America, i.e., lakes Superior, Michigan, Huron, Erie, and Ontario, provide critical habitat for marsh birds. We used 11 years (2011–2021) of data collected by the Great Lakes Coastal Wetland Monitoring Program at 1,962 point count locations in 792 wetlands to quantify the first-ever annual abundance indices and trends of 18 marsh-breeding bird species in coastal wetlands throughout the entire Great Lakes. Nine species (50%) increased by 8–37% per year across all of the Great Lakes combined, whereas none decreased. Twelve species (67%) increased by 5–50% per year in at least 1 of the 5 Great Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at least 1 of the lakes. There were more positive trends among lakes and species (n = 34, 48%) than negative trends (n = 5, 7%). These large increases are welcomed because most of the species are of conservation concern in the Great Lakes. Trends were likely caused by long-term, cyclical fluctuations in Great Lakes water levels. Lake levels increased over most of the study, which inundated vegetation and increased open water-vegetation interspersion and open water extent, all of which are known to positively influence abundance of most of the increasing species and negatively influence abundance of all of the decreasing species. Coastal wetlands may be more important for marsh birds than once thought if they provide high-lake-level-induced population pulses for species of conservation concern. Coastal wetland protection and restoration are of utmost importance to safeguard this process. Future climate projections show increases in lake levels over the coming decades, which will cause "coastal squeeze" of many wetlands if they are unable to migrate landward fast enough to keep pace. If this happens, less habitat will be available to support periodic pulses in marsh bird abundance, which appear to be important for regional population dynamics. Actions that allow landward migration of coastal wetlands during increasing water levels by removing or preventing barriers to movement, such as shoreline hardening, will be useful for maintaining marsh bird breeding habitat in the Great Lakes. Funding provided by: Long Point Waterfowl and Wetlands Research Program of Birds Canada*Crossref Funder Registry ID: Award Number: Funding provided by: Environment and Climate Change CanadaCrossref Funder Registry ID: https://ror.org/026ny0e17Award Number: 3000747437 Funding provided by: Wildlife Habitat Canada (Canada)Crossref Funder Registry ID: https://ror.org/0156t7498Award Number: 23-300 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency**Crossref Funder Registry ID: Award Number: GL-00E00612-0 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency*Crossref Funder Registry ID: Award Number: 00E01567 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency*Crossref Funder Registry ID: Award Number: 00E02956
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2020Publisher:Zenodo Funded by:EC | FIThydroEC| FIThydrovan Treeck, Ruben; Radinger, Johannes; Noble, Richard; Geiger, Franz; Wolter, Christian;Hydroelectricity is critical for decarbonizing global energy production, but hydropower plants affect rivers, disrupt their continuity, and threaten migrating fishes. This puts hydroelectricity production in conflict with efforts to protect threatened species and re-connect fragmented ecosystems. Assessing the impact of hydropower on fishes will support informed decision-making during planning, commissioning, and operation of hydropower facilities. Few methods estimate mortalities of single species passing through hydropower turbines, but no commonly agreed tool assesses hazards of hydropower plants for fish populations. The European Fish Hazard Index bridges this gap. This assessment tool for screening ecological risk considers constellation specific effects of plant design and operation, the sensitivity and mortality of fish species and overarching conservation and environmental development targets for the river. Further, it facilitates impact mitigation of new and existing hydropower plants of various types across Europe. The tool does not yet support VBAs. In order to use it and produce reliable results, all input fields have to be reset manually before making a new assessment. The input window contains examplary dummy data.
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Code Ocean Authors: Ziwei Dai; Zhiyong Zhang; Mingzhou Chen ;This paper proposes a home health care location-routing problem with a mixed fleet of electric and conventional vehicles that considers battery swapping stations. It aims to simultaneously determine the locations of HHC centers, the scheduling of caregivers with respect to skill requirements, and a routing plan for a mixed fleet under specific time windows, load capacities, synchronized visits, and driving ranges. To address this problem, the paper proposes a novel competitive simulated annealing (CSA) algorithm in which a series of problem-specific effective local search operators expand the solution space of the CSA algorithm, with a competitive mechanism to adaptively adjust these operators to accelerate convergence speed and improve exploration ability. To enhance the exploitation ability, it employs a modified simulated annealing algorithm with a heating strategy and variable neighborhood descent. The code of competitive simulated annealing algorithm is provided here in order to address home health care location-routing problem with a mixed fleet and battery swapping stations
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Zenodo Authors: Hansen, Carly; Matson, Paul;These scripts, R project, and accompanying data files document the exploration of reservoir archetypes from the perspective of morphology and climate. We used Archetypal Analysis to identify extremes representing the diversity and makeup of US hydropower reservoirs, limited to those included in the LAGOS-US dataset. This analysis supports evaluation of reservoir diversity and describes the intersection between reservoir similarity and changing climate. High variability in local climate conditions and projected changes in climate conditions may complicate assumptions about similarity in biogeochemical processes (such as greenhouse gas emissions) even among reservoirs that are otherwise similar in shape and watershed setting. Input datasets are derived from: Hansen, C.H. and Matson, P.G. 2023. Hydropower Infrastructure - LAkes, Reservoirs, and RIvers (HILARRI), V2. HydroSource. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. DOI: https/doi.org/10.21951/HILARRI/1960141 Smith, N.J., K.E. Webster, L.K. Rodriguez, K.S. Cheruvelil, and P.A. Soranno. 2021. LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S. ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/e5c2fb8d77467d3f03de4667ac2173ca (Accessed 2023-04-13). Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321. doi:10.1002/2013EO370002 Prairie, Yves T., Mercier-Blais, Sara, Harrison, John A., Soued, Cynthia, Del Giorgio, Paul A., Harby, Atle, Alm, Jukka, Chanudret, Vincent, & Nahas, Roy. (2021). G-res tool modelling database [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4711132 Deemer, Bridget R. et al. (2020), Data from: Greenhouse gas emissions from reservoir water surfaces: a new global synthesis, Dryad, Dataset, https://doi.org/10.5061/dryad.d2kv0
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Zenodo Authors: Eisenschmid, Karolin; Jabbusch, Sarina; Koch, Marcus;As global warming progresses, plants may be forced to adapt to drastically changing environmental conditions. Arctic-alpine plants have been among the first to experience the effects of climate change. As a result, cold acclimation and freezing tolerance may become increasingly crucial for the survival as winter warming events and earlier snowmelt will cause increased exposure to occasional frost. The tribe Cochlearieae in the mustard family (Brassicaceae) offers an instructive system for studying cold adaptation in evolutionary terms, as the two sister genera Ionopsidium and Cochlearia are distributed among different ecological habitats throughout the European continent and the far north into circumarctic regions. By applying an electrolyte leakage assay to leaves obtained from plants cultivated under controlled temperature regimes in growth chambers, the freezing tolerance of different Ionopsidium and Cochlearia species was assessed measuring lethal freezing temperature values (LT50 and LT100), thereby allowing for a comparison across different species and accessions in their responses to cold. We hypothesized that, owing to varying selection pressures, geographically distant species would differ in freezing tolerance. Despite Ionopsidium occurring under warm and dry Mediterranean conditions and Cochlearia species distributed often at cold habitats, all accessions exhibited similar cold responses. The results may indicate that physiological adaptations of primary metabolic pathways to different stressors, such as salinity and drought, may confer an additional tolerance to cold; this is because all these stressors induce osmotic challenges. Data can be accessed using microsoft word office and excel.Funding provided by: Deutsche ForschungsgemeinschaftCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001659Award Number: KO2302/23-2 Electrolyte leakage analysis of single leafs.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2022Publisher:Code Ocean Authors: Brown, Paul D.; Göl, Murat;A demonstration agricultural microgrid containing solar photovoltaic (PV), battery storage system (BSS) and multiple water pumps and reservoirs is presented. A mathematical model of the cost of operating the demonstration microgrid is developed. The mathematical model includes hybrid inverter source switching and BSS charging modes in addition to power balance and inter-period energy and water-level coupling. Electricity pricing and irrigation water use efficiency are allowed to vary by time of day. The mathematical model is formulated as a mixed-integer linear program (MILP), implemented in Python using Pyomo, and optimized using the open-source SCIP solver to plan pumping and water usage. Estimated data for a demonstration system at a farm in Turkey is used to demonstrate the proposed model. Results of the optimization of the demonstration system show intuitive results that are superior to a rule-based initialization. The model may serve as the basis for model predictive control (MPC) or stochastic model predictive control (SMPC).
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2022Publisher:Zenodo Kastl, Brian; Obedzinski, Mariska; Carlson, Stephanie; Boucher, William; Grantham, Ted;Runoff and water temperature data We estimated mean annual precipitation, averaged across each drainage area, using Google Climate Engine, March 2011 - February 2021. Where multiple temperature loggers were present in a study stream, we selected a single location based on the completeness of data in the study season and proximity to the PIT antenna. Hourly temperature measurements were converted into mean daily values. Analysis For data analysis and modeling, we excluded streams that had less than 3 years of biological data, leaving 47 stream-years. We conducted all analyses in R (version 4.0.4, R Core Team, 2018). We tested outmigration timing data for normal distribution among streams, years, and stream-years, using the shapiro.test function of the broom package. The Shapiro-Wilk test showed that all distributions were unlikely to be normally distributed (i.e. among years, p = 5.5 × 10-9 – 7.6 × 10-39 and W = 0.88 – 0.98). However, the Shapiro-Wilk test can provide small p-values for large samples and consequently provide a false negative, regarding normal distribution (among years, sample size range was 485 – 3453). Therefore, we could not rule out the possibility of assumptions being met to perform ANOVA (Analysis of Variance) tests. We did so, using the aov function of the AICcmodavg package: i) one-way, by stream, ii) a one-way, by year, iii) a two-way, by stream and year, and iv) a two-way with stream-year interaction. To isolate the effects of stream and year on variance, we performed the ANOVA tests on the maximum subset of data for which each stream had the same years of outmigration (four streams, each with the same six years of data, totaling 24 stream-years). The aictab function of the AICcmodavg package demonstrated that the two-way model with stream-year interaction was the highest performing (lowest AICc value), followed by: the two-way model, one-way by year model, and one-way by stream model. In both ANOVA tests, the year, stream, and year-stream interaction terms each had "Pr(>F)" values < 2 × 10-16. The "2-way ANOVA with interaction" (year F-value 646.58, stream F-value 349.85, year-stream interaction F-value 29.31, residuals 4.11 × 10-16) had higher F values and lower residuals than the 2-way ANOVA (year F-value 629.3, stream F-value 340.5, residuals 4.22 × 10-16). We used the TukeyHSD function of the AICcmodavg package to conduct pairwise tests for significant differences in outmigration timing distributions. Among streams, five of six pairwise differences were highly significant (p < 0.0001). Among years, all 15 pairwise comparisons were highly significant (p < 0.001). Among stream-years, 216 of 277 pair-wise comparisons were significant (p < 0.05). We checked for homoscedasticity in the interaction model, using the leveneTest function of the car library, and we found evidence that the variance across groups is significantly different. Consequently, we cannot assume homogeneity of variances in the different groups, which is typically a required assumption for conducting ANOVA tests. Since the normal distribution assumption of the one-way ANOVA was not met, we applied the Kruskal-Wallis test, as a non-parametric alternative to test for variance among streams and years, using the package rstatix. As with the ANOVA tests, we performed Kruskal-Wallis tests on the maximum subset of data for which each stream had the same years of outmigration (24 stream-years), using the functions kruskal_test, kruskal_effsize, dunn_test, and wilcox_test. Among streams, we found significant variance (p = 2.16 × 10-143), with a "small" effect size (eta-squared measure = 0.04) (Tomczak and Tomczak 2014), and 5 of 6 pairwise differences were highly significant (Dunn's test & Wilcoxon's test: p < 0.0001). Among years, we found significant variance (p = 0), with a "large" effect size (eta-squared measure = 0.17) (Tomczak and Tomczak 2014), and 13 of 15 pairwise differences were highly significant (Dunn's test & Wilcoxon's test: p < 0.0001). Modeling the effects of streamflow and water temperature on outmigration timing Modeling was limited to the 42 stream-years for which water temperature and outmigration timing data were collected. For the outmigration start date model, the runoff date range was March-April and the degree-days date range was March-April. For the outmigration end date and duration models, the runoff date range was March-June and the degree-days date range was March-April. Coefficient units are "days per daily runoff (mm)" and "days per 100 degree-days". In identifying top model(s), we did not consider degree-days to influence outmigration duration because: i) the AIC value of the runoff-only model was 1.99 less than the additive model, ii) the degree-days in the additive model had a p-value > 0.05, and iii) Mar-Jun runoff had similar coefficient effect sizes in the additive model and run-off only model (Appendix S1: Table S3). We calculated conditional coefficients (including stream, as a random effect) and marginal coefficients (excluding stream, as a random effect) of determination (R2) (Nakagawa and Schielzeth 2013), using the r.squaredGLMM function of the MuMIn package (Barton` 2020). We also reported the model coefficients and 95% confidence intervals, as measures of effect size, and generated partial dependence plots for using the plot_model function of the sjPlot package (Lüdecke 2021). Literature cited Barton`, K. (2020). MuMIn: Multi-Model Inference. R package version 1.43.17. Lüdecke, D. (2021). sjPlot: Data Visualization for Statistics in Social Science. R package version 2.8.9. Nakagawa, S., and H. Schielzeth. 2013. A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4:133–142. Tomczak, M., and E. Tomczak. 2014. The need to report effect size estimates revisited. An overview of some recommended measures of effect size 1:7. Prolonged migration windows buffer migratory animal populations against uncertainty in resource availability. Understanding how intensifying droughts from climate change influence the migration window is critical for biodiversity conservation in a warming world. We explored how drought affects the seaward migration of endangered coho salmon (Oncorhynchus kisutch) near the southern extent of their range in California, USA. We tracked stream departures of juvenile coho, measuring streamflow and temperature in 7 streams over 13 years, spanning an historic drought with extreme dry and warm conditions. Linear mixed effects models indicate that, over the range of observations, a decrease in seasonal streamflow (from 4.5 to 0.5 mm/day seasonal runoff) contracted the migration window by 31% (from 11 to 7 weeks). An increase from 10.2 to 12.8 ℃ in mean seasonal water temperature hastened the migration window by three weeks. Pacific salmon have evolved to synchronize ocean arrival with productive ocean upwelling. However, earlier and shorter migration windows during drought could lead to mismatches, decreasing fitness and population stability. Our study demonstrates that drought-induced low flows and warming threaten coho salmon in California and suggests that environmental flow protections will be needed to support the seaward migration of Pacific salmon in a changing climate. Please see DataS1/data/README_Metadata.pdf.Funding provided by: California Department of Fish and WildlifeCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006238Award Number: Funding provided by: California Sea Grant, University of California, San DiegoCrossref Funder Registry ID: http://dx.doi.org/10.13039/100005522Award Number: Graduate Research Fellowship R/AQ-153FFunding provided by: National Geographic SocietyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006363Award Number: EC-53369R-18Funding provided by: National Oceanic and Atmospheric AdministrationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000192Award Number: Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: Graduate Research Fellowship DGE 1752814Funding provided by: Sonoma Fish and Wildlife Commission*Crossref Funder Registry ID: Award Number: Funding provided by: U.S. Army Corps of EngineersCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006752Award Number:
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2022Publisher:CyVerse Data Commons Authors: Triplett, Amanda;doi: 10.25739/kmk7-b046
"The input data and scripts necessary to run the ParFlow hydrologic model of the middle Heihe River Basin and produce all figures in the paper submission of "climate warming-driven changes in the cryosphere and their impact on groundwater-surface water interactions in the heihe river basin""
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Zenodo Authors: Crockett, Joseph;We obtained 135 one growth year old seedlings of each species (675 seedlings total) from the New Mexico State University John T. Harrington Forestry Research Center in Mora, NM between April 2020 and September 2021. Seedlings were grown from locally sourced seeds from mature trees in northern New Mexico to ensure that they represented local adaptation to conditions. Seedlings were grown in a greenhouse in 10 cm containers at staggered intervals to ensure that they were of similar age and size when they were placed in the incubators. We transplanted seedlings into 22 cm deep pots (volume: 590 cm3) with well-drained soil (2 parts sphagnum moss, 1.5 parts vermiculate, 1.5 parts sand) brought to field capacity following transplanting. We allowed soil moisture to draw down to the treatment level, measuring soil moisture gravimetrically. Once soil moisture matched the treatment condition, we randomly assigned seedlings to one of two Percival Model E-36L1 incubators. We intended to use 15 seedlings per species per temperature/moisture combination, but several seedlings died during moisture drawdown resulting in several treatments using fewer than 15. Incubator rack positions were adjusted to ensure that seedlings in each incubator received equivalent photosynthetically active radiation (~260 mol). We programmed temperature treatments to follow a diurnal cycle with lower temperatures at night (15C) and progressive steps to treatment temperatures during the day. We set photoperiods at 15/9 hours to reflect growing season conditions. Incubators controlled temperature and light. We placed iButtons (Model number DS1923; Temperature accuracy +/- 0.5C; humidity resolution 0.6%; https://www.maximintegrated.com/en/products/ibutton-one-wire/data-loggers/DS1923.html) within each incubator to record the actual temperature and humidity at hourly intervals. Temperatures matched the programmed values and humidity was highest at the start of each stage of the experiment and decreased as moisture was lost to evapotranspiration. We calculated Vapor Pressure Deficit (VPD, kPa) at each time step as the difference between saturated and effective water pressure of the air. We assessed seedling health weekly with visual assessments of needle coloration and by measuring leaf fluorescence with a MultispeQ v2.0 fluorometer (Guadagno et al. 2017). The efficiency of light adapted photosynthetic reaction centers (measured as a ratio of Fv* to Fm*) corresponds well to destructive measures of cell conductance yet provides a non-destructive, rapid assessment of plant death with greater accuracy than visual assessment of foliage color. We determined plant death as either 95% brown/grey needle coloration or below 0.1 Fv*/Fm*. At plant death, we recorded time between treatment start date and seedling death to express results in days until death. Methods for processing the data: To test the physiological tolerances of seedlings from a variety of climates, we subjected seedlings to temperature and soil moisture combinations ranging from those commonly found in burned landscapes to those projected with ongoing climate change. We used five species whose southwestern distributions range from warmer and drier woodlands to cooler and wetter subalpine forests (Supp. Fig. 1). *Pinus edulis* Engelm. is a widespread conifer in the southwestern US, considered drought-hardy and commonly found between 1370 and 2440 m (Burns & Honkala 1990). *Pinus ponderosa* Douglas ex C. Lawson has an extensive range in the western US, is fire-tolerant as an adult, and in the southwest and southern Rockies is found up to 3050 m (Burns & Honkala 1990). Due to a legacy of fire suppression and resultant forest densification in the southwest US, *Pseudotsuga menziesii* (Mirb.) Franco has colonized forests previously dominated by *P. ponderosa*, though is less fire tolerant, climate tolerant, and is generally found at a higher elevation range in the southwestern US (2440m to 3290m) (Burns & Honkala 1990). *Abies concolor* (Gord. & Glend.) Lindl. Ex Hildebr is found up to 3400 m in the central Rockies and is sensitive to heat and drought but generally tolerant of a range of soil conditions (Burns & Honkala 1990). *Picea engelmannii* Parry ex Engelm. is the least widely distributed species in the southwestern US of the five species we examined, occupies the coolest and wettest areas, and is found between 2400 m and 3700 m elevation (Burns & Honkala 1990). Because heat and drought effects vary by species, we used a 3x3 full factorial design, with three levels of temperature (34°C, 39°C, 44°C (based on growing season air temperature measurements in a high-severity burn area of the 2011 Las Conchas fire in northern New Mexico and the maximum temperature limits of the incubators) and three levels of soil moisture (5%, 10%, 15% of soil moisture at field capacity, measured gravimetrically). We calculated VPD from chamber relative humidity to use as a predictor variable because it is an integrated measure of temperature and moisture, but because chambers were unable to control rH levels, VPD varied over time. Data analysis: To analyze the species-specific relationships between temperature, soil moisture, VPD, and time-to-death, we first used two-way ANOVAs to compare temperature and moisture treatments in R (R core team 2021) using a Type II sum of squares implemented in the car package (Smith & Cribbie 2014, Fox & Weisberg 2019). We converted soil moisture weights (g) to volumetric by calculating the ratio of moisture to soil volume (cm3/cm3) so that we could use models to examine projected climate with volumetric soil moisture. We then used a Bayesian framework to construct species-specific discrete-time proportional-hazard models in R with the brms package, which fits models using 'Stan' (Tutz & Schmid 2016, Bürkner 2017). These models allow for an event to be modeled if it occurs between regular observation intervals as well as incorporate time-varying covariates as predictors. These models present the hazard of an event (here, death) occurring. Models took the form of Y_i ~ bernoulli(μ_i)logit (μ_i ) ~ a_ij+ β_1 x_1i+ β_2 x_2i + β_3 x_3i+s(x_4i)s(x_4i ) ~ β_4 x_4i+ z_k,for 1,…,k knots a_ij ~ Normal(0,4)β_1 ~ Normal(1,1) β_2~Normal(-1,1)β_3 ~Normal(1,1)β_4 ~ Uniform(-inf,inf)z_k ~ Normal(0,σ_τ)σ_τ ~ Students-t(3,0,2.5) with descriptions of coefficients and priors in table 1. Where logit (μ_i ) is the logit of death occurring, a_ij is the intercept of seedling j; β_1, β_2, and β_3 are the coefficients of temperature, initial soil moisture, and vapor pressure deficit, respectively; s(x_4i ) is the spline function for time since start, with coefficient β_4 and intercept z_k, for each 1:k knots. Errors have a Bernoulli distribution. Based on a literature search for likely effects of variables, we generated weakly informative, skeptical priors for each covariate (Table 1 and Supp. Table 1) and visually examined prior predictive distributions to ensure they generated realistic-looking data in the absence of observations. Models were fit with a Bernoulli family with a logit link and with a random intercept of plant ID to account for the repeated measures of each plant during the experiment. During model development, we determined that scaling and centering temperature, VPD, and initial soil moisture reduced divergent transition, and following scaling/centering these variables, we extracted the scale and center factors to apply to projected climates. We ran six chains with a 2000 iteration burn-in followed by 4000 iterations, and a thinning rate of 1, totaling 12000 post-warmup draws. We adjusted sampling algorithm settings (i.e., changing the adapt_delta value) where needed to achieve convergence of chains. To validate model performance, we conducted Gelman-Rubin diagnostic tests and checked that MCMC chain trace plots achieved stationarity and demonstrated mixing without autocorrelation between iterations. (Table 1, Supp. Fig 4). We then compared the posterior predictive distributions to the expected observations using the bayesplot package and Bayesian R2, which included both total variance explained and the marginal variance attributed to fixed effects, as well as calculated the root mean squared error (RMSE) from 10-fold cross validations. Following model assessment, we extracted 1000 posterior draws from the linear predictors and calculated the probability of surviving to time t given the hazard of an event: Eq. 5 S(t)= exp(-∑0^t (μ(t〗)) S(t)=exp(-∑0^t μ_i )) In which the survival to time t is the exponentiated negative sum from 0 to time t of the hazard Ui. Present-day and future climate scenarios To determine how present-day species ranges compare to the modelled survival probability, we extracted climate data from 1980 to 2019 for modeled species ranges from the National Individual Tree Species Atlas (Ellenwood et al. 2015, resolution = 30 m) and predicted survival probabilities for these locations. We extracted contemporary climate from GridMET (daily max temperature, precipitation total, and mean VPD, resolution = 4 km, Abatzoglou 2013) and soil moisture from Terraclimate (total column soil moisture [mm/m, converted to cm3/cm3], resolution = 4 km, Abatzoglou et al. 2018). We then calculated species presence using the modeled species ranges from the National Individual Tree Species Atlas as pixels with > 0 basal area and upscaled these data to match the resolution of GridMET. From GridMET, we first calculated the pixel-wise precipitation-free period. For each day in the period, we calculated the mean of the daily temperature and VPD maximums for all days up to that day. We used this approach rather than calculating the mean of the entire period because a single mean daily temperature/VPD maximum for an entire precipitation-free period could obscure shorter heat waves or droughts that occur during that period. We scaled temperature, VPD, and soil moisture with the scale factors used to process data for our models and took 100 draws from the linear predictor to calculate the mean survival for each day of each year using eq 5. We chose 100 draws for projections to avoid computation limitations stemming from size of the area/days/years we analyzed. For each pixel, year, and species, we calculated the minimum survival value. We then calculated the annual percent area for each species' range that exceeded our experimental thresholds (i.e., >34°C). We calculated the number of days that pixels in each bin experienced conditions likely to result in less than a 10% probability of survival. To determine whether area at risk or survival changed during the 1980-2019 period, we compared area at risk and mean survival between the 1980-1999 and 2000-2019 periods with T tests using a 0.05 significance level. To examine how the modelled survival probability may change within present-day species ranges during the 21st century, we used Multivariate Adaptive Constructed Analogs (MACA) downscaled CMIP5 projections forced with the RCP8.5 emissions scenario to calculate the pixel-wise precipitation-free periods and mean daily maximum temperature for each period (MACAv2-METDATA, resolution: 4 km, daily, Abatzoglou & Brown, 2012). In lieu of projected soil moisture, which at the time of writing was not available at a similar scale as MACA, we incorporated monthly climatologies of Terraclimate that were calculated using a 4°C temperature increase (monthly normal, total column soil moisture [mm/m, converted to cm3/cm3], resolution = 4 km, Abatzoglou et al. 2018). We calculated daily temperature maximums and periods with less than 1mm of precipitation from five downscaled CMIP5 models (CCSM4, bcc-csm1-1-m, ACCESS1-3, GFDL-ESM2G, and CESM1-CAM5; Supp. Table 2). For each day in the period, we calculated the mean of the daily temperature and VPD maximums for all days up to that day. As with the present-day thresholds, we calculated the number of aggregate days that pixels in each bin experienced conditions likely to result in less than 10% probability of survival. Using elevation and slope values extracted from a 4 km DEM provided with the gridMET data, we calculated the mean elevation and median aspect per year per species of pixels in which survival probability is less than 10%. Climate change and disturbance are altering forests and the rates and locations of tree regeneration. We examined seedling survival of five southwestern United States (US) conifer species found in warmer and drier woodlands (Pinus edulis, P. ponderosa) and cooler and wetter subalpine forests (Pseudotsuga menziesii, Abies concolor, and Picea engelmanii) under hot and dry conditions in incubators. We constructed models that explained 53% to 76% of the species-specific survival variability, then applied these to recent climate (1980-2019) and projected climate (1980-2099) for the southwestern US. We found that lower elevations within species' range would have low survival under projected climate and that range contraction would be greatest for species that currently occupy warm-dry conditions. These results demonstrate that empirically derived physiological limitations can be used to identify where species composition or vegetation type change are likely to occur in the southwest US. Application: R/Rstudio Package List: 'dplyr','ggpubr','hexbin','raster','sp','','terra','Survival','brms','car','cowplot','curl','ggplot2','grid','gtable','lubridate','readr','reshape2','sf' 'sjPlot','stringr','surrosurv','tidybayes','tidyr','wesanderson','broom','geomtextpath' Funding provided by: National Institute of Food and AgricultureCrossref Funder Registry ID: https://ror.org/05qx3fv49Award Number: 2017-67004-26486/project accession no. 1012226 Funding provided by: National Institute of Food and AgricultureCrossref Funder Registry ID: https://ror.org/05qx3fv49Award Number: 2021-67034-35106/project accession no. 1026366 Funding provided by: Joint Fire Science ProgramCrossref Funder Registry ID: https://ror.org/03ccbtk93Award Number: Project JFSP 16-1-05-8 Funding provided by: Joint Fire Science ProgramCrossref Funder Registry ID: https://ror.org/03ccbtk93Award Number: Project JFSP 20-1-01-9
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2022Publisher:Zenodo Jaworski, Coline; Geslin, Benoît; Zakardjian, Marie; Lecareux, Caroline; Caillault, Pauline; Nève, Gabriel; Meunier, Jean-Yves; Dupouyet, Sylvie; Sweeney, Aoife; Lewis, Owen; Dicks, Lynn; Fernandez, Catherine;1. Study site: CLIMED long-term drought experiment All field data were collected in February-June 2018. We used a subset of established plots that were part of the CLIMED (CLImate change effects on MEDiterranean biodiversity) long-term drought experiment situated at Massif de l'Étoile in Marseille, France (43° 22' N, 5° 25' E). This site has a typical woody shrub community dominated by three species: Quercus coccifera Linnaeus, 1753 (Fagaceae; anemophilous and a resource of very limited use to pollinators in the region; Ropars et al., 2020a), Salvia rosmarinus Spenn., 1835 (Lamiaceae; previously Rosmarinus officinalis; Drew et al., 2017), and Cistus albidus Linnaeus, 1753 (Cistaceae; Montès et al., 2008). Local cumulative precipitation between January and May 2018 (the flowering period surveyed) reached 291 mm, while the average precipitation between January and May for the period 2008-2018 was 205 mm (Marseille-Marignane meteorological station; www.infoclimat.fr). The site is equipped with 46 metallic control and 46 4 × 4 m rain-exclusion shelters established in October 2011, spaced by 1 to 30 m (Santonja et al., 2017). Plot locations were chosen randomly at the time of establishment of the long-term experiment, and were assigned at random to control or drought treatment (Montès et al., 2008). Gutters from rain-exclusion shelters in drought plots were designed to exclude up to 30 % and excluded on average (± SE) 12 ± 2% of precipitation between 2011 and 2018 at the centre of the plots; the intercepted water was carried away from the site with a pipe system. In control plots, the upside-down gutters intercepted a very small fraction of precipitation and rainfall reached the ground (Montès et al., 2008; Santonja et al., 2017). This water deficit attempts to mimic the mean predicted changes during the dry season in the Mediterranean area by the end of this century except in winter when rainfall is expected to increase (Giorgi & Lionello, 2008: averages for 2071-2100 relative to 1961-1990: December to February +0 to +10 %, March to May -10 to -20 %, June to August -20 to -30 %, September to November -0 to -10 %; Mariotti et al., 2015: averages for 2071-2098 relative to 1980-2005: December to February -0.1 to +0.2 mm/day, June to August -0.1 to -0.3 mm/day). The moderate but chronic experimental water deficit induced by the CLIMED experiment can alter plant physiology: carbon assimilation was reduced in C. albidus, and transpiration was reduced in C. albidus and S. rosmarinus but water use efficiency was not significantly changed in 2014 (Rodriguez-Ramirez, 2017). Between January and May 2018, permanent soil moisture probes (TDR100, Campbell Scientific Inc., Logan, Utah) measured soil moisture at 10, 20 and 40 cm in two control and two drought plots. For clarity we use the term drought to refer to the drought treatment in our study. We selected 10 control plots and 10 drought plots out of the 92 plots, based on: (i) where Thymus vulgaris Linnaeus, 1753 (Lamiaceae) was present (four plots for each treatment only) because it is an important resource for pollinators (Ropars et al., 2020a); and (ii) a high and similar percentage cover of C. albidus and S. rosmarinus. The chosen control and drought plots were homogeneously distributed throughout the site. We measured the percentage cover of each species in selected plots twice (February and June 2018). The percentage cover of S. rosmarinus, C. albidus and Q. coccifera and T. vulgaris was 21, 19, 15 and 0.5 % on average respectively in the 20 plots selected, and the community composition did not differ significantly between treatments throughout the long-term experiment. Despite such low diversity, this plant community is natural, and is representative of the site and of the type of dense, closed vegetation plant communities found in the region in areas where wildfires are ancient (> 10 years; Pimont et al., 2018). Thymus vulgaris, C. albidus and S. rosmarinus are all perennial, entomogamous shrub species; T. vulgaris is gynodioecious and obligate entomogamous (dichogamous; Arnan et al., 2014), while S. rosmarinus and C. albidus are self-compatible but with limited self-pollination (Hammer & Junghanns, 2020; Blasco & Mateu, 1995). A fourth shrub species, Ulex parviflorus Pourr., 1788, was also present but very rare (0.3 % percentage cover) with very few flowers during the study period, and other flowering species were even rarer. We did not observe any insect visit to these very rare species and hence excluded them from our study. 2. Floral traits involved in pollinator attraction 2.1. Floral scent sampling and GC-MS analysis We randomly selected up to 14 plant individuals per species in each treatment (control vs. drought) with a maximum of two (four for T. vulgaris) plants in the same plot. A few samples were lost during laboratory analysis, hence final sample sizes were 23 (control: 11; drought: 12) for S. rosmarinus, 22 (control: 11; drought: 11) for C. albidus, and 19 (control: 6 female, 6 hermaphroditic; drought: 5 female, 2 hermaphroditic) for T. vulgaris. Branches of the selected flowering plants bearing around 30-50, 2-3 or 100-400 flowers [1st-3rd quantiles] for S. rosmarinus, C. albidus and T. vulgaris respectively, were enclosed in a Nalophan bag (NA CAL, 30 cm × 30 cm, thickness 25 µm, volume ~ 2L; ETS Charles Frères, Saint-Étienne, France) connected to a pumping system maintaining a 1000 mL/min and a 200mL/min inlet and outlet air flows, respectively, provided by pumps (DC 12V, NMP850KNDC, KNF Neuberger SAS, France) powered by batteries (RS Pro 5Ah, 12V, RS Components SAS, France) and controlled by debit-metres (F65-SV1 Porter, Bronkhorst, France). Inlet air was first purified with activated charcoal (untreated, Mesh 4-8, Sigma Aldrich, USA) to limit the amount of volatiles from ambient air. Second, excess of humidity was removed using drierite (W.A. Hammond DrieriteTM Indicating Absorbents Mesh size 8, USA). Finally, ozone was filtered out through a fiberglass filter disk impregnated with sodium thiosulfate (Na2S2O3) following Pollmann et al. (2005) to limit oxidation of plant volatile organic compounds (VOCs). Air flow was first stabilized for 15 min (the time required to entirely renew the air inside the 2L-bags). VOCs were then adsorbed on a cartridge placed at the bag outlet for 10 min for S. rosmarinus and T. vulgaris, and 15 min for C. albidus. This protocol optimizes the signal-to-threshold ratio without exceeding the breakthrough volume of each VOC in the conditions of our experiment, which would distort the estimated relative composition of chemical profiles (Ormeño et al., 2007). The cartridges were made of glass tubes (Gerstel OD 6 mm for TDS2/3, RIC SAS, Lyon, France) filled with 0.120 g Carbotrap® adsorbent (matrix Carbotrap® B, 20-40 mesh, Sigma-Aldrich, France) then 0.050 g Tenax® Porous Polymer Adsorbent (matrix Tenax® GR, 20-35 mesh, Sigma-Aldrich) separated by glass wool and maintained in the tube by a fixing screen (Gerstel for TDS 2 ID 4.0 mm, RIC SAS, France) at the entrance side and glass wool at the exit side. To discriminate VOCs emitted by plants from possible environmental contamination, ambient air was sampled after every five plant samples using the same protocol. VOCs from four leaf-only plant samples per plant species were also measured to investigate which VOCs contribute most to floral scent versus leaf scent, enclosing branches of comparable size than the inflorescences of floral samples in collection bags. Sample cartridges were stored in a cooler immediately after collection, and transferred to a freezer at -20 °C as soon as possible. Prior to sampling, all cartridges had been cleaned in a Thermal Adsorbent Regenerator (RTA EcoLogicSense RG1301002, TERA Environment SARL, France) at 300 °C for 4 h. To reduce environmental variation from flowering phenology in scent emissions, each species was sampled over three days maximum around the flowering peak, during sunny weather and between 10:00 and 15:00. Throughout sampling, temperature and humidity were recorded inside and outside plant bags with data loggers (OM-EL-USB2-LCD, Omega Engineering Limited, UK). Plant parts inside bags were cut after sampling, dried in an oven at 50°C, and weighed after mass had stabilized (3-5 days, depending on plant species). Samples were analysed one to 20 days after sampling. VOCs were thermodesorbed (cool trap and flash heating -50 to 250 °C at 12°C/s for 10 min; TDS 3 Gerstel equipped with an autosampler TDS A Gerstel). They were analysed with a gas chromatograph coupled with a quadrupole low-resolution mass spectrometer in solvent vent and CIS splitless mode (GC 6890N; MS 5973N; Agilent) equipped with a HP-5MS non-polar capillary column (5 % phenyl-methylsiloxane; length 30 m; internal diameter 0.25 mm; film thickness 0.25 μm; Agilent 19091S-433). The temperature gradient applied to the column was 40°C for 5 min, then up to 245 °C at 3 °C/min and maintained for 2 min (total run time 75.33 min). The carrier gas was helium at 7.1 psi and 1 mL/min. Mass spectra were recorded in the scan EMV mode (EM voltage 1295 eV and scanned from m/z 40 to 400, with one scan every 0.004 min. Chromatograms were analysed with MZmine2 (version 2.18.1 developed for gas chromatography; Pluskal et al., 2010) in a 11-steps batch. Briefly, the baseline of each chromatogram was adjusted to 0, then values of m/z in each scan extracted and attributed to a peak. Peak heights (sum of m/z), areas and retention times, as well as mass spectrum at peak maximum were then exported from MZmine2 and imported into R (R Core Team, 2020; version 3.6.3) for peak identification. Retention indexes of each peak were calculated via a linear approximation built on a C5-C20 n-alkane series injected externally (Van Den Dool & Kratz, 1963). The retention indexes of 21 external standards were also verified with this method and checked against literature (Adams, 2007). The similarity between each peak's mass spectrum and reference mass spectra was calculated using the R function 'SpectrumSimilarity' (R library 'OrgMassSpecR v0.5-3'; Stein & Scott, 1994). The reference mass spectra were that of the 21 standards, and of two libraries converted in JPS format: the Adams 2007 library (Adams, 2007), and the NIST11 (NIST, 2011) library, was used when all similarity hits from the Adams 2007 library were lower than 0.7. Only similarity to reference molecules with a retention index within ± 15 of the peak's calculated retention index was calculated. Identification was processed sequentially starting with the most common VOCs and retention indexes were adjusted locally (+/- 10) at each step based on these new identifications. Peaks with highest similarity < 0.6 were discarded. Peaks with the same identity were then aligned for each species. True absence of a VOC was verified, and areas not integrated with the first MZmine2 round were manually added, similarity calculated as above, with peak discarded if similarity < 0.6 and using a smaller retention index tolerance of ± 5 (0.2 min on RT). Only VOCs previously reported as known plant volatiles in Pherobase (El-Sayed, 2019) or in a comprehensive review (Knudsen et al., 2006). Most of them had also previously been reported in oil extracts or as plant volatiles from the three study species (Katerinopoulos et al., 2005; Ormeño et al., 2007; Satyal et al., 2016). Following Campbell et al. (2019), we removed ambient air contaminants, by selecting only VOCs whose areas exceeded three times that of ambient air samples, by performing two-sample t-tests on , where A is the area of a peak (MZmine2 integration), Q and q are the inlet and outlet flows, respectively, and t is the sampling time, and using the type of sample (air vs. flower sample) as a factor, and with a false-discovery rate of 5 % to control for multiple comparisons. We also removed VOCs quantified in fewer than three samples of each species, because they added too much variance. We then removed the contribution of air samples to the retained VOCs in plant samples (each plant sample matched with the air sample taken closest in time) and calculated emission rates (in µg.h-1.gDM-1): (Sabillón & Cremades, 2001), with k the response coefficient calculated for each chemical family based on external calibration of pure standards (see below), and mDM the total dry mass of the inflorescence branch inside the sampling bag (flowers and leaves). Emission rates were then standardized by temperature (measured inside the bag during air flow stabilization and sampling; Ormeño et al., 2007; Sabillón & Cremades, 2001): where T is the temperature inside the sampling bag (in °K). 2.2. MZMine2 batch parameters 1. Raw data import Select all raw chromatogram files associated with one species 2. Filter scans Filter selected "Round resampling" "Sum duplicate intensities" = True "Remove zero intensity m/z peaks" = True 3. Crop filter Retention time: "min" = 4.5 min (remove solvents and water eluted in the first min) "max" = 64.0 min (after Docosane ~ 311 da; upper limit for molecule volatility at ambient temperature is ~ 300 da). m/z: "min" = 40 (lower limit of mass spectrometer acquisition) "max" = 315 (mass of Docosane molecular peak) 4. Baseline correction Correction method: "RollingBall baseline corrector" "wm (number of scans)" = 200 "ws (number of scans) = 5 5. Mass detection mass detector selected: "Centroid" "Noise level" = 2000 for 1st round, 1000 for 2nd round 6. Chromatogram builder "min time span (min)" = 0.05 "min height" = (same as 5.) "m/z tolerance" = 0.5 (absolute) / 0.001 (ppm) 7. Smoothing "Filter width" = 15 8. Deconvolution Algorithm selected : "Local minimum search" "Chromatographic threshold" = 0.50 "Search minimum in RT range (min)" = 0.04 "Minimum relative height" = 0.001 "Minimum absolute height" = 2000 "Min ratio of peak top/edge" = 1.2 "Peak duration range (min)": "min" = 0.05; "max" = 2.0 9. Peak merging "m/z tolerance" = 500 (absolute) / 5.0 (ppm) "RT tolerance window (number of scans)" = 15 "Use original raw data file" = False "Use detected peaks only" = False "Cumulative computing mode (TIC)" = True 10. Join Aligned (GC module) Our aim here was to align as few peaks as possible, so as to have one line per peak in the final matrix "m/z tolerance" = 0.5 (absolute) / 5.0 (ppm) "Weight for m/z" = 0.8 "Retention time tolerance" = 0.001 "Weight for RT" = 0.2 "Minimum score" = 0.7 "Use RT recalibration" = False "Use detected m/z only" = False "RT tolerance post-recalibration" = 0.4 "Export dendrogram as TXT" = False 11. Export CSV Export Peak RT, RTstart, RTend, Height, Area 2.3. External calibrations and calculation of response coefficients k Three different mixtures of pure standard chemicals (Sigma-Aldrich) were made using cyclohexane as solvent. Cartridges were loaded with 1 µL of the dilutions, except for Mixture 3 in which they were loaded with 1 µL of the solid mixture and 1 µL of the E-Caryophyllene solution. Three samples of each dilution were analysed for each mixture, in the same conditions as plant samples (see Main text). Two linear regressions were calculated: at low injected masses, and high injected masses (DIL6 and DIL5B for Mixtures 1 and 2, and DIL4 and DIL3 for Mixture 3). This is because for high injected masses the column is overloaded and this leads to a smaller than expected peak area. An intercept was used in linear regressions, because areas at low injected masses were indistinguishable no matter the dilution, showing that the quantification threshold (= intercept) was higher than lowest dilutions tested. In the conditions of the analysis it was impossible to properly quantify exact mass of VOCs below that threshold. 2.4. Nectar production Pollinators are declining globally, with climate change implicated as an important driver. Climate change can induce phenological shifts and reduce floral resources for pollinators, but little is known about its effects on floral attractiveness and how this might cascade to affect pollinators, pollination functions and plant fitness. We used an in situ long-term drought experiment to investigate multiple impacts of reduced precipitation in a natural Mediterranean shrubland, a habitat where climate change is predicted to increase the frequency and intensity of droughts. Focusing on three insect-pollinated plant species that provide abundant rewards and support a diversity of pollinators (Cistus albidus, Salvia rosmarinus and Thymus vulgaris), we investigated the effects of drought on a suite of floral traits including nectar production and floral scent. We also measured the impact of reduced rainfall on pollinator visits, fruit set and germination in S. rosmarinus and C. albidus. Drought altered floral emissions of all three plant species qualitatively, and reduced nectar production in T. vulgaris only. Apis mellifera and Bombus gr. terrestris visited more flowers in control plots than drought plots, while small wild bees visited more flowers in drought plots than control plots. Pollinator species richness did not differ significantly between treatments. Fruit set and seed set in S. rosmarinus and C. albidus did not differ significantly between control and drought plots, but seeds from drought plots had slower germination for S. rosmarinus and marginally lower germination success in C. albidus. Synthesis. Overall, we found limited but consistent impacts of a moderate experimental drought on floral phenotype, plant reproduction and pollinator visits. Increased aridity under climate change is predicted to be stronger than the level assessed in the present study. Drought impacts will likely be stronger and this could profoundly affect the structure and functioning of plant-pollinator networks in Mediterranean ecosystems. MZMine: Pluskal, T., Castillo, S., Villar-Briones, A., & Orešič, M. (2010). MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics, 11, 395. R Core Team. (2020). R: A Language and Environment for Statistical Computing. Vienna, Austria, https://www.R-project.org/. Funding provided by: AXA Research FundCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001961Award Number:
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integration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Zenodo Tozer, Douglas; Bracey, Annie M.; Fiorino, Giuseppe E.; Gehring, Thomas M.; Giese, Erin E. Gnass; Niemi, Gerald J.; Wheelock, Bridget A.; Ethier, Danielle M.;Study Area and Design We conducted our study in coastal wetlands throughout the entire Great Lakes basin (see Figure 1 in Tozer et al.). We selected coastal wetlands using a stratified, random sampling protocol (Uzarski et al. 2017, 2019). Further details regarding the study design are in Burton et al. (2008). The sampling universe was all coastal wetlands greater than 4 ha in size with a permanent or periodic surface-water connection to an adjacent Great Lake or their connecting river systems (Uzarski et al. 2017). We stratified our selection of wetlands for the study by 1) wetland hydrogeomorphic type (riverine, lacustrine, barrier protected; Albert et al. 2005), 2) region (northern or southern; Danz et al. 2005), and 3) lake (i.e., the watershed of 1 of the 5 Great Lakes). We sampled approximately 20% of all wetlands in each stratum each year, so that nearly all coastal wetlands within the Great Lakes basin meeting the selection criteria were sampled at least once every 5 years. In addition, we resampled 10% of wetlands between years according to a rotating panel design. Sampled wetlands were dominated by emergent, herbaceous vegetation and shallow water ( 250 m apart to avoid double counting individuals. We surveyed each point count location twice per year, at least 15 days apart, between 20 May and 10 July, which was the peak breeding period for marsh birds in the study area. Surveys took place either in the morning (30 min before sunrise to 4 h after sunrise) or the evening (4 h before sunset to 30 min after sunset), with 1 or both of the 2 surveys being in the morning each year (Tozer et al. 2017). We conducted surveys only when there was no precipitation and wind was < 20 km/h (Beaufort 3 or less). Each point count survey lasted 10 min, consisting of an initial 5-min passive listening period followed by a 5-min call broadcast period. The call broadcast period was intended to increase detections of secretive species by eliciting auditory responses and was composed of 30 sec of vocalizations followed by 30 sec of silence for each of the following: 1) Least Bittern, 2) Sora, 3) Virginia Rail, 4) a mixture of American Coot and Common Gallinule, and 5) Pied-billed Grebe, in that order. We trained observers so they thoroughly understood the field protocols and we required each observer to pass an aural and visual bird identification test in order to collect data. CWMP bird surveys were 15 min in duration from 2011 to 2018 but were reduced to 10 min from 2019 to 2021 (Tozer et al. 2017). To accommodate changes in survey protocol, we filtered the data to only include birds detected in the first 10 min of point counts from 2011 to 2018. For a detailed description of the sampling protocol visit greatlakeswetlands.org/Sampling-protocols. Response Variable The response variable for each species was the maximum number of individuals observed during either of the 2 surveys at each point count location in each year (Tozer 2020, Hohman et al. 2021). We viewed these counts as indices of true density, meaning our modeled values estimated relative abundance (e.g., Thogmartin et al. 2004). We assumed that variation in species-specific detection was uncorrelated with the predictors in our models, including year. This was sufficient in our case because our objective was to quantify relative differences and changes in abundance and not to quantify actual density. Our assumption was warranted because our data were collected using standardized methods designed to reduce heterogeneity in detection, e.g., observer training and testing, as well as restrictions on survey date, time of day, and wind (Conway 2011, Uzarski et al. 2017). It was further justified by other long-term, broad-scale studies of birds based on point counts conducted using similar standardized approaches, which found no differences in year or covariate effects based on counts that were adjusted or unadjusted for detection (Etterson et al. 2009, Zlonis et al. 2019). We note that long-term (1996–2013) marsh-breeding bird monitoring data collected throughout the developed, southern portion of the Great Lakes basin showed no systematic trends in detectability over time for 14 of 15 (93%) species (Tozer 2016). We also found no trends in detectability across years for all of the species in our dataset (see Supplemental Material Figure S1 in Tozer et al.), meaning that differences in detection did not bias our estimates of annual abundance indices or trends. Therefore, we did not adjust for detectability, which has been supported, for instance, by Hutto (2016) and Johnson (2008). The dataset consisted of 8,120 surveys completed at 1,962 point count locations in 792 coastal wetlands in 599 watersheds (defined by Forsyth et al. [2016]) over 11 years (2011–2021; see Figure 1, 2 and Supplemental Material Table S1 in Tozer et al.). There were 2.2 ± 1.6 (mean ± SD) point count locations per wetland (range: 1–8) and 1.3 ± 0.9 wetlands per watershed (range: 1–9). In total, we analyzed 18 species: 1) American Bittern, 2) American Coot, 3) Black Tern, 4) Common Gallinule, 5) Common Grackle, 6) Common Yellowthroat, 7) Forster's Tern, 8) Least Bittern, 9) Marsh Wren, 10) Mute Swan, 11) Pied-billed Grebe, 12) Red-winged Blackbird, 13) Sandhill Crane, 14) Sedge Wren, 15) Sora, 16) Swamp Sparrow, 17) Virginia Rail, and 18) Wilson's Snipe. We chose these species because they were of conservation interest in the Great Lakes region (e.g., Bianchini and Tozer 2023) and regularly nested or foraged in Great Lakes coastal wetlands. We attempted to model abundance and trends for Trumpeter Swan (Cygnus buccinator) and Yellow-headed Blackbird (Xanthocephalus xanthocephalus), but data were too sparse for the models to converge. We considered some regions of our study area to be out of range for some species. We accounted for this by dividing our study area into 10 regions and dropped any of them from species-specific analyses if naive occupancy was < 5% (Supplemental Material Table S2). By excluding out-of-range point count locations, we reduced the number of zero counts and focused our analysis on point count locations where zero counts were most likely to represent legitimate absences. As a result, the number of marsh-breeding bird species for which we quantified abundance and trends varied by lake due to uneven species occurrences across the study area: Superior (n = 10), Ontario (n = 12), Erie (n = 16), Huron (n = 16), and Michigan (n = 17). The CWMP bird survey data are available by request at greatlakeswetlands.org. Environmental Predictors We included the following environmental predictors in our models, which were known to influence abundance of marsh-breeding birds in the Great Lakes: 1) percent local wetland cover within 250 m of point count locations (as a proxy for wetland size; e.g., Studholme et al. 2023), 2) detrended, standardized Great Lakes water levels (to avoid correlation with year; e.g., Hohman et al. 2021, Denomme-Brown et al. 2023), 3) percent urban land cover in the surrounding watershed (e.g., Rahlin et al. 2022), and 4) percent agricultural land cover in the surrounding watershed (e.g., Saunders et al. 2019). The land cover predictors were static covariates (i.e., they were the same for all years), whereas detrended, standardized Great Lakes water level was a dynamic covariate (i.e., it varied annually). Land cover and water-level information at finer spatial and temporal scales would have been preferred, but such data were unavailable. Nonetheless, it is reasonable to assume that the land cover and water-level data we used provided useful approximations of the true values, particularly at the watershed scale (e.g., Michaud et al. 2022). Percent local wetland cover was based on the coastal wetland layer built by the Great Lakes Coastal Wetland Consortium (Burton et al. 2008, Uzarski et al. 2017), and percent urban and agricultural land cover were from Host et al. (2019) with watersheds defined by Forsyth et al. (2016); all of these data are available at glahf.org/data. We used ArcGIS 10.8.1 to overlay CWMP sample points onto the land cover layers and extracted the relevant predictors for each point (see Figure 3 in Tozer et al.). Yearly water levels were from the National Oceanic and Atmospheric Administration (noaa.gov). We used the mean yearly water level from May to July since these months overlapped with our survey period. We detrended water levels from year by using the residuals from a line of best fit for each lake, given that water levels generally increased in all lakes over the course of the study. Water levels were also standardized across lakes by dividing the annual value for each lake by the long-term mean (2011–2021) for each lake, given the reference value is the same for all lakes (International Great Lakes Datum 1985). Our detrended, standardized lake levels therefore represent water levels without being confounded with year (see Figure 4 in Tozer et al.). The environmental predictors were not correlated (-0.2 < r < 0.2; see Supplemental Material Figure S2 in Tozer et al.). Statistical Modeling We fit models in a Bayesian framework with Integrated Nested Laplace Approximation (INLA) using the R-INLA package (Rue and Martino 2009) for R statistical computing (version 4.2.0; R Core Team 2022). For each species, we modeled the expected (predicted mean) number of individuals per point count location in each Great Lake in each year, as well as the trend in these values across years in each lake, and then pooled the lake-specific trends to obtain Great Lakes-wide estimates. We included spatial structure in the models using an intrinsic conditional autoregressive (iCAR) structure (Besag et al. 1991), which allowed for information on relative abundance to be shared across lakes sharing basin boundaries. By accounting for this spatial structure in counts, the model allowed abundance and trend information to be shared among adjacent lakes (as described below), which improved estimates for lakes with limited sample sizes (Bled et al. 2013) and reduced the amount of spatial autocorrelation in model residuals (Zuur et al. 2017). We modeled counts уi,j,t using the maximum number of individuals observed at a point count location within a given wetland (j), lake (i), and year (t). The expected counts per lake within a given year µi,t for each of the 18 species took the form: log(µit) = αi + τiΤi,j,t + κj + ρj + уi,t + β1Wj + β2Lj + β3Uj + β4Ai where α = random lake intercept; T = year, indexed to 2021; τ = random lake slope effect; κ = random wetland effect; ρ = random wetland type effect; and у = random lake-year effect. Environmental predictors included: W = percent local wetland cover within 250 m; L = detrended, standardized water level; U = percent urban land cover in the surrounding watershed; and A = percent agricultural land cover in the surrounding watershed. The random lake intercept (αi) had an iCAR structure, where values of αi came from a normal distribution with a mean value related to the average of adjacent lakes. The random lake intercept also had a conditional variance proportional to the variance across adjacent lakes and inversely proportional to the number of adjacent lakes. We modeled the random lake slopes (τi) as spatially structured, lake-specific, random slope coefficients for the year effect, using the iCAR structure, with conditional means and variances as described above. We incorporated spatial structure into the random lake slopes (τi) to allow for information about year effects to be shared across neighboring lakes, and to allow year effects to vary among lakes. We transformed year (T) such that the maximum year was 0, and each preceding year was a negative integer. This scaling meant that the estimates of the random lake intercepts (αi) could be interpreted as the lake-specific expected counts (i.e., index of abundance) during the final year of the time series. We accounted for differences in relative abundance among wetlands (κ) and wetland types (ρ) with an independent and identically distributed (idd) random effect. To derive an annual index of abundance per lake, we included a random effect per lake-year (у) with an idd, and combined these effects with α and τ. Β1, β2, β3, and β4 were given normal priors with mean of zero and precision equal to 0.001. We scaled the spatial structure parameters α and τ such that the geometric mean of marginal variances was equal to one (Sørbye and Rue 2014, Riebler et al. 2016, Freni-Sterrantino et al. 2018), and priors for precision parameters were penalized complexity (PC) priors, with parameter values UPC = 1 and PC = 0.01 (Simpson et al. 2017). We also assigned precision for the random wetland, wetland type, and lake-year effects with a PC prior with parameter values previously stated. In general, the weakly informed priors used here tend to shrink the structured and unstructured random effects towards zero in the absence of a strong signal (Simpson et al. 2017). We validated distributional assumptions with simulation to ensure models could handle the large number of zero counts for some species. The abundance of most species was modeled using a zero-inflated Poisson (ZIP) distribution. Common Grackle and Red-winged Blackbird, which were more frequently detected compared to the other species, better fit a negative binomial distribution, and Common Yellowthroat better fit a Poisson distribution. We further validated models by visually inspecting 1) the fit versus raw counts; 2) residuals versus predictors; and 3) the estimate for Ф, the dispersion parameter (Zuur and Ieno 2016). Our visual inspections of fit versus raw counts suggested models were not overfit and were able to capture the variation of the raw counts. In general, residuals versus fit values behaved randomly around the zero line and residuals appeared to behave randomly with each predictor, suggesting the models fit well. The dispersion statistics were around 1 for all species, ranging lowest for Common Yellowthroat (0.72) and highest for Mute Swan (3.38), suggesting some residual under and over dispersion, respectively. Mute Swan had some high counts (outliers) which may have contributed to this. Following model analysis, we computed posterior estimates of trends (τ) and associated credible intervals for the full extent of the study area (i.e., by pooling lake-specific trends) using lake watershed size to calculate area-weighted averages (Link and Sauer 2002). References Albert, D. A., D. A. Wilcox, J. W. Ingram, and T. A. Thompson (2005). Hydrogeomorphic classification for Great Lakes coastal wetlands. Journal of Great Lakes Research 31:129–146. Besag, J., J. York, and A. Mollié (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43:1–20. Bianchini, K., and D. C. Tozer (2023). Using Breeding Bird Survey and eBird data to improve marsh bird monitoring abundance indices and trends. Avian Conservation and Ecology 18(1):4. Bled, F., J. Sauer, K. Pardieck, P. Doherty, and J. A. Royle (2013). Modeling trends from North American breeding bird survey data: a spatially explicit approach. PLoS ONE 8, e81867. Burton, T. M., J. C. Brazner, J. J. H. Ciborowski, G. P. Grabas, J. Hummer, J. Schneider, and D. G. Uzarski (Editors) (2008). Great Lakes Coastal Wetlands Monitoring Plan. Developed by the Great Lakes Coastal Wetlands Consortium, for the US EPA, Great Lakes National Program Office, Chicago, IL. Great Lakes Commission, Ann Arbor, Michigan, USA. Conway, C. J. (2011). Standardized North American marsh bird monitoring protocol. Waterbirds 34:319–346. Danz, N. P., R. R. Regal, G. J. Niemi, V. J. Brady, T. Hollenhorst, L. B. Johnson, G. E. Host, J. M. Hanowski, C. A. Johnston, T. Brown, J. Kingston, and J. R. Kelly (2005). Environmentally stratified sampling design for the development of Great Lakes environmental indicators. Environmental Monitoring and Assessment 102:41–65. Denomme-Brown, S. T., G. E. Fiorino, T. M. Gehring, G. J. Lawrence, D. C. Tozer, and G. P. Grabas (2023). Marsh birds as ecological performance indicators for Lake Ontario outflow regulation. Journal of Great Lakes Research 49:479–490. Etterson, M. A., G. J. Niemi, and N. P. Danz (2009). Estimating the effects of detection heterogeneity and overdispersion on trends estimated from avian point counts. Ecological Applications 19:2049–2066. Forsyth, D. K., C. M. Riseng, K. E. Wehrly, L. A. Mason, J. Gaiot, T. Hollenhorst, C. M. Johnston, C. Wyrzykowski, G. Annis, C. Castiglione, K. Todd, et al. (2016) The Great Lakes hydrography dataset: consistent, binational watersheds for the Laurentian Great Lakes basin. Journal of the American Water Resources Association 52:1068–1088. Freni-Sterrantino, A., M. Ventrucci, and H. Rue (2018). A note on intrinsic conditional autoregressive models for disconnected graphs. Spatial and Spatio-temporal Epidemiology 26:25–34. Hohman, T. R., R. W. Howe, D. C. Tozer, E. E. Gnass Giese, A. T. Wolf, G. J. Niemi, T. M. Gehring, G. P. Grabas, and C. J. Norment (2021). Influence of lake-levels on water extent, interspersion, and marsh birds in Great Lakes coastal wetlands. Journal of Great Lakes Research 47:534–545. Host, G. E., K. E. Kovalenko, T. N. Brown, J. J. H. Ciborowski, and L. B. Johnson (2019). Risk-based classification and interactive map of watersheds contributing anthropogenic stress to Laurentian Great Lakes coastal ecosystems. Journal of Great Lakes Research 45:609–618. Hutto, R. L. (2016). Should scientists be required to use a model-based solution to adjust for possible distance-based detectability bias? Ecological Applications 26:1287–1294. Johnson, D. H. (2008). In defense of indices: the case of bird surveys. Journal of Wildlife Management 72:857–868. Link, W. A., and J. R. Sauer (2002). A hierarchical analysis of population change with application to Cerulean Warblers. Ecology 83:2832–2840. Michaud, W., J. Telech, M. Green, B. Daneshfar, and M. Pawlowski (2022). Sub-indicator: land cover. In State of the Great Lakes 2022 Technical Report. Published by Environment and Climate Change Canada and U.S. Environmental Protection Agency. R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Rahlin, A. A., S. P. Saunders, and S. Beilke (2022). Spatial drivers of wetland bird occupancy within an urbanized matrix in the upper midwestern United States. Ecosphere 13, e4232. Riebler, A., S. H. Sørbye, D. Simpson, and H. Rue (2016). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research 25:1145–1165. Rue, H., S. Martino, and N. Chopin (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society Series B (Statistical Methodology) 71:319–392. Saunders, S. P., K. A. L. Hall, N. Hill, and N. L. Michel (2019). Multiscale effects of wetland availability and matrix composition on wetland breeding birds in Minnesota, USA. Condor 121:duz024. Simpson, D., H. Rue, A. Riebler, T. G. Martins, and S. H. Sørbye (2017). Penalising model component complexity: a principled, practical approach to constructing priors. Statistical Science 32:1–28. Sørbye, S. H., and H. Rue (2014). Scaling intrinsic Gaussian Markov random field priors in spatial modeling. Spatial Statistics 8:39–51. Studholme, K. R., G. E. Fiorino, G. P. Grabas, and D. C. Tozer (2023). Influence of surrounding land cover on marsh-breeding birds: implications for wetland restoration and conservation planning. Journal of Great Lakes Research 49:318–331. Thogmartin, W. E., J. R. Sauer JR, and M. G. Knutson (2004). A hierarchical spatial model of avian abundance with application to Cerulean Warblers. Ecological Applications 14:1766–1779. Tozer, D. C. (2016). Marsh bird occupancy dynamics, trends, and conservation in the southern Great Lakes basin: 1996 to 2013. Journal of Great Lakes Research 42:136–145. Tozer, D. C. (2020). Great Lakes Marsh Monitoring Program: 25 years of conserving birds and frogs. Birds Canada, Port Rowan, Ontario, Canada. Tozer, D. C., C. M. Falconer, A. M. Bracey, E. E. Gnass Giese, G. J. Niemi, R. W. Howe, T. M. Gerhing, and C. J. Norment (2017). Influence of call broadcast timing within point counts and survey duration on detection probability of marsh breeding birds. Avian Conservation and Ecology 12(2):8. [Tozer et al.] Tozer DC, Bracey AM, Fiorino GE, Gehring TM, Gnass Giese EE, Grabas GP, Howe RW, Lawrence GJ, Niemi GJ, Wheelock BA, Ethier DM. Increasing marsh bird abundance in coastal wetlands of the Great Lakes, 2011–2021, likely caused by increasing water levels. Ornithological Applications. Uzarski, D. G., D. A. Wilcox, V. J. Brady, M. J. Cooper, D. A. Albert, J. J. H. Ciborowski, N. P. Danz, A. Garwood, J. P. Gathman, T. M. Gehring, G. P. Grabas, et al. (2019). Leveraging a landscape-level monitoring and assessment program for developing resilient shorelines throughout the Laurentian Great Lakes. Wetlands 39:1357–1366. Uzarski, D. G., V. J. Brady, M. J. Cooper, D. A. Wilcox, D. A. Albert, R. P. Axler, P. Bostwick, T. N. Brown, J. J. H. Ciborowski, N. P. Danz, J. P. Gathman, et al. (2017). Standardized measures of coastal wetland condition: implementation at a Laurentian Great Lakes basin-wide scale. Wetlands 37:15–32. Zlonis, E. J., N. G. Walton, B. R. Sturtevant, P. T. Wolter, and G. J. Niemi (2019). Burn severity and heterogeneity mediate avian response to wildfire in a hemiboreal forest. Forest Ecology and Management 439:70–80. Zuur, A. F., and E. I. Ieno (2016). A protocol for conducting and presenting results of regression-type analyses. Methods in Ecology and Evolution 7:636–645. Zuur, A. F., E. I. Ieno, and A. A. Saveliev (2017). Beginner's guide to spatial, temporal and spatial-temporal ecological data analysis with R-INLA. Volume I: Using GLM and GLMM. Highland Statistics, Newburgh, United Kingdom. Wetlands of the Laurentian Great Lakes of North America, i.e., lakes Superior, Michigan, Huron, Erie, and Ontario, provide critical habitat for marsh birds. We used 11 years (2011–2021) of data collected by the Great Lakes Coastal Wetland Monitoring Program at 1,962 point count locations in 792 wetlands to quantify the first-ever annual abundance indices and trends of 18 marsh-breeding bird species in coastal wetlands throughout the entire Great Lakes. Nine species (50%) increased by 8–37% per year across all of the Great Lakes combined, whereas none decreased. Twelve species (67%) increased by 5–50% per year in at least 1 of the 5 Great Lakes, whereas only 3 species (17%) decreased by 2–10% per year in at least 1 of the lakes. There were more positive trends among lakes and species (n = 34, 48%) than negative trends (n = 5, 7%). These large increases are welcomed because most of the species are of conservation concern in the Great Lakes. Trends were likely caused by long-term, cyclical fluctuations in Great Lakes water levels. Lake levels increased over most of the study, which inundated vegetation and increased open water-vegetation interspersion and open water extent, all of which are known to positively influence abundance of most of the increasing species and negatively influence abundance of all of the decreasing species. Coastal wetlands may be more important for marsh birds than once thought if they provide high-lake-level-induced population pulses for species of conservation concern. Coastal wetland protection and restoration are of utmost importance to safeguard this process. Future climate projections show increases in lake levels over the coming decades, which will cause "coastal squeeze" of many wetlands if they are unable to migrate landward fast enough to keep pace. If this happens, less habitat will be available to support periodic pulses in marsh bird abundance, which appear to be important for regional population dynamics. Actions that allow landward migration of coastal wetlands during increasing water levels by removing or preventing barriers to movement, such as shoreline hardening, will be useful for maintaining marsh bird breeding habitat in the Great Lakes. Funding provided by: Long Point Waterfowl and Wetlands Research Program of Birds Canada*Crossref Funder Registry ID: Award Number: Funding provided by: Environment and Climate Change CanadaCrossref Funder Registry ID: https://ror.org/026ny0e17Award Number: 3000747437 Funding provided by: Wildlife Habitat Canada (Canada)Crossref Funder Registry ID: https://ror.org/0156t7498Award Number: 23-300 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency**Crossref Funder Registry ID: Award Number: GL-00E00612-0 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency*Crossref Funder Registry ID: Award Number: 00E01567 Funding provided by: Great Lakes Restoration Initiative as provided by the Great Lakes National Program Office of the United States Environmental Protection Agency*Crossref Funder Registry ID: Award Number: 00E02956
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2020Publisher:Zenodo Funded by:EC | FIThydroEC| FIThydrovan Treeck, Ruben; Radinger, Johannes; Noble, Richard; Geiger, Franz; Wolter, Christian;Hydroelectricity is critical for decarbonizing global energy production, but hydropower plants affect rivers, disrupt their continuity, and threaten migrating fishes. This puts hydroelectricity production in conflict with efforts to protect threatened species and re-connect fragmented ecosystems. Assessing the impact of hydropower on fishes will support informed decision-making during planning, commissioning, and operation of hydropower facilities. Few methods estimate mortalities of single species passing through hydropower turbines, but no commonly agreed tool assesses hazards of hydropower plants for fish populations. The European Fish Hazard Index bridges this gap. This assessment tool for screening ecological risk considers constellation specific effects of plant design and operation, the sensitivity and mortality of fish species and overarching conservation and environmental development targets for the river. Further, it facilitates impact mitigation of new and existing hydropower plants of various types across Europe. The tool does not yet support VBAs. In order to use it and produce reliable results, all input fields have to be reset manually before making a new assessment. The input window contains examplary dummy data.
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Code Ocean Authors: Ziwei Dai; Zhiyong Zhang; Mingzhou Chen ;This paper proposes a home health care location-routing problem with a mixed fleet of electric and conventional vehicles that considers battery swapping stations. It aims to simultaneously determine the locations of HHC centers, the scheduling of caregivers with respect to skill requirements, and a routing plan for a mixed fleet under specific time windows, load capacities, synchronized visits, and driving ranges. To address this problem, the paper proposes a novel competitive simulated annealing (CSA) algorithm in which a series of problem-specific effective local search operators expand the solution space of the CSA algorithm, with a competitive mechanism to adaptively adjust these operators to accelerate convergence speed and improve exploration ability. To enhance the exploitation ability, it employs a modified simulated annealing algorithm with a heating strategy and variable neighborhood descent. The code of competitive simulated annealing algorithm is provided here in order to address home health care location-routing problem with a mixed fleet and battery swapping stations
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Zenodo Authors: Hansen, Carly; Matson, Paul;These scripts, R project, and accompanying data files document the exploration of reservoir archetypes from the perspective of morphology and climate. We used Archetypal Analysis to identify extremes representing the diversity and makeup of US hydropower reservoirs, limited to those included in the LAGOS-US dataset. This analysis supports evaluation of reservoir diversity and describes the intersection between reservoir similarity and changing climate. High variability in local climate conditions and projected changes in climate conditions may complicate assumptions about similarity in biogeochemical processes (such as greenhouse gas emissions) even among reservoirs that are otherwise similar in shape and watershed setting. Input datasets are derived from: Hansen, C.H. and Matson, P.G. 2023. Hydropower Infrastructure - LAkes, Reservoirs, and RIvers (HILARRI), V2. HydroSource. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. DOI: https/doi.org/10.21951/HILARRI/1960141 Smith, N.J., K.E. Webster, L.K. Rodriguez, K.S. Cheruvelil, and P.A. Soranno. 2021. LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S. ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/e5c2fb8d77467d3f03de4667ac2173ca (Accessed 2023-04-13). Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321. doi:10.1002/2013EO370002 Prairie, Yves T., Mercier-Blais, Sara, Harrison, John A., Soued, Cynthia, Del Giorgio, Paul A., Harby, Atle, Alm, Jukka, Chanudret, Vincent, & Nahas, Roy. (2021). G-res tool modelling database [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4711132 Deemer, Bridget R. et al. (2020), Data from: Greenhouse gas emissions from reservoir water surfaces: a new global synthesis, Dryad, Dataset, https://doi.org/10.5061/dryad.d2kv0
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Zenodo Authors: Eisenschmid, Karolin; Jabbusch, Sarina; Koch, Marcus;As global warming progresses, plants may be forced to adapt to drastically changing environmental conditions. Arctic-alpine plants have been among the first to experience the effects of climate change. As a result, cold acclimation and freezing tolerance may become increasingly crucial for the survival as winter warming events and earlier snowmelt will cause increased exposure to occasional frost. The tribe Cochlearieae in the mustard family (Brassicaceae) offers an instructive system for studying cold adaptation in evolutionary terms, as the two sister genera Ionopsidium and Cochlearia are distributed among different ecological habitats throughout the European continent and the far north into circumarctic regions. By applying an electrolyte leakage assay to leaves obtained from plants cultivated under controlled temperature regimes in growth chambers, the freezing tolerance of different Ionopsidium and Cochlearia species was assessed measuring lethal freezing temperature values (LT50 and LT100), thereby allowing for a comparison across different species and accessions in their responses to cold. We hypothesized that, owing to varying selection pressures, geographically distant species would differ in freezing tolerance. Despite Ionopsidium occurring under warm and dry Mediterranean conditions and Cochlearia species distributed often at cold habitats, all accessions exhibited similar cold responses. The results may indicate that physiological adaptations of primary metabolic pathways to different stressors, such as salinity and drought, may confer an additional tolerance to cold; this is because all these stressors induce osmotic challenges. Data can be accessed using microsoft word office and excel.Funding provided by: Deutsche ForschungsgemeinschaftCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001659Award Number: KO2302/23-2 Electrolyte leakage analysis of single leafs.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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visibility 16visibility views 16 download downloads 2 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2022Publisher:Code Ocean Authors: Brown, Paul D.; Göl, Murat;A demonstration agricultural microgrid containing solar photovoltaic (PV), battery storage system (BSS) and multiple water pumps and reservoirs is presented. A mathematical model of the cost of operating the demonstration microgrid is developed. The mathematical model includes hybrid inverter source switching and BSS charging modes in addition to power balance and inter-period energy and water-level coupling. Electricity pricing and irrigation water use efficiency are allowed to vary by time of day. The mathematical model is formulated as a mixed-integer linear program (MILP), implemented in Python using Pyomo, and optimized using the open-source SCIP solver to plan pumping and water usage. Estimated data for a demonstration system at a farm in Turkey is used to demonstrate the proposed model. Results of the optimization of the demonstration system show intuitive results that are superior to a rule-based initialization. The model may serve as the basis for model predictive control (MPC) or stochastic model predictive control (SMPC).
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2022Publisher:Zenodo Kastl, Brian; Obedzinski, Mariska; Carlson, Stephanie; Boucher, William; Grantham, Ted;Runoff and water temperature data We estimated mean annual precipitation, averaged across each drainage area, using Google Climate Engine, March 2011 - February 2021. Where multiple temperature loggers were present in a study stream, we selected a single location based on the completeness of data in the study season and proximity to the PIT antenna. Hourly temperature measurements were converted into mean daily values. Analysis For data analysis and modeling, we excluded streams that had less than 3 years of biological data, leaving 47 stream-years. We conducted all analyses in R (version 4.0.4, R Core Team, 2018). We tested outmigration timing data for normal distribution among streams, years, and stream-years, using the shapiro.test function of the broom package. The Shapiro-Wilk test showed that all distributions were unlikely to be normally distributed (i.e. among years, p = 5.5 × 10-9 – 7.6 × 10-39 and W = 0.88 – 0.98). However, the Shapiro-Wilk test can provide small p-values for large samples and consequently provide a false negative, regarding normal distribution (among years, sample size range was 485 – 3453). Therefore, we could not rule out the possibility of assumptions being met to perform ANOVA (Analysis of Variance) tests. We did so, using the aov function of the AICcmodavg package: i) one-way, by stream, ii) a one-way, by year, iii) a two-way, by stream and year, and iv) a two-way with stream-year interaction. To isolate the effects of stream and year on variance, we performed the ANOVA tests on the maximum subset of data for which each stream had the same years of outmigration (four streams, each with the same six years of data, totaling 24 stream-years). The aictab function of the AICcmodavg package demonstrated that the two-way model with stream-year interaction was the highest performing (lowest AICc value), followed by: the two-way model, one-way by year model, and one-way by stream model. In both ANOVA tests, the year, stream, and year-stream interaction terms each had "Pr(>F)" values < 2 × 10-16. The "2-way ANOVA with interaction" (year F-value 646.58, stream F-value 349.85, year-stream interaction F-value 29.31, residuals 4.11 × 10-16) had higher F values and lower residuals than the 2-way ANOVA (year F-value 629.3, stream F-value 340.5, residuals 4.22 × 10-16). We used the TukeyHSD function of the AICcmodavg package to conduct pairwise tests for significant differences in outmigration timing distributions. Among streams, five of six pairwise differences were highly significant (p < 0.0001). Among years, all 15 pairwise comparisons were highly significant (p < 0.001). Among stream-years, 216 of 277 pair-wise comparisons were significant (p < 0.05). We checked for homoscedasticity in the interaction model, using the leveneTest function of the car library, and we found evidence that the variance across groups is significantly different. Consequently, we cannot assume homogeneity of variances in the different groups, which is typically a required assumption for conducting ANOVA tests. Since the normal distribution assumption of the one-way ANOVA was not met, we applied the Kruskal-Wallis test, as a non-parametric alternative to test for variance among streams and years, using the package rstatix. As with the ANOVA tests, we performed Kruskal-Wallis tests on the maximum subset of data for which each stream had the same years of outmigration (24 stream-years), using the functions kruskal_test, kruskal_effsize, dunn_test, and wilcox_test. Among streams, we found significant variance (p = 2.16 × 10-143), with a "small" effect size (eta-squared measure = 0.04) (Tomczak and Tomczak 2014), and 5 of 6 pairwise differences were highly significant (Dunn's test & Wilcoxon's test: p < 0.0001). Among years, we found significant variance (p = 0), with a "large" effect size (eta-squared measure = 0.17) (Tomczak and Tomczak 2014), and 13 of 15 pairwise differences were highly significant (Dunn's test & Wilcoxon's test: p < 0.0001). Modeling the effects of streamflow and water temperature on outmigration timing Modeling was limited to the 42 stream-years for which water temperature and outmigration timing data were collected. For the outmigration start date model, the runoff date range was March-April and the degree-days date range was March-April. For the outmigration end date and duration models, the runoff date range was March-June and the degree-days date range was March-April. Coefficient units are "days per daily runoff (mm)" and "days per 100 degree-days". In identifying top model(s), we did not consider degree-days to influence outmigration duration because: i) the AIC value of the runoff-only model was 1.99 less than the additive model, ii) the degree-days in the additive model had a p-value > 0.05, and iii) Mar-Jun runoff had similar coefficient effect sizes in the additive model and run-off only model (Appendix S1: Table S3). We calculated conditional coefficients (including stream, as a random effect) and marginal coefficients (excluding stream, as a random effect) of determination (R2) (Nakagawa and Schielzeth 2013), using the r.squaredGLMM function of the MuMIn package (Barton` 2020). We also reported the model coefficients and 95% confidence intervals, as measures of effect size, and generated partial dependence plots for using the plot_model function of the sjPlot package (Lüdecke 2021). Literature cited Barton`, K. (2020). MuMIn: Multi-Model Inference. R package version 1.43.17. Lüdecke, D. (2021). sjPlot: Data Visualization for Statistics in Social Science. R package version 2.8.9. Nakagawa, S., and H. Schielzeth. 2013. A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4:133–142. Tomczak, M., and E. Tomczak. 2014. The need to report effect size estimates revisited. An overview of some recommended measures of effect size 1:7. Prolonged migration windows buffer migratory animal populations against uncertainty in resource availability. Understanding how intensifying droughts from climate change influence the migration window is critical for biodiversity conservation in a warming world. We explored how drought affects the seaward migration of endangered coho salmon (Oncorhynchus kisutch) near the southern extent of their range in California, USA. We tracked stream departures of juvenile coho, measuring streamflow and temperature in 7 streams over 13 years, spanning an historic drought with extreme dry and warm conditions. Linear mixed effects models indicate that, over the range of observations, a decrease in seasonal streamflow (from 4.5 to 0.5 mm/day seasonal runoff) contracted the migration window by 31% (from 11 to 7 weeks). An increase from 10.2 to 12.8 ℃ in mean seasonal water temperature hastened the migration window by three weeks. Pacific salmon have evolved to synchronize ocean arrival with productive ocean upwelling. However, earlier and shorter migration windows during drought could lead to mismatches, decreasing fitness and population stability. Our study demonstrates that drought-induced low flows and warming threaten coho salmon in California and suggests that environmental flow protections will be needed to support the seaward migration of Pacific salmon in a changing climate. Please see DataS1/data/README_Metadata.pdf.Funding provided by: California Department of Fish and WildlifeCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006238Award Number: Funding provided by: California Sea Grant, University of California, San DiegoCrossref Funder Registry ID: http://dx.doi.org/10.13039/100005522Award Number: Graduate Research Fellowship R/AQ-153FFunding provided by: National Geographic SocietyCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006363Award Number: EC-53369R-18Funding provided by: National Oceanic and Atmospheric AdministrationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000192Award Number: Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: Graduate Research Fellowship DGE 1752814Funding provided by: Sonoma Fish and Wildlife Commission*Crossref Funder Registry ID: Award Number: Funding provided by: U.S. Army Corps of EngineersCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006752Award Number:
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visibility 33visibility views 33 download downloads 2 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2022Publisher:CyVerse Data Commons Authors: Triplett, Amanda;doi: 10.25739/kmk7-b046
"The input data and scripts necessary to run the ParFlow hydrologic model of the middle Heihe River Basin and produce all figures in the paper submission of "climate warming-driven changes in the cryosphere and their impact on groundwater-surface water interactions in the heihe river basin""
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2023Publisher:Zenodo Authors: Crockett, Joseph;We obtained 135 one growth year old seedlings of each species (675 seedlings total) from the New Mexico State University John T. Harrington Forestry Research Center in Mora, NM between April 2020 and September 2021. Seedlings were grown from locally sourced seeds from mature trees in northern New Mexico to ensure that they represented local adaptation to conditions. Seedlings were grown in a greenhouse in 10 cm containers at staggered intervals to ensure that they were of similar age and size when they were placed in the incubators. We transplanted seedlings into 22 cm deep pots (volume: 590 cm3) with well-drained soil (2 parts sphagnum moss, 1.5 parts vermiculate, 1.5 parts sand) brought to field capacity following transplanting. We allowed soil moisture to draw down to the treatment level, measuring soil moisture gravimetrically. Once soil moisture matched the treatment condition, we randomly assigned seedlings to one of two Percival Model E-36L1 incubators. We intended to use 15 seedlings per species per temperature/moisture combination, but several seedlings died during moisture drawdown resulting in several treatments using fewer than 15. Incubator rack positions were adjusted to ensure that seedlings in each incubator received equivalent photosynthetically active radiation (~260 mol). We programmed temperature treatments to follow a diurnal cycle with lower temperatures at night (15C) and progressive steps to treatment temperatures during the day. We set photoperiods at 15/9 hours to reflect growing season conditions. Incubators controlled temperature and light. We placed iButtons (Model number DS1923; Temperature accuracy +/- 0.5C; humidity resolution 0.6%; https://www.maximintegrated.com/en/products/ibutton-one-wire/data-loggers/DS1923.html) within each incubator to record the actual temperature and humidity at hourly intervals. Temperatures matched the programmed values and humidity was highest at the start of each stage of the experiment and decreased as moisture was lost to evapotranspiration. We calculated Vapor Pressure Deficit (VPD, kPa) at each time step as the difference between saturated and effective water pressure of the air. We assessed seedling health weekly with visual assessments of needle coloration and by measuring leaf fluorescence with a MultispeQ v2.0 fluorometer (Guadagno et al. 2017). The efficiency of light adapted photosynthetic reaction centers (measured as a ratio of Fv* to Fm*) corresponds well to destructive measures of cell conductance yet provides a non-destructive, rapid assessment of plant death with greater accuracy than visual assessment of foliage color. We determined plant death as either 95% brown/grey needle coloration or below 0.1 Fv*/Fm*. At plant death, we recorded time between treatment start date and seedling death to express results in days until death. Methods for processing the data: To test the physiological tolerances of seedlings from a variety of climates, we subjected seedlings to temperature and soil moisture combinations ranging from those commonly found in burned landscapes to those projected with ongoing climate change. We used five species whose southwestern distributions range from warmer and drier woodlands to cooler and wetter subalpine forests (Supp. Fig. 1). *Pinus edulis* Engelm. is a widespread conifer in the southwestern US, considered drought-hardy and commonly found between 1370 and 2440 m (Burns & Honkala 1990). *Pinus ponderosa* Douglas ex C. Lawson has an extensive range in the western US, is fire-tolerant as an adult, and in the southwest and southern Rockies is found up to 3050 m (Burns & Honkala 1990). Due to a legacy of fire suppression and resultant forest densification in the southwest US, *Pseudotsuga menziesii* (Mirb.) Franco has colonized forests previously dominated by *P. ponderosa*, though is less fire tolerant, climate tolerant, and is generally found at a higher elevation range in the southwestern US (2440m to 3290m) (Burns & Honkala 1990). *Abies concolor* (Gord. & Glend.) Lindl. Ex Hildebr is found up to 3400 m in the central Rockies and is sensitive to heat and drought but generally tolerant of a range of soil conditions (Burns & Honkala 1990). *Picea engelmannii* Parry ex Engelm. is the least widely distributed species in the southwestern US of the five species we examined, occupies the coolest and wettest areas, and is found between 2400 m and 3700 m elevation (Burns & Honkala 1990). Because heat and drought effects vary by species, we used a 3x3 full factorial design, with three levels of temperature (34°C, 39°C, 44°C (based on growing season air temperature measurements in a high-severity burn area of the 2011 Las Conchas fire in northern New Mexico and the maximum temperature limits of the incubators) and three levels of soil moisture (5%, 10%, 15% of soil moisture at field capacity, measured gravimetrically). We calculated VPD from chamber relative humidity to use as a predictor variable because it is an integrated measure of temperature and moisture, but because chambers were unable to control rH levels, VPD varied over time. Data analysis: To analyze the species-specific relationships between temperature, soil moisture, VPD, and time-to-death, we first used two-way ANOVAs to compare temperature and moisture treatments in R (R core team 2021) using a Type II sum of squares implemented in the car package (Smith & Cribbie 2014, Fox & Weisberg 2019). We converted soil moisture weights (g) to volumetric by calculating the ratio of moisture to soil volume (cm3/cm3) so that we could use models to examine projected climate with volumetric soil moisture. We then used a Bayesian framework to construct species-specific discrete-time proportional-hazard models in R with the brms package, which fits models using 'Stan' (Tutz & Schmid 2016, Bürkner 2017). These models allow for an event to be modeled if it occurs between regular observation intervals as well as incorporate time-varying covariates as predictors. These models present the hazard of an event (here, death) occurring. Models took the form of Y_i ~ bernoulli(μ_i)logit (μ_i ) ~ a_ij+ β_1 x_1i+ β_2 x_2i + β_3 x_3i+s(x_4i)s(x_4i ) ~ β_4 x_4i+ z_k,for 1,…,k knots a_ij ~ Normal(0,4)β_1 ~ Normal(1,1) β_2~Normal(-1,1)β_3 ~Normal(1,1)β_4 ~ Uniform(-inf,inf)z_k ~ Normal(0,σ_τ)σ_τ ~ Students-t(3,0,2.5) with descriptions of coefficients and priors in table 1. Where logit (μ_i ) is the logit of death occurring, a_ij is the intercept of seedling j; β_1, β_2, and β_3 are the coefficients of temperature, initial soil moisture, and vapor pressure deficit, respectively; s(x_4i ) is the spline function for time since start, with coefficient β_4 and intercept z_k, for each 1:k knots. Errors have a Bernoulli distribution. Based on a literature search for likely effects of variables, we generated weakly informative, skeptical priors for each covariate (Table 1 and Supp. Table 1) and visually examined prior predictive distributions to ensure they generated realistic-looking data in the absence of observations. Models were fit with a Bernoulli family with a logit link and with a random intercept of plant ID to account for the repeated measures of each plant during the experiment. During model development, we determined that scaling and centering temperature, VPD, and initial soil moisture reduced divergent transition, and following scaling/centering these variables, we extracted the scale and center factors to apply to projected climates. We ran six chains with a 2000 iteration burn-in followed by 4000 iterations, and a thinning rate of 1, totaling 12000 post-warmup draws. We adjusted sampling algorithm settings (i.e., changing the adapt_delta value) where needed to achieve convergence of chains. To validate model performance, we conducted Gelman-Rubin diagnostic tests and checked that MCMC chain trace plots achieved stationarity and demonstrated mixing without autocorrelation between iterations. (Table 1, Supp. Fig 4). We then compared the posterior predictive distributions to the expected observations using the bayesplot package and Bayesian R2, which included both total variance explained and the marginal variance attributed to fixed effects, as well as calculated the root mean squared error (RMSE) from 10-fold cross validations. Following model assessment, we extracted 1000 posterior draws from the linear predictors and calculated the probability of surviving to time t given the hazard of an event: Eq. 5 S(t)= exp(-∑0^t (μ(t〗)) S(t)=exp(-∑0^t μ_i )) In which the survival to time t is the exponentiated negative sum from 0 to time t of the hazard Ui. Present-day and future climate scenarios To determine how present-day species ranges compare to the modelled survival probability, we extracted climate data from 1980 to 2019 for modeled species ranges from the National Individual Tree Species Atlas (Ellenwood et al. 2015, resolution = 30 m) and predicted survival probabilities for these locations. We extracted contemporary climate from GridMET (daily max temperature, precipitation total, and mean VPD, resolution = 4 km, Abatzoglou 2013) and soil moisture from Terraclimate (total column soil moisture [mm/m, converted to cm3/cm3], resolution = 4 km, Abatzoglou et al. 2018). We then calculated species presence using the modeled species ranges from the National Individual Tree Species Atlas as pixels with > 0 basal area and upscaled these data to match the resolution of GridMET. From GridMET, we first calculated the pixel-wise precipitation-free period. For each day in the period, we calculated the mean of the daily temperature and VPD maximums for all days up to that day. We used this approach rather than calculating the mean of the entire period because a single mean daily temperature/VPD maximum for an entire precipitation-free period could obscure shorter heat waves or droughts that occur during that period. We scaled temperature, VPD, and soil moisture with the scale factors used to process data for our models and took 100 draws from the linear predictor to calculate the mean survival for each day of each year using eq 5. We chose 100 draws for projections to avoid computation limitations stemming from size of the area/days/years we analyzed. For each pixel, year, and species, we calculated the minimum survival value. We then calculated the annual percent area for each species' range that exceeded our experimental thresholds (i.e., >34°C). We calculated the number of days that pixels in each bin experienced conditions likely to result in less than a 10% probability of survival. To determine whether area at risk or survival changed during the 1980-2019 period, we compared area at risk and mean survival between the 1980-1999 and 2000-2019 periods with T tests using a 0.05 significance level. To examine how the modelled survival probability may change within present-day species ranges during the 21st century, we used Multivariate Adaptive Constructed Analogs (MACA) downscaled CMIP5 projections forced with the RCP8.5 emissions scenario to calculate the pixel-wise precipitation-free periods and mean daily maximum temperature for each period (MACAv2-METDATA, resolution: 4 km, daily, Abatzoglou & Brown, 2012). In lieu of projected soil moisture, which at the time of writing was not available at a similar scale as MACA, we incorporated monthly climatologies of Terraclimate that were calculated using a 4°C temperature increase (monthly normal, total column soil moisture [mm/m, converted to cm3/cm3], resolution = 4 km, Abatzoglou et al. 2018). We calculated daily temperature maximums and periods with less than 1mm of precipitation from five downscaled CMIP5 models (CCSM4, bcc-csm1-1-m, ACCESS1-3, GFDL-ESM2G, and CESM1-CAM5; Supp. Table 2). For each day in the period, we calculated the mean of the daily temperature and VPD maximums for all days up to that day. As with the present-day thresholds, we calculated the number of aggregate days that pixels in each bin experienced conditions likely to result in less than 10% probability of survival. Using elevation and slope values extracted from a 4 km DEM provided with the gridMET data, we calculated the mean elevation and median aspect per year per species of pixels in which survival probability is less than 10%. Climate change and disturbance are altering forests and the rates and locations of tree regeneration. We examined seedling survival of five southwestern United States (US) conifer species found in warmer and drier woodlands (Pinus edulis, P. ponderosa) and cooler and wetter subalpine forests (Pseudotsuga menziesii, Abies concolor, and Picea engelmanii) under hot and dry conditions in incubators. We constructed models that explained 53% to 76% of the species-specific survival variability, then applied these to recent climate (1980-2019) and projected climate (1980-2099) for the southwestern US. We found that lower elevations within species' range would have low survival under projected climate and that range contraction would be greatest for species that currently occupy warm-dry conditions. These results demonstrate that empirically derived physiological limitations can be used to identify where species composition or vegetation type change are likely to occur in the southwest US. Application: R/Rstudio Package List: 'dplyr','ggpubr','hexbin','raster','sp','','terra','Survival','brms','car','cowplot','curl','ggplot2','grid','gtable','lubridate','readr','reshape2','sf' 'sjPlot','stringr','surrosurv','tidybayes','tidyr','wesanderson','broom','geomtextpath' Funding provided by: National Institute of Food and AgricultureCrossref Funder Registry ID: https://ror.org/05qx3fv49Award Number: 2017-67004-26486/project accession no. 1012226 Funding provided by: National Institute of Food and AgricultureCrossref Funder Registry ID: https://ror.org/05qx3fv49Award Number: 2021-67034-35106/project accession no. 1026366 Funding provided by: Joint Fire Science ProgramCrossref Funder Registry ID: https://ror.org/03ccbtk93Award Number: Project JFSP 16-1-05-8 Funding provided by: Joint Fire Science ProgramCrossref Funder Registry ID: https://ror.org/03ccbtk93Award Number: Project JFSP 20-1-01-9
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For further information contact us at helpdesk@openaire.euintegration_instructions Research softwarekeyboard_double_arrow_right Software 2022Publisher:Zenodo Jaworski, Coline; Geslin, Benoît; Zakardjian, Marie; Lecareux, Caroline; Caillault, Pauline; Nève, Gabriel; Meunier, Jean-Yves; Dupouyet, Sylvie; Sweeney, Aoife; Lewis, Owen; Dicks, Lynn; Fernandez, Catherine;1. Study site: CLIMED long-term drought experiment All field data were collected in February-June 2018. We used a subset of established plots that were part of the CLIMED (CLImate change effects on MEDiterranean biodiversity) long-term drought experiment situated at Massif de l'Étoile in Marseille, France (43° 22' N, 5° 25' E). This site has a typical woody shrub community dominated by three species: Quercus coccifera Linnaeus, 1753 (Fagaceae; anemophilous and a resource of very limited use to pollinators in the region; Ropars et al., 2020a), Salvia rosmarinus Spenn., 1835 (Lamiaceae; previously Rosmarinus officinalis; Drew et al., 2017), and Cistus albidus Linnaeus, 1753 (Cistaceae; Montès et al., 2008). Local cumulative precipitation between January and May 2018 (the flowering period surveyed) reached 291 mm, while the average precipitation between January and May for the period 2008-2018 was 205 mm (Marseille-Marignane meteorological station; www.infoclimat.fr). The site is equipped with 46 metallic control and 46 4 × 4 m rain-exclusion shelters established in October 2011, spaced by 1 to 30 m (Santonja et al., 2017). Plot locations were chosen randomly at the time of establishment of the long-term experiment, and were assigned at random to control or drought treatment (Montès et al., 2008). Gutters from rain-exclusion shelters in drought plots were designed to exclude up to 30 % and excluded on average (± SE) 12 ± 2% of precipitation between 2011 and 2018 at the centre of the plots; the intercepted water was carried away from the site with a pipe system. In control plots, the upside-down gutters intercepted a very small fraction of precipitation and rainfall reached the ground (Montès et al., 2008; Santonja et al., 2017). This water deficit attempts to mimic the mean predicted changes during the dry season in the Mediterranean area by the end of this century except in winter when rainfall is expected to increase (Giorgi & Lionello, 2008: averages for 2071-2100 relative to 1961-1990: December to February +0 to +10 %, March to May -10 to -20 %, June to August -20 to -30 %, September to November -0 to -10 %; Mariotti et al., 2015: averages for 2071-2098 relative to 1980-2005: December to February -0.1 to +0.2 mm/day, June to August -0.1 to -0.3 mm/day). The moderate but chronic experimental water deficit induced by the CLIMED experiment can alter plant physiology: carbon assimilation was reduced in C. albidus, and transpiration was reduced in C. albidus and S. rosmarinus but water use efficiency was not significantly changed in 2014 (Rodriguez-Ramirez, 2017). Between January and May 2018, permanent soil moisture probes (TDR100, Campbell Scientific Inc., Logan, Utah) measured soil moisture at 10, 20 and 40 cm in two control and two drought plots. For clarity we use the term drought to refer to the drought treatment in our study. We selected 10 control plots and 10 drought plots out of the 92 plots, based on: (i) where Thymus vulgaris Linnaeus, 1753 (Lamiaceae) was present (four plots for each treatment only) because it is an important resource for pollinators (Ropars et al., 2020a); and (ii) a high and similar percentage cover of C. albidus and S. rosmarinus. The chosen control and drought plots were homogeneously distributed throughout the site. We measured the percentage cover of each species in selected plots twice (February and June 2018). The percentage cover of S. rosmarinus, C. albidus and Q. coccifera and T. vulgaris was 21, 19, 15 and 0.5 % on average respectively in the 20 plots selected, and the community composition did not differ significantly between treatments throughout the long-term experiment. Despite such low diversity, this plant community is natural, and is representative of the site and of the type of dense, closed vegetation plant communities found in the region in areas where wildfires are ancient (> 10 years; Pimont et al., 2018). Thymus vulgaris, C. albidus and S. rosmarinus are all perennial, entomogamous shrub species; T. vulgaris is gynodioecious and obligate entomogamous (dichogamous; Arnan et al., 2014), while S. rosmarinus and C. albidus are self-compatible but with limited self-pollination (Hammer & Junghanns, 2020; Blasco & Mateu, 1995). A fourth shrub species, Ulex parviflorus Pourr., 1788, was also present but very rare (0.3 % percentage cover) with very few flowers during the study period, and other flowering species were even rarer. We did not observe any insect visit to these very rare species and hence excluded them from our study. 2. Floral traits involved in pollinator attraction 2.1. Floral scent sampling and GC-MS analysis We randomly selected up to 14 plant individuals per species in each treatment (control vs. drought) with a maximum of two (four for T. vulgaris) plants in the same plot. A few samples were lost during laboratory analysis, hence final sample sizes were 23 (control: 11; drought: 12) for S. rosmarinus, 22 (control: 11; drought: 11) for C. albidus, and 19 (control: 6 female, 6 hermaphroditic; drought: 5 female, 2 hermaphroditic) for T. vulgaris. Branches of the selected flowering plants bearing around 30-50, 2-3 or 100-400 flowers [1st-3rd quantiles] for S. rosmarinus, C. albidus and T. vulgaris respectively, were enclosed in a Nalophan bag (NA CAL, 30 cm × 30 cm, thickness 25 µm, volume ~ 2L; ETS Charles Frères, Saint-Étienne, France) connected to a pumping system maintaining a 1000 mL/min and a 200mL/min inlet and outlet air flows, respectively, provided by pumps (DC 12V, NMP850KNDC, KNF Neuberger SAS, France) powered by batteries (RS Pro 5Ah, 12V, RS Components SAS, France) and controlled by debit-metres (F65-SV1 Porter, Bronkhorst, France). Inlet air was first purified with activated charcoal (untreated, Mesh 4-8, Sigma Aldrich, USA) to limit the amount of volatiles from ambient air. Second, excess of humidity was removed using drierite (W.A. Hammond DrieriteTM Indicating Absorbents Mesh size 8, USA). Finally, ozone was filtered out through a fiberglass filter disk impregnated with sodium thiosulfate (Na2S2O3) following Pollmann et al. (2005) to limit oxidation of plant volatile organic compounds (VOCs). Air flow was first stabilized for 15 min (the time required to entirely renew the air inside the 2L-bags). VOCs were then adsorbed on a cartridge placed at the bag outlet for 10 min for S. rosmarinus and T. vulgaris, and 15 min for C. albidus. This protocol optimizes the signal-to-threshold ratio without exceeding the breakthrough volume of each VOC in the conditions of our experiment, which would distort the estimated relative composition of chemical profiles (Ormeño et al., 2007). The cartridges were made of glass tubes (Gerstel OD 6 mm for TDS2/3, RIC SAS, Lyon, France) filled with 0.120 g Carbotrap® adsorbent (matrix Carbotrap® B, 20-40 mesh, Sigma-Aldrich, France) then 0.050 g Tenax® Porous Polymer Adsorbent (matrix Tenax® GR, 20-35 mesh, Sigma-Aldrich) separated by glass wool and maintained in the tube by a fixing screen (Gerstel for TDS 2 ID 4.0 mm, RIC SAS, France) at the entrance side and glass wool at the exit side. To discriminate VOCs emitted by plants from possible environmental contamination, ambient air was sampled after every five plant samples using the same protocol. VOCs from four leaf-only plant samples per plant species were also measured to investigate which VOCs contribute most to floral scent versus leaf scent, enclosing branches of comparable size than the inflorescences of floral samples in collection bags. Sample cartridges were stored in a cooler immediately after collection, and transferred to a freezer at -20 °C as soon as possible. Prior to sampling, all cartridges had been cleaned in a Thermal Adsorbent Regenerator (RTA EcoLogicSense RG1301002, TERA Environment SARL, France) at 300 °C for 4 h. To reduce environmental variation from flowering phenology in scent emissions, each species was sampled over three days maximum around the flowering peak, during sunny weather and between 10:00 and 15:00. Throughout sampling, temperature and humidity were recorded inside and outside plant bags with data loggers (OM-EL-USB2-LCD, Omega Engineering Limited, UK). Plant parts inside bags were cut after sampling, dried in an oven at 50°C, and weighed after mass had stabilized (3-5 days, depending on plant species). Samples were analysed one to 20 days after sampling. VOCs were thermodesorbed (cool trap and flash heating -50 to 250 °C at 12°C/s for 10 min; TDS 3 Gerstel equipped with an autosampler TDS A Gerstel). They were analysed with a gas chromatograph coupled with a quadrupole low-resolution mass spectrometer in solvent vent and CIS splitless mode (GC 6890N; MS 5973N; Agilent) equipped with a HP-5MS non-polar capillary column (5 % phenyl-methylsiloxane; length 30 m; internal diameter 0.25 mm; film thickness 0.25 μm; Agilent 19091S-433). The temperature gradient applied to the column was 40°C for 5 min, then up to 245 °C at 3 °C/min and maintained for 2 min (total run time 75.33 min). The carrier gas was helium at 7.1 psi and 1 mL/min. Mass spectra were recorded in the scan EMV mode (EM voltage 1295 eV and scanned from m/z 40 to 400, with one scan every 0.004 min. Chromatograms were analysed with MZmine2 (version 2.18.1 developed for gas chromatography; Pluskal et al., 2010) in a 11-steps batch. Briefly, the baseline of each chromatogram was adjusted to 0, then values of m/z in each scan extracted and attributed to a peak. Peak heights (sum of m/z), areas and retention times, as well as mass spectrum at peak maximum were then exported from MZmine2 and imported into R (R Core Team, 2020; version 3.6.3) for peak identification. Retention indexes of each peak were calculated via a linear approximation built on a C5-C20 n-alkane series injected externally (Van Den Dool & Kratz, 1963). The retention indexes of 21 external standards were also verified with this method and checked against literature (Adams, 2007). The similarity between each peak's mass spectrum and reference mass spectra was calculated using the R function 'SpectrumSimilarity' (R library 'OrgMassSpecR v0.5-3'; Stein & Scott, 1994). The reference mass spectra were that of the 21 standards, and of two libraries converted in JPS format: the Adams 2007 library (Adams, 2007), and the NIST11 (NIST, 2011) library, was used when all similarity hits from the Adams 2007 library were lower than 0.7. Only similarity to reference molecules with a retention index within ± 15 of the peak's calculated retention index was calculated. Identification was processed sequentially starting with the most common VOCs and retention indexes were adjusted locally (+/- 10) at each step based on these new identifications. Peaks with highest similarity < 0.6 were discarded. Peaks with the same identity were then aligned for each species. True absence of a VOC was verified, and areas not integrated with the first MZmine2 round were manually added, similarity calculated as above, with peak discarded if similarity < 0.6 and using a smaller retention index tolerance of ± 5 (0.2 min on RT). Only VOCs previously reported as known plant volatiles in Pherobase (El-Sayed, 2019) or in a comprehensive review (Knudsen et al., 2006). Most of them had also previously been reported in oil extracts or as plant volatiles from the three study species (Katerinopoulos et al., 2005; Ormeño et al., 2007; Satyal et al., 2016). Following Campbell et al. (2019), we removed ambient air contaminants, by selecting only VOCs whose areas exceeded three times that of ambient air samples, by performing two-sample t-tests on , where A is the area of a peak (MZmine2 integration), Q and q are the inlet and outlet flows, respectively, and t is the sampling time, and using the type of sample (air vs. flower sample) as a factor, and with a false-discovery rate of 5 % to control for multiple comparisons. We also removed VOCs quantified in fewer than three samples of each species, because they added too much variance. We then removed the contribution of air samples to the retained VOCs in plant samples (each plant sample matched with the air sample taken closest in time) and calculated emission rates (in µg.h-1.gDM-1): (Sabillón & Cremades, 2001), with k the response coefficient calculated for each chemical family based on external calibration of pure standards (see below), and mDM the total dry mass of the inflorescence branch inside the sampling bag (flowers and leaves). Emission rates were then standardized by temperature (measured inside the bag during air flow stabilization and sampling; Ormeño et al., 2007; Sabillón & Cremades, 2001): where T is the temperature inside the sampling bag (in °K). 2.2. MZMine2 batch parameters 1. Raw data import Select all raw chromatogram files associated with one species 2. Filter scans Filter selected "Round resampling" "Sum duplicate intensities" = True "Remove zero intensity m/z peaks" = True 3. Crop filter Retention time: "min" = 4.5 min (remove solvents and water eluted in the first min) "max" = 64.0 min (after Docosane ~ 311 da; upper limit for molecule volatility at ambient temperature is ~ 300 da). m/z: "min" = 40 (lower limit of mass spectrometer acquisition) "max" = 315 (mass of Docosane molecular peak) 4. Baseline correction Correction method: "RollingBall baseline corrector" "wm (number of scans)" = 200 "ws (number of scans) = 5 5. Mass detection mass detector selected: "Centroid" "Noise level" = 2000 for 1st round, 1000 for 2nd round 6. Chromatogram builder "min time span (min)" = 0.05 "min height" = (same as 5.) "m/z tolerance" = 0.5 (absolute) / 0.001 (ppm) 7. Smoothing "Filter width" = 15 8. Deconvolution Algorithm selected : "Local minimum search" "Chromatographic threshold" = 0.50 "Search minimum in RT range (min)" = 0.04 "Minimum relative height" = 0.001 "Minimum absolute height" = 2000 "Min ratio of peak top/edge" = 1.2 "Peak duration range (min)": "min" = 0.05; "max" = 2.0 9. Peak merging "m/z tolerance" = 500 (absolute) / 5.0 (ppm) "RT tolerance window (number of scans)" = 15 "Use original raw data file" = False "Use detected peaks only" = False "Cumulative computing mode (TIC)" = True 10. Join Aligned (GC module) Our aim here was to align as few peaks as possible, so as to have one line per peak in the final matrix "m/z tolerance" = 0.5 (absolute) / 5.0 (ppm) "Weight for m/z" = 0.8 "Retention time tolerance" = 0.001 "Weight for RT" = 0.2 "Minimum score" = 0.7 "Use RT recalibration" = False "Use detected m/z only" = False "RT tolerance post-recalibration" = 0.4 "Export dendrogram as TXT" = False 11. Export CSV Export Peak RT, RTstart, RTend, Height, Area 2.3. External calibrations and calculation of response coefficients k Three different mixtures of pure standard chemicals (Sigma-Aldrich) were made using cyclohexane as solvent. Cartridges were loaded with 1 µL of the dilutions, except for Mixture 3 in which they were loaded with 1 µL of the solid mixture and 1 µL of the E-Caryophyllene solution. Three samples of each dilution were analysed for each mixture, in the same conditions as plant samples (see Main text). Two linear regressions were calculated: at low injected masses, and high injected masses (DIL6 and DIL5B for Mixtures 1 and 2, and DIL4 and DIL3 for Mixture 3). This is because for high injected masses the column is overloaded and this leads to a smaller than expected peak area. An intercept was used in linear regressions, because areas at low injected masses were indistinguishable no matter the dilution, showing that the quantification threshold (= intercept) was higher than lowest dilutions tested. In the conditions of the analysis it was impossible to properly quantify exact mass of VOCs below that threshold. 2.4. Nectar production Pollinators are declining globally, with climate change implicated as an important driver. Climate change can induce phenological shifts and reduce floral resources for pollinators, but little is known about its effects on floral attractiveness and how this might cascade to affect pollinators, pollination functions and plant fitness. We used an in situ long-term drought experiment to investigate multiple impacts of reduced precipitation in a natural Mediterranean shrubland, a habitat where climate change is predicted to increase the frequency and intensity of droughts. Focusing on three insect-pollinated plant species that provide abundant rewards and support a diversity of pollinators (Cistus albidus, Salvia rosmarinus and Thymus vulgaris), we investigated the effects of drought on a suite of floral traits including nectar production and floral scent. We also measured the impact of reduced rainfall on pollinator visits, fruit set and germination in S. rosmarinus and C. albidus. Drought altered floral emissions of all three plant species qualitatively, and reduced nectar production in T. vulgaris only. Apis mellifera and Bombus gr. terrestris visited more flowers in control plots than drought plots, while small wild bees visited more flowers in drought plots than control plots. Pollinator species richness did not differ significantly between treatments. Fruit set and seed set in S. rosmarinus and C. albidus did not differ significantly between control and drought plots, but seeds from drought plots had slower germination for S. rosmarinus and marginally lower germination success in C. albidus. Synthesis. Overall, we found limited but consistent impacts of a moderate experimental drought on floral phenotype, plant reproduction and pollinator visits. Increased aridity under climate change is predicted to be stronger than the level assessed in the present study. Drought impacts will likely be stronger and this could profoundly affect the structure and functioning of plant-pollinator networks in Mediterranean ecosystems. MZMine: Pluskal, T., Castillo, S., Villar-Briones, A., & Orešič, M. (2010). MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics, 11, 395. R Core Team. (2020). R: A Language and Environment for Statistical Computing. Vienna, Austria, https://www.R-project.org/. Funding provided by: AXA Research FundCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001961Award Number:
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