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USGS2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

Ecology and Evolution↗

USGS2008

Simulating the impact of cholinesterase-inhibiting pesticides on non-target wildlife in irrigated crops

We present a simulation model for risk assessment of the impact of insecticide inhibitors of cholinesterase (ChE) applied in irrigated agricultural fields on non-target wildlife. The model, which we developed as a compartment model based on difference equations (??t = 1 h), consists of six submodels describing the dynamics of (1) insecticide application, (2) insecticide movement into floodable soil, (3) irrigation and rain, (4) insecticide dissolution in water, (5) foraging and insecticide intake from water, and (6) ChE inhibition and recovery. To demonstrate application of the model, we simulated historical and "worst-case" scenarios of the impact of ChE-inhibiting insecticides on white-winged doves (Zenaida asiatica) inhabiting natural brushland adjacent to cotton and sugarcane fields in the Lower Rio Grande Valley of Texas, USA. Only when a rain event occurred just after insecticide application did predicted levels of ChE inhibition surpass the diagnostic level of 20% exposure. The present model should aid in assessing the effect of ChE-inhibiting insecticides on ChE activity of different species that drink contaminated water from irrigated agricultural fields, and in identifying specific situations in which the juxtaposition of environmental conditions and management schemes could result in a high risk to non-target wildlife. ?? 2007 Elsevier B.V. All rights reserved.

Ecological Modelling↗

USGS2022

Modeling risk dynamics of contaminants of emerging concern in a temperate-region wastewater effluent-dominated stream

Wastewater effluent-dominated streams are becoming increasingly common worldwide, including in temperate regions, with potential impacts on ecological systems and drinking water sources. We recently quantified the occurrence/spatiotemporal dynamics of pharmaceutical mixtures in a representative temperate-region wastewater effluent-dominated stream (Muddy Creek, Iowa) under baseflow conditions and characterized relevant fate processes. Herein, we quantified the ecological risk quotients (RQs) of 19 effluent-derived contaminants of emerging concern (CECs; including: 14 pharmaceuticals, 2 industrial chemicals, and 3 neonicotinoid insecticides) and 1 run-off-derived compound (atrazine) in the stream under baseflow conditions, and estimated the probabilistic risks of effluent-derived CECs under all-flow conditions ( i.e. , including runoff events) using stochastic risk modeling. We determined that 11 out of 20 CECs pose medium-to-high risks to local ecological systems ( i.e. , algae, invertebrates, fish) based on literature-derived acute effects under measured baseflow conditions. Stochastic risk modeling indicated decreased, but still problematic, risk of effluent-derived CECs ( i.e. , RQ ≥ 0.1) under all-flow conditions when runoff events were included. Dilution of effluent-derived chemicals from storm flows thus only minimally decreased risk to aquatic biota in the effluent-dominated stream. We also modeled in-stream transport. Thirteen out of 14 pharmaceuticals persisted along the stream reach (median attenuation rate constant k < 0.1 h −1 ) and entered the Iowa River at elevated concentrations. Predicted and measured concentrations in the drinking water treatment plant were below the human health benchmarks. This study demonstrates the application of probabilistic risk assessments for effluent-derived CECs in a representative effluent-dominated stream under variable flow conditions (when measurements are less practical) and provides an enhanced prediction tool transferable to other effluent-dominated systems.

Iowa↗

USGS2012

A review of the key genetic tools to assist imperiled species conservation: analyzing West Indian manatee populations

Managers faced with decisions on threatened and endangered wildlife populations often are lacking detailed information about the species of concern. Integration of genetic applications will provide management teams with a better ability to assess and monitor recovery efforts on imperiled species. The field of molecular biology continues to progress rapidly and many tools are currently available. Presently, little guidance is available to assist researchers and managers with the appropriate selection of genetic tools to study the status of wild manatee populations. We discuss several genetic tools currently employed in the application of conservation genetics, and address the utility of using these tools to determine population status to aid in conservation efforts. As an example, special emphasis is focused on the endangered West Indian manatee (Order Sirenia). All four extant species of sirenians are imperiled throughout their range, predominately due to anthropogenic sources; therefore, the need for genetic information on their population status is direly needed.

Journal of Marine Animals and Their Ecology↗

USGS2024

Editorial: From cold seeps to hydrothermal vents: Geology, chemistry, microbiology, and ecology in marine and coastal environments

This Research Topic compiles contemporary studies on cold seeps, hydrothermal vents, mud volcanoes, and related seafloor features that are associated with focused fluid emissions and the transfer of carbon, other chemical species, and sometimes heat from the geosphere to the ocean. Because these features sometimes tap fluids and gas originating kilometers below the seafloor, they provide an important window into deep processes that are otherwise inaccessible to scientists. At the shallow portion of their journey, migrating fluids nearing the seafloor contribute to a range of unique biological, physical, and chemical processes within the sediments themselves and at the sediment-water interface. Seafloor fluid emissions play a critical role in global biogeochemical cycles, ocean chemistry, and possibly even climate change. Seafloor leakage points often emit hydrocarbon gases (especially methane and CO 2 ) and are sometimes the loci for deposition of seafloor minerals that have economic value. A burgeoning area of research focuses on natural products generated at these features, seeking compounds with potential pharmaceutical or other applications. Multidisciplinary studies have become routine for characterization of seafloor fluid emission sites, attesting to the inseparability of geologic, physical, chemical, and biological processes in these settings. It is increasingly common for researchers to combine in a single research cruise: subbottom imaging and seafloor mapping; porewater and water column geochemistry and gas sampling; sediment retrieval for lithologic, biostratigraphic, and solid phase analyses; and studies of benthic and subseafloor communities at the microbial to macrofaunal scales. This multidisciplinary approach has the advantage of ensuring the spatial and temporal coincidence of surveys and samples, an important factor at highly dynamic seafloor fluid emission sites. In addition, researchers often use remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), or human-occupied vehicles (HOVs) to record video of the seafloor, compile photomosaics, collect targeted samples, and survey with high-resolution geophysical near-seafloor systems, providing a degree of detail about seafloor fluid emission sites that is unprecedented compared to most areas of the deep ocean. While rarer, long-term cabled observatories or shorter-term deployments of portable observatories are also used at some loci for seafloor fluid flux and are particularly helpful for capturing temporal variations at these dynamic features. Here we summarize the Research Topic’s contribution to multidisciplinary seafloor emission studies in the categories of cold seeps, mud volcanoes, and hydrothermal vents. Figure 1 shows the geographic distribution of the studies in this Research Topic and key features referred to in this Introduction.

Frontiers in Earth Science↗

USGS2010

A general science-based framework for dynamical spatio-temporal models

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.

Test↗

USGS2007

Vegetation classification, mapping, and monitoring at Voyageurs National Park, Minnesota: An application of the U.S. National Vegetation Classification

Question: How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring? Location: Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54 243 ha of terrestrial habitat in the sub-boreal region of North America. Methods: We classified and mapped the natural vegetation using the USNVC, with 'alliance' and 'association' as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types. Results and Discussion: Ordination and cluster analyses confirmed 49 associations and 42 alliances, with three associations ranked as globally vulnerable to extirpation. Ordination provided a useful summary of vegetation and ecological gradients. Overall map accuracy was 82.4%. Pinus banksiana - Picea mariana forests were less frequent in areas unburned since the 1930s. Conclusion: The USNVC provides a consistent ecological tool for summarizing and mapping vegetation. The products provide a baseline for assessing forests and wetlands, including fire management. The standardized classification and map units provide local to continental perspectives on park resources through linkages to state, provincial, and national classifications in the U.S. and Canada, and to NatureServe's Ecological Systems classification. IAVS; Opulus Press.

Minnesota↗

USGS2019

UAV-derived estimates of forest structure to inform ponderosa pine forest restoration

Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in density (e.g., basal area). Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon field‐based measurements, manned airborne or satellite remote sensing datasets. We used unmanned aerial vehicle (UAV ) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine‐ (<4 ha) and mid‐scales (4–400 ha) across a forest density gradient. We then used these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter were most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low‐ to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV ‐derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows.

Western United States↗

USGS2006

Changes in organic matter biodegradatility influencing sulfate reduction in an aquifer contaminated by landfill leachate

In situ experiments were conducted to measure sulfate reduction rates and identify rate-limiting factors in a shallow, alluvial aquifer contaminated with municipal landfill leachate. Single-well, push–pull tests conducted in a well adjacent to the landfill with >8 mM dissolved organic carbon (DOC) exhibited a sulfate reduction rate of 3.2 μmol SO 4 −2 (L sediment) −1 day −1 , a value in close agreement with laboratory-derived estimates. Identical tests conducted in wells located 90 m downgradient where DOC levels remained high (>3 mM) showed no detectable sulfate consumption, and laboratory assays confirmed this observation. However, the rates of sulfate reduction in sediment samples obtained from this site were three times larger when they were amended with filter-sterilized groundwater from the upgradient location. The effect of various amendments on sulfate reduction rates was further examined in laboratory incubations using sediment collected from the downgradient site amended with 35 S sulfate. Unamended sediments showed only weak conversion of the tracer to 35 S sulfide (5 to 7 cpm/cm 2 ), whereas the addition of Desulfovibrio cells increased 35 S sulfide production to 44 cpm/cm 2 . However, the application of heat-killed Desulfovibrio had a similar stimulatory effect, as did a lactate amendment. Collectively, these findings indicate that the lack of measurable sulfate reduction at the downgradient site was not due to the absence of the necessary metabolic potential, the presence of lower sulfate concentration, or the quantity of electron donor, but by its biodegradability. The findings also indicate that field bioaugmentation attempts should be interpreted with caution.

Microbial Ecology↗

USGS2021

Eagle fatalities are reduced by automated curtailment of wind turbines

Collision‐caused fatalities of animals at wind power facilities create a ‘green versus green’ conflict between wildlife conservation and renewable energy. These fatalities can be mitigated via informed curtailment whereby turbines are slowed or stopped when wildlife are considered at increased risk of collision. Automated monitoring systems could improve efficacy of informed curtailment, yet such technology is undertested. We test the efficacy of an automated curtailment system—a camera system that detects flying objects, classifies them and decides whether to curtail individual turbines to avoid potential collision—in reducing counts of fatalities of eagles, at Top of the World Windpower Facility (hereafter, the treatment site) in Wyoming, USA. We perform a before–after–control–impact study, comparing the number of eagle fatalities observed at the treatment site with those at a nearby (15 km) control site without automated curtailment, both before and after the implementation of automated curtailment at the treatment site. After correcting for carcass detection probability and scaling fatality estimates to turbine‐years, we estimate that the number of fatalities at the treatment site declined by 63% (95% CI = 59%–66%) between before and after periods while increasing at the control site by 113% (51%–218%). In total, there was an 82% (75%–89%) reduction in the fatality rate at the treatment site relative to the control site. Synthesis and applications . Automated curtailment of wind turbine operation substantially reduced eagle fatalities. This technology therefore has the potential to lessen the conflict between wind energy and raptor conservation. Although automated curtailment reduced fatalities, they were not fully eliminated. Therefore, automated curtailment, as implemented here, is not a panacea and its efficacy could be improved if considered in conjunction with other mitigation actions.

Wyoming↗

USGS2022

Epidemiological differences between sexes affect management efficacy in simulated chronic wasting disease systems

Sex-based differences in physiology, behaviour and demography commonly result in differences in disease prevalence. However, sex differences in prevalence may reflect exposure rather than transmission, which could affect disease control programmes. One potential example is chronic wasting disease (CWD), which has been observed at greater prevalence among male than female deer. We used an age- and sex-structured simulation model to explore harvest-based management of CWD under three different transmission scenarios that all generate higher male prevalence: (1) increased male susceptibility, (2) high male-to-male transmission or (3) high female-to-male transmission. Both female and male harvests were required to limit CWD epidemics across all transmission scenarios (approximated by R 0 ), though invasion was more likely under high female-to-male transmission. In simulations, heavily male-biased harvests controlled CWD epidemics and maintained large host populations under high male-to-male transmission and increased male susceptibility scenarios. However, male-biased harvests were ineffective under high female-to-male transmission. Instead, female-biased harvests were able to limit disease transmission under high female-to-male transmission but incurred a trade-off with smaller population sizes. Synthesis and applications . Higher disease prevalence in a sex or age group may be due to higher exposure or susceptibility but does not necessarily indicate if that group is responsible for more disease transmission. We showed that multiple processes can result in the pattern of higher male prevalence, but that population-level management interventions must focus on the sex responsible for disease transmission, not just those that are most exposed.

Journal of Applied Ecology↗

USGS2026

Spatially concentrating logging could mitigate climate-magnified fragmentation risks to a globally endangered bird

1. Rising timber demand is transforming forest structure globally, profoundly affecting biodiversity and climate resilience. Logging-driven fragmentation is potentially a major driver of biodiversity loss in production landscapes, yet its interactions with escalating climate stressors remain poorly understood. 2. We combine two decades of Landsat-derived habitat metrics with 29,000 surveys of the marbled murrelet ( Brachyramphus marmoratus )—an iconic Pacific Northwest old-forest specialist seabird affecting management of >10 million hectares. Controlling for habitat amount and detection probability, increasing landscape-scale forest edge amount sharply reduces murrelet occupancy, with impacts worsening under unfavourable climate-driven ocean conditions. 3. Comparing alternative landscape-scale timber harvest strategies, spatially concentrated logging consistently supports higher murrelet populations than fragmented approaches producing equivalent wood volumes, with benefits amplified under adverse ocean conditions. However, historical harvesting policies in the Pacific Northwest have instead driven severe habitat fragmentation, which we show is eroding the value of core set-aside forests on federal and conservation lands and ultimately rendering murrelets more vulnerable to climate change. 4. Synthesis and applications : We map key opportunities to boost populations by reducing edginess around remaining nesting habitat and investigate these opportunities' spatial distribution across land ownership and timber productivity gradients. Concentrating logging could be critical for mitigating fragmentation and climate threats for murrelets and potentially other forest-dependent species amid rising timber demand.

California, Oregon, Washington↗

USGS2019

Nonlinear reaction–diffusion process models improve inference for population dynamics

Partial differential equations (PDEs) are a useful tool for modeling spatiotemporal dynamics of ecological processes. However, as an ecological process evolves, we need statistical models that can adapt to changing dynamics as new data are collected. We developed a model that combines an ecological diffusion equation and logistic growth to characterize colonization processes of a population that establishes long-term equilibrium over a heterogeneous environment. We also developed a homogenization strategy to statistically upscale the PDE for faster computation and adopted a hierarchical framework to accommodate multiple data sources collected at different spatial scales. We highlighted the advantages of using a logistic reaction component instead of a Malthusian component when population growth demonstrates asymptotic behavior. As a case study, we demonstrated that our model improves spatiotemporal abundance forecasts of sea otters in Glacier Bay, Alaska. Furthermore, we predicted spatially varying local equilibrium abundances as a result of environmentally driven diffusion and density-regulated growth. Integrating equilibrium abundances over the study area in our application enabled us to infer the overall carrying capacity of sea otters in Glacier Bay, Alaska.

Alaska↗

USGS2011

Luring anglers to enhance fisheries

Current fisheries management is, unfortunately, reactive rather than proactive to changes in fishery characteristics. Furthermore, anglers do not act independently on waterbodies, and thus, fisheries are complex socio-ecological systems . Proactive management of these complex systems necessitates an approach—adaptive fisheries management—that allows learning to occur simultaneously with management. A promising area for implementation of adaptive fisheries management is the study of luring anglers to or from specific waterbodies to meet management goals. Purposeful manipulation of anglers, and its associated field of study, is nonexistent in past management. Evaluation of different management practices (i.e., hypotheses) through an iterative adaptive management process should include both a biological and sociological survey to address changes in fish populations and changes in angler satisfaction related to changes in management. We believe adaptive management is ideal for development and assessment of management strategies targeted at angler participation. Moreover these concepts and understandings should be applicable to other natural resource users such as hunters and hikers.

Journal of Environmental Management↗

USGS2021

Widespread use of the nitrification inhibitor nitrapyrin: Assessing benefits and costs to agriculture, ecosystems, and environmental health

Agricultural production and associated applications of nitrogen (N) fertilizers have increased dramatically in the last century, and current projections to 2050 show that demands will continue to increase as the human population grows. Applied in both organic and inorganic fertilizer forms, N is an essential nutrient in crop productivity. Increased fertilizer applications, however, create the potential for more N loss before plant uptake. One strategy for minimizing N loss is the use of enhanced efficiency fertilizers, fortified with a nitrification inhibitor, such as nitrapyrin. In soils and water, nitrapyrin inhibits the activity of ammonia monooxygenase, a microbial enzyme that catalyzes the first step of nitrification from ammonium to nitrite. Potential benefits of using nitrification inhibitors range from reduced nitrate leaching and nitrous oxide emissions to increased crop yield. The extent of these benefits, however, depends on environmental conditions and management practices. Thus, such benefits are not always realized. Additionally, nitrapyrin has been shown to transport off-field, and it is unknown what effects environmental nitrapyrin could have on nontarget organisms and the ecological nitrogen cycle. Here, we review the agronomic and environmental benefits and costs of nitrapyrin use and present a series of research questions and considerations to be addressed with future nitrification inhibitor research.

Environmental Science and Technology↗

USGS2023

Estimating groundwater pumping for irrigation: A method comparison

Effective groundwater management is critical to future environmental, ecological, and social sustainability and requires accurate estimates of groundwater withdrawals. Unfortunately, these estimates are not readily available in most areas due to physical, regulatory, and social challenges. Here, we compare four different approaches for estimating groundwater withdrawals for agricultural irrigation. We apply these methods in a groundwater-irrigated region in the state of Kansas, USA, where high-quality groundwater withdrawal data are available for evaluation. The four methods represent a broad spectrum of approaches: (1) the hydrologically-based Water Table Fluctuation method (WTFM); (2) the demand-based SALUS crop model; (3) estimates based on satellite-derived evapotranspiration (ET) data from OpenET; and (4) a landscape hydrology model which integrates hydrologic- and demand-based approaches. The applicability of each approach varies based on data availability, spatial and temporal resolution, and accuracy of predictions. In general, our results indicate that all approaches reasonably estimate groundwater withdrawals in our region, however, the type and amount of data required for accurate estimates and the computational requirements vary among approaches. For example, WTFM requires accurate groundwater levels, specific yield, and recharge data, whereas the SALUS crop model requires adequate information about crop type, land use, and weather. This variability highlights the difficulty in identifying what data, and how much, are necessary for a reasonable groundwater withdrawal estimate, and suggests that data availability should drive the choice of approach. Overall, our findings will help practitioners evaluate the strengths and weaknesses of different approaches and select the appropriate approach for their application.

Kansas↗

USGS1979

Approaches, field considerations and problems associated with radio tracking carnivores

The adaptation of radio tracking to ecological studies was a major technological advance affecting field investigations of animal movements and behavior. Carnivores have been the recipients of much attention with this new technology and study approaches have varied from simple to complex. Equipment performance has much improved over the years, but users still face many difficulties. The beginning of all radio tracking studies should be a precise definition of objectives. Study objectives dictate type of gear required and field procedures. Field conditions affect equipment performance and investigator ability to gather data. Radio tracking carnivores is demanding and generally requires greater time than anticipated. Problems should be expected and planned for in study design. Radio tracking can be an asset in carnivore studies but caution is needed in its application.

Book chapter↗

USGS2020

Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife

Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count‐based manner (e.g., number of dead animals) that is difficult to translate into rate‐based estimates important to infer population‐level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population‐level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count‐based field data on individuals to rate‐based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field‐based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor‐induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable.

Ecosphere↗