• Energy Research
• 14. Life underwater close
• US close
• CA close

Analyses of ancient food webs reveal important paleoecological processes and responses to a range of perturbations throughout Earth’s history, such as climate change. These responses can inform our forecasts of future biotic responses to similar perturbations. However, previous analyses of ancient food webs rarely accounted for key differences between modern and ancient community data, particularly selective loss of soft-bodied taxa during fossilization. To consider how fossilization impacts inferences of ancient community structure we (1) analyzed node-level attributes to identify correlations between ecological roles and fossilization potential and (2) applied selective information loss procedures to food web data for extant systems. We found that selective loss of soft-bodied organisms has predictable effects on the trophic structure of “artificially fossilized” food webs, because these organisms occupy unique, consistent food web positions. Fossilized food webs misleadingly appear less stable (i.e., more prone to trophic cascades), with less predation and an overrepresentation of generalist consumers. We also found that ecological differences between soft- and hard-bodied taxa—indicated by distinct positions in modern food webs—are recorded in an Early Eocene web, but not in Cambrian webs. This suggests that ecological differences between the groups have existed for ≥ 48 million years. Our results indicate that accounting for soft-bodied taxa is vital for accurate depictions of ancient food webs. However, the consistency of information loss trends across the analyzed food webs means it is possible to predict how the selective loss of soft-bodied taxa affects food web metrics, which can permit better modeling of ancient communities. Repository Contents: Supplementary Information: Containing Supplementary Text, Figures, Tables, and Data descriptions. Supplementary Data 1: Food web data (adjacency matrices and metadata; see publication; see Related Works). Supplementary Data 2: Additional references consulted for preservation group assignments. Supplementary Data 3: Data and R scripts to recreate analyses from this study. S3_AllWebTaxonomy_updated_200903.csv: Taxonomy data for all food web nodes. S3_AnalysisOfTaxonomicRanks.csv: Lowest taxonomic rank for each node. S3_MainFigures_CaimanComparison.R: Compare the three food webs contained in (Roopnarine and Hertog 2013). S3_MainFigures_ComparisonFunctions.R: Functions for calculating metrics and generating trophic species webs. S3_MainFigures_FossilizationFunctions.R: Functions for artificially fossilizing networks. S3_MainFigures_Setup_200826.R: Setup to import food webs. S3_MainFigures_Code.R: Code to apply functions. S3_pbdb_data_200504.csv: Data from the Paleobiology Database, excluding Lagerstätten (see publication). S3_PresGrAssignments_updated_200902.csv: Preservation group assignments for all nodes. Fossil faunal lists were downloaded from the PBDB on 17th January 2020. Any data processing steps are shown in R Scripts and described in publication. Paleobiology Database is licensed under a CC BY 4.0 International License. https://creativecommons.org/licenses/by/4.0/. We analyzed food webs for four modern marine systems, one modern freshwater system, two ancient marine systems, and one ancient lake system from previous publications. All webs have similar, broad higher-rank taxonomic compositions and contain at least 85 nodes (the size of the smallest ancient network considered). For comparisons with ancient diversity, we downloaded fossil occurrences from the Paleobiology Database (PBDB) on 17th January 2020. 

Aim: Climate-induced pulse (e.g., hurricanes) and press (e.g., global warming) disturbances represent threats to populations, communities, and the ecosystem services that they provide. We leveraged three decades of annual data on tropical gastropods to quantify the effects of major hurricanes, associated secondary succession, and global warming on abundance, biodiversity, and species composition. Location: Luquillo Mountains, Puerto Rico. Methods: Gastropod abundance, biodiversity, and composition were estimated annually for each of 27 years in a tropical montane forest that experienced three major hurricanes (Hugo, Georges, and Maria). Generalized linear mixed-effects, linear mixed-effects, and linear models evaluated population- and community-level responses to year, ambient temperature, understory temperature, hurricane, and time since hurricane. Variation partitioning determined the unique and shared variation in biotic responses associated with temperature, disturbance, and succession. Results: Rather than declining, gastropod abundances generally increased through time, whereas the responses of biodiversity were weak and scale dependent. Hurricanes and associated secondary succession, rather than ambient atmospheric temperature, molded long-term trends in abundances and biodiversity. Main conclusions: Global warming over the past 30 years has not progressed sufficiently to elicit significant responses by gastropods in the Luquillo Mountains. Rather, effects from pulse disturbances (i.e., hurricanes) and secondary succession currently drive long-term variation in abundance and biodiversity. Gastropods evince high resilience in this tropical ecosystem. Historical exposure to recurrent hurricanes likely imbued the fauna with broad niches that make them resistant to current levels of global warming. We predict that biotic resiliency will be challenged once changes in temperature exceed interannual and inter-habitat differences that typify this hurricane-mediated system, or combine with an increased frequency of hurricanes and droughts to alter associations among environmental characteristics that define the fundamental niches of species. Only then might significant declines in abundance or the appearance of novel communities characterize the gastropod fauna in the Luquillo Mountains. Gastropods were surveyed annually from 1993 through 2019 at each of 40 points on the LFDP. At each point, all surfaces (rocks, litter, debris, vegetation) within a 3 m radius and up to 3 m of height were inspected for gastropods. Surveys were conducted at night, when terrestrial gastropods are most active (Heatwole & Heatwole, 1978). The same 40 points were surveyed twice in 1993, thrice in 1994, and four times all other years. To minimize alteration of long-term study sites, litter was not manipulated, and specimens were returned as closely as possible to the point of capture. Consequently, our considerations are restricted to gastropods that occur on or above ground litter. Seventeen species of terrestrial gastropod typically occur in these habitats, with some species being undetected in particular years (e.g., Diplosolenodes occidentalis, Obeliscus terebraster, Subulina octona) and other species occurring at over half of the survey points during most years (e.g., Caracolus caracolla, Gaeotis nigrolineata, Nenia tridens). All data are from the Luquillo Long-Term Ecological Research Site, whose collection is supported by the US National Science Foundation (NSF). Gastropod data are archived publicly in accordance with NSF guidelines (https://luq.lter.network/data/luqmetadata107 or https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-luq.107.9996737). Additional details on sampling (Willig et al., 1998) and gastropod autecology (Garrison & Willig, 1996) in the Luquillo Experimental Forest are available elsewhere.

Ambient conditions shape microbiome responses to both short- and long-duration environment changes through processes including physiological acclimation, compositional shifts, and evolution. Thus, we predict that microbial communities inhabiting locations with larger diel, episodic, and annual variability in temperature and pH should be less sensitive to shifts in these climate-change factors. To test this hypothesis, we compared responses of surface ocean microbes from more variable (nearshore) and more constant (offshore) sites to short-term factorial warming (+3 °C) and/or acidification (pH -0.3). In all cases, warming alone significantly altered microbial community composition, while acidification had a minor influence. Compared with nearshore microbes, warmed offshore microbiomes exhibited larger changes in community composition, phylotype abundances, respiration rates, and metatranscriptomes, suggesting increased sensitivity of microbes from the less-variable environment. Moreover, while warming increased respiration rates, offshore metatranscriptomes yielded evidence of thermal stress responses in protein synthesis, heat shock proteins, and regulation. Future oceans with warmer waters may enhance overall metabolic and biogeochemical rates, but they will host altered microbial communities, especially in relatively thermally stable regions of the oceans. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2019) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-10-20.

Increasingly frequent marine heatwaves are devastating coral reefs. Corals that survive these extreme events must rapidly recover if they are to withstand subsequent events, and long-term survival in the face of rising ocean temperatures may hinge on recovery capacity and acclimatory gains in heat tolerance over an individual's lifespan. To better understand coral recovery trajectories in the face of successive marine heatwaves, we monitored the responses of bleaching-susceptible and bleaching-resistant individuals of two dominant coral species in Hawaiʻi, Montipora capitata and Porites compressa, over a decade that included three marine heatwaves. This dataset contains benthic cover data and photoquadrat images including point counts and organism identifications from patch reef 13 in Kāne'ohe Bay, O'ahu, Hawai'i from 2015 to 2022.

Summary We used a combination of regional ocean climate projections and simulated species distributions (Leroy et al., 2016) to quantify sources of uncertainty in projections of spatially-explicit biomass for three species archetypes in the CCS (1985-2100; Fig. 1). Species archetypes were simplified representations of three general groups of marine finfish found in the CCS that comprise ecologically and/or economically important fisheries and that might be expected to show variable patterns of redistribution under climate change based on their habitat preferences, population dynamics, and mobility characteristics: 1) a highly migratory species (HMS) that was designed to resemble north Pacific albacore; 2) a coastal pelagic species (CPS) that was designed to resemble northern anchovy (CPS); and 3) a groundfish species (GFS) that was designed to resemble sablefish. SDMs (n=15; Figure 1) were then fitted to simulated biomass data for each archetype (training period 1985-2010) and projected from 2011-2100 using each of the three regional ocean climate models. Our framework resulted in 252 SDMs (15 SDM types, three species archetypes, three ESMs, and two environmental parameter simulations; Figure 1). To address our study goal of assessing SDM performance and understanding sources of uncertainty in species distribution projections, we compared the output of SDM projections against simulated “observations” for 2011-2100 and quantified the uncertainty introduced by the climate projection (ESM uncertainty) versus the uncertainty introduced by the SDM structure (SDM uncertainty). Environmental Covariates from Regional Ocean Projections Environmental covariates used in species distribution simulations were obtained from regional ocean projections (Pozo Buil et al., 2021) forced by three ESMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive: Geophysical Fluid Dynamics Laboratory (GFDL) ESM2M, Hadley Center HadGEM2-ES (HAD), and Institut Pierre Simon Laplace (IPSL) CM5A-MR. These ESMs, hereafter referred to as GFDL, HAD, and IPSL, span the approximate range of potential changes in physical and biogeochemical conditions across all CMIP5 models (Pozo Buil et al., 2021). ESMs were downscaled using the Regional Ocean Modelling System (ROMS) coupled with a biogeochemical model (NEMUCSC) (Fiechter et al., 2018, 2021) based on the North Pacific Ecosystem Model for Understanding Regional Oceanography (NEMURO) (Kishi et al., 2007). The ROMS domain spans the CCS from 30-48°N and from the coast to 134°W at 0.1° horizontal resolution with 42 terrain-following vertical layers (Figure 2). Each downscaled ESM used the Representative Concentration Pathway (RCP) 8.5 climate change scenario. While we only examined RCP 8.5, it should be noted that using RCPs 2.6 and 4.5 would result in only minor differences in the spread of future environmental change for the variables and ESMs examined here. Specifically, uncertainty in biogeochemical change among the chosen ESMs in RCP8.5 envelops the uncertainty among RCPs 2.6 and 4.5; while for temperature GFDL and HAD represent opposite ends of the spectrum for the projected magnitude of warming in the CMIP5 ensemble (Drenkard et al., 2021; Pozo Buil et al., 2021). As such, we do not explore scenario uncertainty. Environmental covariates used in species distribution simulations were sea surface temperature (SST; C), bottom temperature (BT; C), bottom oxygen (BO; mmol m-3), mixed layer depth (MLD; m), surface chlorophyll-a (Chl-a; mg m-3), and zooplankton concentration integrated over 50 m (zoo_50; mmol N m-2) and 200 m (zoo_200; mmol N m-2). These environmental covariates were averaged over spring months (March-May) annually (1985-2100) to encompass the seasonal period when ocean productivity is most influential on the long-term population dynamics of most marine fishes in the CCS. Operating Models: Simulated Species Biomass Biomass distributions for three species archetypes were simulated on the ROMS grid for each year and each ESM from 1985-2100. Simulations were run using the ‘virtualspecies’ R package (Leroy et al., 2016) that is specifically designed to reflect real-world ecological properties and species-environment relationships (Meynard et al., 2019). We refer to these simulated species distributions as ‘operating models’. Species simulations used a two-step process. First, habitat suitability was calculated based on environmental data and specified species’ habitat preferences (Table S1). Environmental preferences used to force species distributions varied among species archetypes based on representative life histories (see Supplementary Material). The domain for the HMS archetype was set to the entire CCS, whereas the CPS and GFS archetypes were reduced to inshore waters to reflect the CPS archetype’s preference for pelagic waters over the continental shelf and slope, and the GFS archetype’s preference for demersal shelf and slope habitats (Leeuwis et al., 2019; Stierhoff et al., 2020). Second, total habitat suitability was calculated, and converted to presence-absence using a logistic function (which specifies at what suitability value the species becomes present). When species were present, biomass was estimated from a log-normal distribution, and when species were absent biomass was set to zero. Biomass at each grid cell was multiplied by habitat suitability of that same grid cell to provide habitat-informed biomass. For CPS and GFS archetypes, an additional biomass multiplier was used to encompass population-level dynamics (Figure S1; see supplementary methods) (Punt et al., 2016). Specifically, CPS biomass was made to reflect boom-bust population dynamics that are common in CPS species in the CCS, while GFS biomass integrated a 20-year phase shift between low and high recruitment, as has been observed for sablefish (Haltuch et al., 2019). Simulated data were generated for each grid cell (HMS = 21912 grid cells; CPS & GFC = 4012 grid cells) once per year for 116 years (1985-2100). Detailed methods for the simulation are provided in the Supplementary material, and R code is provided on GitHub (https://github.com/stephbrodie1/Projecting_SDMs). Estimation Models: Species Distribution Models We parameterized a series of SDMs to estimate the relationship between simulated species biomass and covariates (Figure 1). Because these are fitted to data from an operating model, we refer to these SDMs as ‘estimation models’. Multiple approaches were tested to explore how decisions about model type and parameterization influence model accuracy and predictive performance (Brodie et al., 2020). We used four types of SDMs: generalized additive models (GAM), generalized linear mixed models (GLMM), boosted regression trees (BRT), and multilayer perceptron models (MLP; a type of artificial neural network model) (Table S2). Parameterization options included various combinations of environmental (E), spatial (S), and temporal (T) covariates (Figure 1; see supplementary methods). Spatial and temporal covariates can act as proxies for unobserved or unmeasured processes that drive species distributions, and were included here given their common use in SDMs (typically called spatiotemporal models) (Brodie et al., 2020). We expect spatiotemporal SDMs with no environmental covariates to perform poorly over the projection period. We constructed all SDMs as delta (hurdle) models, where the probability of occurrence (binomial) and positive biomass (log-normal) were estimated as separate processes. All SDMs were trained on data from 1985-2010, where only 500 random samples per year (2% of available data) were used for fitting (n=13 000). Random samples included both presence and absences sampled across the entire domain. No SDM validation or model selection was required as our simulation experiment is designed to explore a range of model parameterizations. Fitted SDMs were then used to predict species biomass on projected environmental data, for every year and grid cell in the domain. Only 500 randomly sampled grid cells per year (2011-2100) were used for testing purposes (n=45 000), to match the resolution of samples used to train models. Importantly, not all environmental covariates used to simulate species biomass (see 2.3 above) were included in the fitted SDMs. Specifically, we used chlorophyll-a as a proxy for prey fields (zooplankton) to approximate real-world conditions where imperfect information is available for estimating species’ habitat preferences. In addition to the 15 SDM parameterizations listed in Figure 1, we examined SDMs that only contained a single covariate of temperature (either surface or bottom temperature depending on the archetype). This experiment was done to test how under-parameterized models that miss key environmental drivers of species distributions performs, and the degree to which this approach decreases model fit and increases projection uncertainty. We refer to these SDMs as ‘temperature-only’ models (Figure 1).  Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change - rather than accurately predict specific outcomes - it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling. The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true-state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change. .rds and raster files can be opened in R statistical software. 

Biomass of ulvoid algae in Armitage Bay, Blakely Island, Washington by species composition.

1. The maintenance of habitat heterogeneity in agricultural landscapes has been promoted as a key strategy to conserve biodiversity. Animal response to grassland heterogeneity resulting from spatiotemporal variation in disturbance is well documented; however, the degree to which edaphic variation generates heterogeneity detectable by grassland wildlife has proven more difficult to study in natural settings. 2. We conducted a field experiment to study how soils directly affect vegetation structure and composition and indirectly affect bird and butterfly assemblages using plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. The experimental design included four vegetation treatments of varying species richness replicated on three soil types. 3. Habitat characteristics varied widely among soils. Crops on sandy loam, the driest, most acidic soil with the lowest nutrient content, developed shorter, less dense vegetation with sparse litter accumulation and more bare ground compared to crops on loam and clay loam. 4. Birds and butterflies responded similarly to soil-induced variation in habitat characteristics. Their abundance and species richness were similar on all soils, but their assemblage compositions varied among soils in certain vegetation treatments. 5. In low-diversity grass crops, bird assemblages using sandy loam were dominated by species preferring open ground and sparse vegetation for foraging and nesting, whereas assemblages using loam and clay loam were dominated by birds preferring tall, dense vegetation with abundant litter. In high-diversity prairie crops, the species composition of forbs in bloom varied among soils and strongly influenced butterfly assemblages. 6. Synthesis and applications. Prairie agroenergy crops established with identical management practices developed variable habitat characteristics due to natural edaphic variation, and this heterogeneity influenced the spatial distribution of bird and butterfly assemblages due to differential habitat use among species. This finding suggests that if unfertilized prairie crops were grown for agroenergy by land managers large-scale, soil-induced habitat heterogeneity would promote wildlife diversity within and among fields, further increasing the habitat value of these crops compared to the fertilized, annual monocultures that currently dominate the agricultural landscape. Our study also highlights the need for managers to consider soil properties when selecting sites to restore grassland habitat for species of conservation concern. Soil, vegetation, bird, and butterfly dataData from Myers, M.C., J.T. Mason, B.J. Hoksch, C.A. Cambardella, J.D. Pfrimmer (2015) Birds and butterflies respond to soil-induced habitat heterogeneity in experimental plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. Journal of Applied Ecology. This Excel file includes separate sheets containing the soil, vegetation, and bird and butterfly assemblage data analyzed in the paper. Four-letter column labels for species abundances are derived from the first two letters of the genus and species names (e.g. Chondestes grammacus = "chgr"). Species lists are available from the online Supporting Information. Please contact Mark Myers (mark.myers@uni.edu) with inquiries.JournalAppliedEcology_Myersetal_DryadData.xlsx

Global transition towards renewable energy production has increased the demand for new and more flexible hydropower operations. Before management and stakeholders can make informed choices on potential mitigations, it is essential to understand how the hydropower reservoir ecosystems respond to water level regulation (WLR) impacts that are likely modified by the reservoirs' abiotic and biotic characteristics. Yet, most reservoir studies have been case-specific, which hampers large-scale planning, evaluation and mitigation actions across various reservoir ecosystems. Here, we investigated how the effect of the magnitude, frequency and duration of WLR on fish populations varies along environmental gradients. We used biomass, density, size, condition and maturation of brown trout (Salmo trutta L.) in Norwegian hydropower reservoirs as a measure of ecosystem response, and tested for interacting effects of WLR and lake morphometry, climatic conditions and fish community structure. Our results showed that environmental drivers modified the responses of brown trout populations to different WLR patterns. Specifically, brown trout biomass and density increased with WLR magnitude particularly in large and complex-shaped reservoirs, but the positive relationships were only evident in reservoirs with no other fish species. Moreover, increasing WLR frequency was associated with increased brown trout density but decreased condition of individuals within the populations. WLR duration had no significant impacts on brown trout, and the mean weight and maturation length of brown trout showed no significant response to any WLR metrics. Our study demonstrates that local environmental characteristics and the biotic community strongly modify the hydropower-induced WLR impacts on reservoir fishes and ecosystems, and that there are no one-size-fits-all solutions to mitigate environmental impacts. This knowledge is vital for sustainable planning, management and mitigation of hydropower operations that need to meet the increasing worldwide demand for both renewable energy and ecosystem services delivered by freshwaters. Data of environmental characteristics and brown trout populations in 102 Norwegian hydropower reservoirsThe data contains field-collected data of brown trout populations in 102 Norwegian reservoirs with variable environmental characteristics. The brown trout data (i.e. response variables) include estimates of: "Biomass" (grams of fish per 100m2 net per night); "Density" (number of fish per 100m2 net per night); "Mean weight" (mean wet mass in grams); "Mean condition" (mean Fulton's condition factor); and "Mean maturity length" (mean total length of mature females in millimeters). All abbreviations for different variables (columns) are explained in the paper. Many reservoirs ("Lake") have various names, some including Norwegian letters (æ, ø & å). Hence, we recommend to use coordinate data (EPSG:4326; "decimalLongitude" and "decimalLatitude") and Norwegian national lake ID numbers ("Lake_nr"; managed by the Norwegian Water Resources and Energy Directorate; www.nve.no) to locate the reservoirs. The variables "Year", "Month" and "Day" refer to times when survey fishing was conducted. Lake morphometry data ("A"=surface area, "SD"=shoreline development) is obtained from NVE database. The lake climatic and catchment data ("T"=mean July air temperature, "NDVI"= Normalized Difference Vegetation Index, and "SL"=terrain slope) is obtained and measured as described by Finstad et al. (2014; DOI: 10.1111/ele.12201). Other abbreviations include: "FC"=presence of other fish species (1=absent, 2=present); "GS"=gillnet series (1=Nordic, 2=Jensen); and "ST"=brown trout stocking (0=no stocking, 1=stocking). The water level regulation (WLR) metrics include: ): "WLR_magnitude"= maximum regulation amplitude; "WLR_frequency"=relative proportion of weeks with a sudden rise or drop in water level; and "WLR_duration"=the relative proportion of weeks with exceptionally low water levels.Data-in_doi.org-10.1016-j.scitotenv.2017.10.268.xlsx