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Increasing levels of anthropogenic underwater noise have caused concern over their potential impacts on marine life. Offshore renewable energy developments and seismic exploration can produce impulsive noise which is especially hazardous for marine mammals because it can induce auditory damage at shorter distances and behavioural disturbance at longer distances. However, far-field effects of impulsive noise remain poorly understood, causing a high level of uncertainty when predicting the impacts of offshore energy developments on marine mammal populations. Here we used a 10-year dataset on the occurrence of coastal bottlenose dolphins over the period 2009-2019 to investigate far-field effects of impulsive noise from offshore activities undertaken in three different years. Activities included a 2D seismic survey and the pile installation at two offshore wind farms, 20-75 km from coastal waters known to be frequented by dolphins. We collected passive acoustic data in key coastal areas and used a Before-After Control-Impact design to investigate variation in dolphin detections in areas exposed to different levels of impulsive noise from these offshore activities. We compared dolphin detections at two temporal scales, comparing years and days with and without impulsive noise. Passive acoustic data confirmed that dolphins continued to use the impact area throughout each offshore activity period, but also provided evidence of short-term behavioural responses in this area. Unexpectedly, and only at the smallest temporal scale, a consistent increase in dolphin detections was observed at the impact sites during activities generating impulsive noise. We suggest that this increase in dolphin detections could be explained by changes in vocalization behaviour. Marine mammal protection policies focus on the near-field effects of impulsive noise; however, our results emphasize the importance of investigating the far-field effects of anthropogenic disturbances to better understand the impacts of human activities on marine mammal populations. Echolocation detectors (CPODs; Chelonia Ltd) were deployed between 2009 and 2019 to investigate the variation in dolphin detections in relation to the impulsive noise from three energy developments: a seismic survey for oil and gas exploration and the installation of foundation piles for two offshore wind farms (Beatrice Offshore Wind Farm and Moray East Offshore Wind Farm). Data on the timing of the seismic survey and piling operations were provided by the developers (Oil and Gas UK Ltd., COWRIE, Beatrice Offshore Wind Ltd. and Moray Offshore Wind Farm East). Data consist of 7 files and include the datasets and R code required to repeat all the analyses. A full description of the files provided in the Readme.txt file: OFB_FarField_DPH.csv OFB_FarField_BOWL.csv OFB_FarField_MEOW.csv OFB_FarField_BACI_Obtain_DPH_Dataset.R OFB_FarField_DPH_for_BACI.csv OFB_FarField_BACI_DPH_Models.R OFB_FarField_Readme.txt

Processes leading to range contractions and population declines of Arctic megafauna during the late Pleistocene and early-Holocene are uncertain, with intense debate on the roles of human hunting, climatic change, and their synergy. Obstacles to a resolution, have included an over reliance on correlative rather than process-explicit approaches for inferring drivers of distributional and demographic change. Using process-explicit macroecological models that integrate modern and fossil occurrence records, spatiotemporal reconstructions of past climatic change, speciesspecific population ecology and the growth and spread of anatomically modern humans, we disentangle the ecological mechanisms and threats that were integral in the decline and extinction of the muskox (Ovibos moschatus) in Eurasia, and in its expansion in North America. We show that accurately reconstructing inferences of past demographic changes for muskox over the last 21,000 years requires high dispersal abilities, large maximum densities, and a small Allee effect. Climatic change was the primary driver of muskox distribution shifts and demographic changes across its previously extensive (circumpolar) range, with populations responding negatively to rapid warming events. Regional analyses reveal that the range collapse and extinction of the muskox in Europe (~ 13 thousand years ago) was caused by humans operating in synergy with climatic warming. In Canada and Greenland, climatic change and human activities combined to drive recent population sizes. The impact of past climatic change on the range and extinction dynamics of muskox during the Pleistocene-Holocene transition signals a vulnerability of this species to future increased warming. By disentangling the ecological processes that shaped the distribution of the muskox through space and time, process-explicit models have important applications for the future conservation and management of this iconic species in a warming Arctic. We built process-explicit macroecological models of muskox that simulate interactions between metapopulation dynamics, climate variability, and hunting by humans. We used calibrated fossils and modern occurrence records obtained from publicly available databases and published literature. Records were intersected with paleoclimate reconstructions accessed using PaleoView, and modern climate data from CRU TS v4. Niche hypervolumes and spatiotemporal projections of habitat suitability were built in R using the 'hypervolume' package. Process-explicit macroecological models were built in R using the 'poems' and 'paleopop' package. Human abundance was modelled using a Climate Informed Spatial Genetics Model (CISGeM). Funding provided by: Australian Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000923Award Number: DP180102392Funding provided by: Australian Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000923Award Number: FT140101192Funding provided by: Danish Research Foundation*Crossref Funder Registry ID: Award Number: DNRF96

We compiled data from experimental studies on fisheries species that compared mortality of parasitized and unparasitized hosts at a static temperature. We defined fisheries species to include both invertebrate and vertebrate species that are harvested commercially or recreationally. In Fall 2019, we searched Web of Science following PRISMA protocols (O'Dea et al. 2021) using key terms that would return papers focused on harvested aquatic species, parasites, and diseases, but would exclude papers that were focused on human, environmental or domestic animal health (see Appendix S1 in Supporting Information). This search yielded 1,201 papers. We then screened the abstracts of these papers, and retained only papers that satisfied four criteria: 1) an experiment was performed that included at least one parasite exposure treatment paired with an unexposed control group, 2) temperatures were intended to be constant and not intentionally varied, 3) hosts were from species that constitute a fishery, including those in aquaculture, and 4) estimates of survival or mortality were reported for both infected and uninfected hosts at each temperature treatment. This selection process reduced the number of studies to 386 (Appendix S1 and Figure S1). We obtained full versions of 364 papers (22 papers from the original 386 were unobtainable). We then screened the full text of these papers to ensure a match to our four criteria, which reduced the 364 papers to 70. To increase statistical power to estimate the effect of host Order on parasite-induced mortality, we excluded experiments from hosts in Orders with fewer than three effect sizes. This reduced the number of papers included in our study from 70 to 60 and yielded a total of 301 effect sizes from 140 experiments (several papers included more than one experiment; Appendix S1 and S2, Figure S1). At least two people extracted data from each paper to reduce extraction error. If extracted values differed, the data were re-extracted until there was agreement between the two extractors. For data that were displayed in a graphical format only, we used WebPlotDigitizer (Rohatgi 2022) to extract data. Data (which may have been presented as mortality rates, or proportion surviving) were converted to numbers of host individuals that were dead and alive at the end of the experiment. We also extracted information about the paper itself, including the source of the hosts used in the paper and the motivation for conducting the experiment (see Appendix S1). Finally, we collected additional information about host and parasite traits from outside sources (e.g., other peer reviewed papers, government reports) when necessary to obtain moderator variables (Table 1, Appendix S1 and S2). The moderators (Table 1) were used to test a priori hypotheses regarding how host, parasite, and study design traits influenced how temperature affected parasite-induced mortality. Because our focus was on parasite-induced mortality, we used log odds ratios and the variance surrouding log odds ratio as our effect size to compare host survival in the parasitized vs unparasitized treatments. Rapid warming could drastically alter host-parasite relationships, which is especially important for fisheries crucial to human nutrition and economic livelihoods; yet we lack a synthetic understanding of how warming influences parasite-induced mortality in these systems. We conducted a meta-analysis using 301 effect sizes from 60 empirical papers on harvested aquatic species and determined the relationship between parasite-induced host mortality and temperature and how this relationship was altered by host, parasite and study design traits. Overall, temperature increased parasite-induced host mortality; however, the magnitude and sometimes direction of this relationship varied. Hosts from the order Salmoniformes experienced a greater increase in parasite-induced mortality with temperature than average. Opportunistic parasites were correlated with a greater increase in host mortality with temperature than average, while bacterial parasite-induced mortality was lower than average as temperature increased. Thus, parasites will generally increase host mortality as the environment warms; however, this effect will vary among systems. Funding provided by: National Science FoundationCrossref Funder Registry ID: https://ror.org/021nxhr62Award Number: DGE-1545433 Funding provided by: Georgia Sea GrantCrossref Funder Registry ID: https://ror.org/0014w1417Award Number: NA180AR417008 Funding provided by: University of GeorgiaCrossref Funder Registry ID: https://ror.org/00te3t702Award Number: Funding provided by: National Science FoundationCrossref Funder Registry ID: https://ror.org/021nxhr62Award Number: DEB-1655426

Rapid urbanisation along the coasts of the world in recent decades has increased their vulnerability to storm surges, especially in response to mean sea level rise. The unique geographical and social conditions of Copenhagen, a major European coastal city, have prompted urban expansion along Køge Bay to the south of the city. However, this new urbanisation area is confronted with the common obstacle of developing a coastal defence strategy, i.e., the lack of long-term observational data required to determine a reliable storm surge protection level. This study aims to address this issue by developing a framework that integrates historical records of extreme storm surge events into coastal defence strategies, using Copenhagen as a case study. 'Statistical Modelling and Forecasting' is one of the steps in our proposed four-step framework solution. Using Bayesian statistical methods, we fitted the historical storm surge data to appropriate probability distributions. This enabled us to generate probabilistic forecasts of storm surge magnitudes, providing insight into the likelihood of future events and their potential impacts on the coastal area. Bayesian MCMC methods offer a powerful framework for incorporating uncertainty and expert knowledge into extreme value analysis. By utilising prior distributions and combining them with the likelihood function, these methods enable the estimation of posterior distributions of model parameters. This is particularly advantageous when dealing with limited data, as expert opinions and historical knowledge can be effectively integrated. We outline the application of the aforementioned extreme value analysis techniques and Bayesian MCMC methods within our four-step framework to integrate historical storm surge events into coastal defence strategies.

As climate change continues, anticipating species' responses to rising temperatures requires an understanding of the drivers of thermal sensitivity, which itself may vary over space and time. We measured metabolic rates of three representative marine invertebrate species (hermit crabs Pagurus hirsutiusculus, periwinkle snails Littorina sitkana, and mussels Mytilus trossulus) and evaluated the relationship between thermal sensitivity (Q10) and thermal history. We tested the hypothesis that thermal history drives thermal sensitivity and quantified how this relationship differs over time (short-term to seasonal time scales) and between species. Organisms were collected from tide pools in Sitka, Alaska where we also recorded temperatures to characterize thermal history prior to metabolic rate assays. Using respirometry, we estimated mass-specific oxygen consumption (MO2) at ambient and increased temperatures for one individual per species per tide pool across three seasons. We evaluated relationships between thermal sensitivity and pool temperatures for time periods ranging from 1 day to 1 month prior to collection. For all species, thermal sensitivity was related to thermal history for the shorter time periods (1 day to 1 week). However, the direction of the relationships and most important thermal parameters (i.e., maximum, mean, or range) differed between species and seasons. We found that on average, P. hirsutiusculus and L. sitkana were more thermally sensitive than M. trossulus. These findings show that variability in thermal history over small spatial scales influences individuals' metabolic response to warming and may be indicative of these species' ability to acclimate to future climate change. Temperature Data:Temperature data was collected at the tide pool level using Onset ® HOBO TidbiT temperature loggers (±0.2 accuracy) that recorded temperature consecutively every 5 min from December 2018 to September 2019. Temperature data were summarized for the 1-month, 1-week, and 1-day periods preceding each collection. For each tidepool, we calculated the following thermal parameters: variance, minimum, mean daily minimum, 10th percentile, range, mean daily range, mean daily average, maximum, mean daily maximum, 90th percentile, mean daily 90th percentile, 95th percentile, and mean daily 95th percentile temperatures.Oxygen Consumption Data:Raw Oxygen data was collected using Pre-Sens PSt3 sensor spots (PreSens Precision Sensing, Germany) and a 10-channel OXY-10 SMA (G2) oxygen meter (PreSens Precision Sensing, Germany). Data were processed after visual inspection and fit with a linear regression of oxygen consumption over time (only slopes that had an R2>0.90 were used in the analysis). Please use README files on how to use these datasets.Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: OCE-1756173Funding provided by: International Women's Fishing Association*Crossref Funder Registry ID: Award Number: Funding provided by: Ford FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000010Award Number:

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. 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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

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.

This software involves files to fit the physiologically-guided abundance (PGA) model ('fish_predictions_pga.R'), a naive model ('fish_predictions_naive.R'), and a PGA model using simulated data ('PGA_sim.R'). Each R file calls as associated .stan file that contains the model code that is executed by stan when running the R code ('fish_prediction.stan', 'fish_prediction_naive.stan', and 'fish_prediction_sim.stan', respecively.). The R scripts are currently setup to fit the model to bluegill data, but this can be changed to either yellow perch or cisco within the R scrip. The data for fitting the models can be found at https://hdl.handle.net/11299/228403.