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At least 1,297 records · Page 72Linked to original sources
USGS2024

Multiple stressors mediate the effects of warming on leaf decomposition in a large regulated river

Predicting how increasing temperatures interact with other global change drivers to influence the structure and dynamics of Earth's ecosystems is a primary challenge in ecology. Our study made use of multiple simultaneous “natural experiments” to examine how rapid warming, declining nutrients, invasive consumers, and riparian invasive species management interact to influence leaf decomposition in a large and regulated river. Specifically, we compared the breakdown of cottonwood ( Populus fremontii ), willow ( Salix exigua ), and saltcedar ( Tamarix sp.) leaf litter in 2022 to a previous experiment from 1998 that occurred under much cooler water temperatures, and had higher water phosphorus concentrations, low numbers of invasive New Zealand mudsnails ( Potamopyrgus antipodarum ), and unaltered litter chemistry from the herbivory of saltcedar leaf beetles ( Diorhabda carinulata ). We found that the effects of up to 10°C warmer temperatures on leaf decomposition were mediated by the establishment and management of invasive species and declining water nutrient concentrations arising from upstream reservoir lowering. Such interactions led to accelerated breakdown of saltcedar, but relatively minor effects of warming on the rate of cottonwood and willow decomposition. Additionally, our results demonstrate the potential for favorable invasive species management outcomes in the terrestrial environment to produce unintended responses in adjacent freshwater ecosystems. As temperatures continue to rise, it is critical that future studies consider how warming interacts with multiple stressors and environmental factors to influence processes such as decomposition in freshwater ecosystems.

Arizona↗
USGS2024

Phenology forecasting models for detection and management of invasive annual grasses

Non-native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well-timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range-wide phenology forecasts for two problematic invasive annual grasses: cheatgrass ( Bromus tectorum ), and red brome ( Bromus rubens ). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat-insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within-season or within-year decision support.

Ecosphere↗
USGS2024

Partly cloudy with a chance of mosquitoes: Developing a flexible approach to forecasting mosquito populations

Climate-induced shifts in mosquito phenology and population structure have important implications for the health of humans and wildlife. The timing and intensity of mosquito interactions with infected and susceptible hosts are a primary determinant of vector-borne disease dynamics. Like most ectotherms, rates of mosquito development and corresponding phenological patterns are expected to change under shifting climates. However, developing accurate forecast of mosquito phenology under climate change that can be used to inform management programs remains challenging despite an abundance of available data. As climate change will have variable effects on mosquito demography and phenology across species it is vital that we identify associated traits which may explain the observed variation. Here, we review a suite of modeling approaches that could be applied to generate forecasts of mosquito activity under climate change and evaluate the strengths and weaknesses of the different approaches. We describe four primary life-history and physiological traits that can be used to constrain models and demonstrate how this prior information can be harnessed to develop a more general understanding of how mosquito activity will shift under changing climates. Combining a trait-based approach with appropriate modeling techniques can allow for the development of actionable, flexible, and multi-scale forecasts of mosquito population dynamics and phenology for diverse stakeholders.

Michigan, Wisconsin↗
USGS2025

Spatiotemporal interactions facilitate sympatry in a diverse mammalian community

Understanding mechanisms underlying coexistence among potential competitors, and between predators and prey, is a persistent challenge in community ecology. Using 6 years (2013–2018) of camera-trapping data and species interaction models, we investigated the spatiotemporal patterns of inter- and intra-guild interspecific interactions in a diverse terrestrial mammalian community in Pakke Wildlife Sanctuary and Tiger Reserve (PTR), Northeast India. We found no evidence of spatial interaction among apex predators (tiger Panthera tigris , leopard Panthera pardus , and dhole Cuon alpinus ). However, dholes temporally separated themselves from tigers and leopards. Among small carnivores, marbled cat ( Pardofelis marmorata ) and leopard cat ( Prionailurus bengalensis ) exhibited temporal separation, whereas leopard cat overlapped spatially and temporally with other small carnivores. Herbivores exhibited neither spatial nor temporal separation with each other. All apex predators exhibited diel activity and space-use patterns to overlap with their preferred prey. Our results suggest that the assembly of the diverse mammalian community of PTR is a complex process, and coexistence among potential competitors, and predators and prey is likely facilitated by several mechanisms including spatial and temporal segregation, and potentially dietary separation.

Arunachal Pradesh↗
USGS2024

A comparative framework to develop transferable species distribution models for animal telemetry data

Species distribution models (SDMs) have become increasingly popular for making ecological inferences, as well as predictions to inform conservation and management. In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. These modeling decisions may limit the performance of SDMs beyond the study region or sampling period. Given the increasing desire to develop transferable SDMs, a robust framework is necessary that can account for known challenges of model transferability. Here, we propose a comparative framework to develop transferable SDMs, which was tested using satellite telemetry data from green turtles ( Chelonia mydas ). This framework is characterized by a set of steps comparing among different models based on (1) model algorithm (e.g., generalized linear model vs. Gaussian process regression) and formulation (e.g., correlative model vs. hybrid model), (2) spatial scale, and (3) accounting for life stage. SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. Independent validation datasets from Brazil and Qatar were used to assess model transferability. A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. The comparative framework presented here may be applied to a variety of species, ecological datasets (e.g., presence-only, presence-absence, mark-recapture), and modeling frameworks (e.g., resource selection functions, step selection functions, occupancy models) to generate transferable predictions of species–habitat associations. We expect that SDM predictions resulting from this comparative framework will be more informative management tools and may be used to more accurately assess climate change impacts on a wide array of taxa.

Ecosphere↗
USGS2025

Ecological thresholds and transformations due to climate change: The role of abiotic stress

An ecological threshold is the point at which a comparatively small environmental change triggers an abrupt and disproportionately large ecological response. In the face of accelerating climate change, there is concern that abrupt ecosystem transformations will become more widespread as critical ecological thresholds are crossed. There has been ongoing debate, however, regarding the prevalence of ecological thresholds across the natural world. While ecological thresholds are ubiquitous in some ecosystems, thresholds have been difficult to detect in others. Some studies have even concluded that threshold responses are uncommon in the natural world and overly emphasized in the ecological literature. As ecologists who work in ecosystems chronically exposed to high abiotic stress, we consider ecological thresholds and ecosystem transformations to be critical concepts that can greatly advance understanding of ecological responses to climate change and inform ecosystem management. But quantifying ecological thresholds can be challenging, if not impossible, without data that are strategically collected for that purpose. Here, we present a conceptual framework built upon linkages between abiotic stress, climate-driven ecological threshold responses, and the risk of ecosystem transformation. We also present a simple approach for quantifying ecological thresholds across abiotic stress gradients. We hypothesize that climate-driven threshold responses are especially influential in ecosystems chronically exposed to high abiotic stress, where autotroph diversity is low and foundation species play a prominent ecological role. Abiotic conditions in these environments are often near physiological tolerance limits of foundation species, which means that small abiotic changes can trigger landscape-level ecological transformations. Conversely, the alleviation of stress near thresholds can allow foundation species to thrive and spread into previously inhospitable locations. We provide examples of this climate-driven threshold behavior from four high-stress environments: coastal wetlands, coral reefs, drylands, and alpine ecosystems. Our overarching aim in this review is to clarify the strong relationships between abiotic stress, climate-driven ecological thresholds, and the risk of ecosystem transformation under climate change.

Ecosphere↗
USGS2025

Dynamic environments generate geographic fluctuations in population structure of an inland shorebird

Species distributions depend on fine-scale ecological processes and population growth trajectories and are influenced by climate and weather changes. However, the characterization of inter-population dynamics underlying the geographic distributions of migratory organisms remains challenging. We adopted a stable isotope approach to investigate the dynamic population geography of a terrestrial migratory bird across multiple generations. We found that the age-specific geographic source of Mountain Plovers sampled during winter shifted over four years across a latitudinal gradient. Moreover, our results show that differential effects of climate on the probability of occurrence at the wintering ground could be a driver of population turnover in a migratory species adapted to extreme environmental stochasticity (i.e., drought occurrence). We propose a framework for the identification of spatial and temporal climate and weather components and respective effects on population composition and recruitment into migratory wintering populations. Our approach is useful to reveal population compositional shifts through hydrogen stable isotope analysis while accounting for cumulative drought effects.

Ecosphere↗
USGS2025

Assessing the combined influence of biotic and anthropogenic stressors on polar bears to inform conservation planning

Developing conservation strategies for species vulnerable to the effects of climate change, like polar bears ( Ursus maritimus ), can be challenging given the uncertainty of future environmental conditions. Effective conservation planning requires identifying and ranking threats to the persistence of polar bears throughout their circumpolar range and then assessing the ability of mitigative actions to aid in meeting plan objectives. We used a Bayesian network model to (1) characterize the relative importance of multiple biotic and anthropogenic stressors on four ecoregional polar bear populations, at two future decadal time periods, and based on two Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios (Shared Socioeconomic Pathways [SSPs] 2.6 [low] and 8.5 [high]); and (2) identify achievable management actions that may enhance the prospects of long-term persistence. Normative model runs indicated that populations in all four ecoregions incurred increasing probabilities of being decreased or greatly decreased over time. The probabilities of polar bear populations being decreased or greatly decreased from mid- to end of the century ranged from ~55% to 87% for the SSP 2.6 emissions scenario, and 82% to 94% for the SSP 8.5 emissions scenario among ecoregions. Arctic sea ice conditions and marine prey availability had the greatest influences on future polar bear population outcomes and overrode any relative influence from all other stressors. Hunting mortality was the most influential individual anthropogenic stressor in the Archipelago and Seasonal Ice Ecoregions, whereas terrestrial refugia quality grouped with various anthropogenic activities or factors (e.g., resource extraction, oil spill) was most influential for the Polar Basin Divergent Ice and Polar Basin Convergent Ice ecoregions. Our findings indicate that near-term proactive management of multiple anthropogenic stressors could cumulatively reduce the decline in populations such that if future sea ice habitat loss is eventually curtailed, population abundance would be greater than it would have been otherwise. Additionally, our findings suggest that there is value in tailoring management actions to address ecoregion-specific threats, which may prove useful in informing the development of future circumpolar conservation plans.

Ecosphere↗
USGS2025

DNA metabarcoding and video camera collars yield different inferences about the summer diet of an arctic ungulate

The diets of wild ungulates are a foundational component of their ecology, influencing their behavior, body condition, and demography. With changing environmental conditions, there is a significant need to identify important forage items for ungulates, but this has often proved challenging. Declines in several barren-ground caribou herds across the North American Arctic have raised concerns about the influence of climate change on caribou forage conditions. Shifts in plant phenology, biomass, quality, and composition may be influencing caribou diets and subsequently affecting their body condition and demographic rates. Although forage is a primary driver of barren-ground caribou behavior and population dynamics, there is limited recent information about the specific foods they consume, and uncertainty about appropriate methods for identifying those foods. Investigators are increasingly using fecal DNA metabarcoding and video camera collars to assess ungulate diets, but comparative studies of these approaches are lacking. To examine the summer diets of barren-ground caribou, we used both fecal metabarcoding and video camera collars to identify forage used by the Porcupine caribou herd, which spans the Alaska–Yukon border. In 2021, we sampled the diets of adult females by collecting fecal samples and observing collar videos during 4 sampling occasions. We found that caribou consumed very specific forage items, and those items varied markedly across the growing season. Caribou predominantly consumed graminoids and lichens during early summer, and shrubs and forbs later in the season. Metabarcoding and video data provided significantly different estimates of diet for all taxonomic levels we evaluated, and inferences from the two approaches were often disparate. Metabarcoding failed to detect some items frequently consumed in videos, such as lichens, and indicated high use of other items rarely consumed, such as mosses. We found that video data provided greater taxonomic diversity and resolution for vascular plants and lichens, and more closely aligned with past research and biological expectations than data from fecal metabarcoding. Additional research is needed to be able to use these methods to identify the biomass of different forage items consumed.

Alaska, Yukon↗
USGS2025

Similar population dynamics before and after a chytridiomycosis outbreak in a tropical riparian amphibian species

Emerging infectious diseases can cause rapid, widespread host mortality, and the lack of demographic data before and after pathogen emergence complicates understanding mechanisms of host persistence. This challenge is further compounded by environmental conditions that influence host behavior, while driving pathogen growth and virulence. These interactions create complex disease outcomes that hinder predictions of when and how hosts endure pathogen outbreaks. Here, we analyzed 10 years of capture-mark-recapture data (2000–2014) spanning wet and dry seasons for male Espadarana prosoblepon in El Copé, Panama, encompassing a period before (2000–2004) and after (2010–2014) a Batrachochytrium dendrobatidis ( Bd ) outbreak using Jolly-Seber models. We found that post- Bd male E. prosoblepon population size (range in mean population size among primary periods = 136–225 individuals) was similar to pre- Bd population size (range in mean population size among primary periods = 201–242 individuals). Pre- Bd , average monthly survival probability in the wet season was 0.93 (95% credible interval [CI] = 0.90–0.96). Post- Bd , uninfected individuals had survival probability higher in the wet season (mean = 0.97; [95% CI = 0.95–0.98]) than the dry season (mean = 0.90 [95% CI = 0.84–0.94]), while survival probability for infected individuals decreased as a function of Bd infection intensity. Pre- Bd , mean monthly per-capita entry probability was 0.07 (95% CI = 0.05–0.10), and post- Bd , mean monthly per-capita entry probability was 0.06 (95% CI = 0.00–0.10). Lastly, infection probability during the wet season was lower (mean = 0.04 [95% CI = 0.03–0.05]) than the dry season (mean = 0.10 [95% CI = 0.05–0.15]), and recovery probability during the wet season was lower (mean = 0.19 [95% CI = 0.11–0.28]) than the dry season (mean = 0.54 [95% CI = 0.20–0.88]). Our findings suggest that survival probabilities of uninfected individuals, as well as per-capita entry probabilities, are similar pre- and post- Bd , leading to a stable and similar sized pre- Bd population. These results contribute to understanding disease dynamics and tropical amphibian ecology.

Ecosphere↗
USGS2025

Some of these are not like the others: Relative thermal sensitivity among anuran species of the Southeast United States

Estimating how close a species is to its upper thermal limits (i.e., warming tolerance, a thermal sensitivity index) and how that proximity changes across space enables spatially explicit identification of species with increased extinction risk as temperatures increase. Yet, thermal sensitivity is often difficult to calculate because it is the result of many traits. We aimed to synthesize multiple traits into a single estimate of relative terrestrial thermal sensitivity for 13 anuran species in the southeastern United States. We employed models that incorporate traits and microclimate variation to (1) estimate species warming tolerance (the difference between species critical thermal maximum and modeled operative temperature, an estimate of body temperature) and (2) investigate how warming tolerance varied with latitude (whereby latitude represents different temperature regimes and external drivers of thermal sensitivity). We ran mechanistic niche models across a 12° latitudinal gradient and 10 years to estimate individual operative temperature. We calculated the minimum, 25th percentile (hottest quarter), and median daily minimum warming tolerance. Estimates of minimum warming tolerance spanned −5 to 10°C ( Lithobates palustris and Gastrophryne carolinensis respectively) and differed among species. For most species, modeled operative temperatures exceeded species' critical thermal maximum during extreme warm temperatures (i.e., heat waves) in part of their range, and warming tolerance increased with latitude. During heat waves, five species had lower warming tolerance at higher latitudes, and three species' warming tolerance did not change with latitude. We identified species that are approaching their thermal limits in the Southeast and characterized spatial patterns of warming tolerance. Increased temperatures could increase anuran extinction risk, posing an additional challenge for threatened anuran species. Spatial patterns of warming tolerance were not consistent among species in our study, highlighting that patterns identified at higher taxonomic categories could be inconsistent at lower taxonomic categories.

southeastern United States↗
USGS2018

Inferring species interactions through joint mark–recapture analysis

Introduced species are frequently implicated in declines of native species. In many cases, however, evidence linking introduced species to native declines is weak. Failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. For many species, mark–recapture analysis is the more rigorous form of demographic analysis. However, to our knowledge, there are no mark–recapture models that allow for joint modeling of interacting species. Here, we introduce a two‐species mark–recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. We use a simulation study to explore bias and choose an approach to model selection. We then use the model to investigate species interactions between endangered humpback chub ( Gila cypha ) and introduced rainbow trout ( Oncorhynchus mykiss ) in the Colorado River between 2009 and 2016. In particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. We also project the long‐term effects of different rainbow trout abundances on adult humpback chub abundances. Our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. When the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. Effects on interspecific interactions on survival and capture probability were strongly supported, whereas support for the growth effect was weaker. Environmental factors were also identified to be important and in many cases stronger than interspecific interactions, and there was still substantial unexplained variation in growth and survival rates. The general approach presented here for combining mark–recapture data for two species is applicable in many other systems and could be modified to model abundance of the invader via other modeling approaches.

Ecology↗
USGS2019

Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data

Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor provide parameters for predictive models. State space models (SSMs) applied to time series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in a SSM framework to seasonal time series (1994-2012) using a recently developed, maximum likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g. nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the observed B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore provides evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios such as climate change.

Ecology↗
USGS2020

The seasonal energetic landscape of an apex marine carnivore, the polar bear

Divergent movement strategies have enabled wildlife populations to adapt to environmental change. In recent decades, the Southern Beaufort Sea subpopulation of polar bears (Ursus maritimus) has developed a divergent movement strategy in response to diminishing sea ice where the majority of the subpopulation (73–85%) stays on the sea ice in summer and the remaining bears move to land. Although declines in sea ice are generally considered a challenge to energy balance in polar bears residing in some regions of the Arctic, little quantitative data exists concerning the seasonal energy expenditures of this apex marine carnivore. We used GPS satellite collars with tri-axial accelerometers and conductivity sensors to measure the location, behavior, and energy expenditure of five adult female polar bears in the southern Beaufort Sea across seasons of sea ice breakup and minimum extent. Using a Bayesian mixed-effects model, we found that energy expenditure was influenced by month, ocean depth, and habitat type (sea ice or land). Total energy expenditure from May to October ranged from 37.7 – 47.2 mJ kg-1 for individual bears. Bears that moved to land expended 7% more energy on average from May to October than bears that remained on the receding sea ice. In August, when bears were moving from the sea ice to land or moving north with the receding pack ice, bears that moved to land spent 7% more time swimming and expended 22% more energy. Meaning the immediate cost of moving to land exceeded the cost of remaining on the receding summer pack ice. These findings suggest a physiological reason why the majority of the Southern Beaufort Sea subpopulation continues to inhabit a diminishing summer ice platform. However, bears that moved to land spent 29% more time in preferred hunting habitats over the continental shelf than bears that remained on the sea ice. Bears on land also had access to subsistence-harvested bowhead whale carcasses. Hence, our findings indicate there may be a greater overall energetic benefit to move to land in this region, which suggests that the use of the diminishing summer sea ice may be functioning as an ecological trap.

Alaska↗
USGS2020

Scientist’s guide to developing explanatory statistical models using causal analysis principles

Recent discussions of model selection and multimodel inference highlight a general challenge for researchers, which is how to clearly convey the explanatory content of a hypothesized model or set of competing models. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create “well‐formed hypotheses”, as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that.

Ecology↗
USGS2021

An integrated path for spatial capture–recapture and animal movement modeling

Ecologists and conservation biologists increasingly rely on spatial capture–recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.

Ecology↗
USGS2021

Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had a validation R2 of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogenous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time, yet the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.

Ecology↗
USGS2022

Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie

Diet analysis integrates a wide variety of visual, chemical, and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, for example, highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package mvtweedie that extends capabilities of widely used packages in R ( mgcv and ggplot2 ) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted Puffins ( Fratercula cirrhata ) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980–2000) and then Pacific herring and capelin (2000–2020), while wolves ( Canis lupus ligoni ) in southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines.

Ecology↗