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At least 1,243 records · Page 69Linked to original sources

USGS2025

Longevity, age-specific survival, and mean generation time of Rana muscosa: Implications for conservation of possibly the longest lived Ranid frog

Life history strategies vary widely among species and play a vital role in extinction risk, especially in a rapidly changing environment. For many taxa, information on life history such as longevity, lifespan, and generation time is incomplete. This is especially true for amphibians, which have experienced large-scale declines in recent decades. The mountain yellow-legged frog ( Rana muscosa ) is a California endemic recognized as a state and federally endangered species. We evaluated a 23-year dataset of six wild R. muscosa populations in southern California. We calculated the average lifespan of individuals in these six populations to be approximately 9.5 years, with a mean generation time of 7.4 years. We did not detect a difference in longevity between sexes or a difference in apparent survival across various ages of adults. We also documented the longest-lived ranid frog ever recorded from a wild population: a male R. muscosa that was at least 21 years old. Our results suggest a relatively long generation time for this species, a characteristic that may benefit them because reproduction is regularly challenged by drought, fire activity, and disease. This information is important for understanding the complex life history of this endangered ranid frog and can help guide efforts to manage and recover the species.

California↗

USGS2026

Using demographic estimation and power analysis to inform monitoring efforts and detect declines in freshwater mussels

Population assessments can be used to determine species' viability and inform conservation efforts; however, obtaining sufficient data quality and quantity can be challenging when monitoring resources are scarce. Brook Floater ( Alasmidonta varicosa ) is a freshwater mussel that is state listed in the United States as endangered or threatened in 10 of the 14 states it occupies. Despite the conservation concern, little is known about the size and demographics of most Brook Floater populations. The primary objective of this study was to determine demographic parameters (abundance and survival) for Brook Floater at five newly established, long-term monitoring sites in Maine and Massachusetts, United States. A secondary objective was to evaluate tradeoffs of potential future sampling strategies by state natural resource agencies at these monitoring sites. Mussels were sampled at each site using visual and tactile snorkel surveys twice each summer during 2018–2020 and marked with alphanumeric tags to distinguish individuals. We considered a candidate set of capture-recapture models that allowed parameters to vary depending on year, sampling occasion, and individual shell length. Abundance estimates among sites varied widely, from 29 individuals (range: 12–119) in the Nissitissit River in Massachusetts to 311 individuals (range: 307–320) in the East Branch of the Pleasant River in Maine. Annual survival was high across all sites (0.87–0.96). Given the differences in capture probability and abundance between states, we determined the statistical power available to detect given population declines in Massachusetts and explored alternative population abundance estimators in Maine. The power analysis suggested that the existing sampling regimen in Maine is likely adequate while sampling in Massachusetts may require additional occasions each year to realistically detect population declines with sufficient statistical power. This analysis demonstrates the utility of considering sampling frequency when designing capture-recapture studies for freshwater mussels and other taxa.

Maine, Massachusetts↗

USGS2021

Transcriptome assembly and differential gene expression of the invasive avian malaria parasite Plasmodium relictum in Hawaiʻi

The malaria parasite Plasmodium relictum (lineage GRW4) was introduced less than a century ago to the native avifauna of Hawaiʻi, where it has since caused major declines of endemic bird populations. One of the native bird species that is frequently infected with GRW4 is the Hawaiʻi ʻamakihi ( Chlorodrepanis virens ). To achieve a better understanding of the transcriptional activities of this virulent parasite, we performed a controlled challenge experiment of 15 ʻamakihi that were infected with GRW4. Blood samples containing malaria parasites were collected at two time points (intermediate and peak infection stages) from host individuals that were either experimentally infected by mosquitoes or inoculated with infected blood. We then used RNA sequencing to assemble a high‐quality blood transcriptome of P. relictum GRW4, allowing us to quantify parasite expression levels inside individual birds. We found few significant differences (one to two transcripts) in GRW4 expression levels between host infection stages and between inoculation methods. However, 36 transcripts showed differential expression levels among all host individuals, indicating a potential presence of host‐specific gene regulation across hosts. To reduce the extinction risk of the remaining native bird species in Hawaiʻi, genetic resources of the local Plasmodium lineage are needed to enable further molecular characterization of this parasite. Our newly built Hawaiian GRW4 transcriptome assembly, together with analyses of the parasite's transcriptional activities inside the blood of Hawaiʻi ʻamakihi, can provide us with important knowledge on how to combat this deadly avian disease in the future.

Hawaii↗

USGS2021

Linking behavioral states to landscape features for improved conservation management

A central theme for conservation is understanding how animals differentially use, and are affected by change in, the landscapes they inhabit. However, it has been challenging to develop conservation schemes for habitat-specific behaviors. Here we use behavioral change point analysis to identify behavioral states of golden eagles ( Aquila chrysaetos ) in the Sonoran and Mojave Deserts of the southwestern United States, and we identify, for each behavioral state, conservation-relevant habitat associations. We modeled behavior using 186,859 GPS points from 48 eagles and identified 2,851 distinct segments comprising four behavioral states. Altitude above ground level (AGL) best differentiated behavioral states, with two clusters of short-distance movement behaviors characterized by low AGL (state 1 AGL = 14 m (median); state 2 AGL = 11 m) and two associated with longer-distance movement behaviors and characterized by higher AGL (state 3 AGL = 108 m; state 4 AGL = 450 m). Behaviors such as perching and low-altitude hunting were associated with short-distance movements in updraft-poor environments, at higher elevations, and over steeper and more north-facing terrain. In contrast, medium-distance movements such as hunting and transiting were over gentle and south-facing slopes. Long-distance transiting occurred over the desert habitats that generate the best updraft. This information can guide management of this species, and our approach provides a template for behavior-specific habitat associations for other species of management concern.

California↗

USGS2021

Survival and abundance of polar bears in Alaska’s Beaufort Sea, 2001–2016

The Arctic Ocean is undergoing rapid transformation toward a seasonally ice-free ecosystem. As ice-adapted apex predators, polar bears ( Ursus maritimus ) are challenged to cope with ongoing habitat degradation and changes in their prey base driven by food-web response to climate warming. Knowledge of polar bear response to environmental change is necessary to understand ecosystem dynamics and inform conservation decisions. In the southern Beaufort Sea (SBS) of Alaska and western Canada, sea ice extent has declined since satellite observations began in 1979 and available evidence suggests that the carrying capacity of the SBS for polar bears has trended lower for nearly two decades. In this study, we investigated the population dynamics of polar bears in Alaska's SBS from 2001 to 2016 using a multistate Cormack–Jolly–Seber mark–recapture model. States were defined as geographic regions, and we used location data from mark–recapture observations and satellite-telemetered bears to model transitions between states and thereby explain heterogeneity in recapture probabilities. Our results corroborate prior findings that the SBS subpopulation experienced low survival from 2003 to 2006. Survival improved modestly from 2006 to 2008 and afterward rebounded to comparatively high levels for the remainder of the study, except in 2012. Abundance moved in concert with survival throughout the study period, declining substantially from 2003 and 2006 and afterward fluctuating with lower variation around an average of 565 bears (95% Bayesian credible interval [340, 920]) through 2015. Even though abundance was comparatively stable and without sustained trend from 2006 to 2015, polar bears in the Alaska SBS were less abundant over that period than at any time since passage of the U.S. Marine Mammal Protection Act. The potential for recovery is likely limited by the degree of habitat degradation the subpopulation has experienced, and future reductions in carrying capacity are expected given current projections for continued climate warming.

Alaska↗

USGS2022

Classifying behavior from short-interval biologging data: An example with GPS tracking of birds

Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets. We apply a framework for using K -means clustering to classify bird behavior using points from short time interval GPS tracks. K -means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K -means clustering to six focal variables derived from GPS data collected at 1–11 s intervals from free-flying bald eagles ( Haliaeetus leucocephalus ) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior. After filtering for data quality, the K -means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight. The K -means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.

Ecology and Evolution↗

USGS2022

Assessing vegetation recovery from energy development using a dynamic reference approach

Ecologically relevant references are useful for evaluating ecosystem recovery, but references that are temporally static may be less useful when environmental conditions and disturbances are spatially and temporally heterogeneous. This challenge is particularly acute for ecosystems dominated by sagebrush ( Artemisia spp.), where communities may require decades to recover from disturbance. We demonstrated application of a dynamic reference approach to studying sagebrush recovery using three decades of sagebrush cover estimates from remote sensing (1985–2018). We modelled recovery on former oil and gas well pads ( n = 1200) across southwestern Wyoming, USA, relative to paired references identified by the Disturbance Automated Reference Toolset. We also used quantile regression to account for unmodelled heterogeneity in recovery, and projected recovery from similar disturbance across the landscape. Responses to weather and site-level factors often differed among quantiles, and sagebrush recovery on former well pads increased more when paired reference sites had greater sagebrush cover. Little (<5%) of the landscape was projected to recover within 100 years for low to mid quantiles, and recovery often occurred at higher elevations with cool and moist annual conditions. Conversely, 48%–78% of the landscape recovered quickly (within 25 years) for high quantiles of sagebrush cover. Our study demonstrates advantages of using dynamic reference sites when studying vegetation recovery, as well as how additional inferences obtained from quantile regression can inform management.

Wyoming↗

USGS2022

Integrating growth and survival models for flexible estimation of size-dependent survival in a cryptic, endangered snake

Estimates of demographic rates for animal populations and individuals have many applications for ecological and conservation research. In many animals, survival is size-dependent, but estimating the form of the size–survival relationship presents challenges. For elusive species with low recapture rates, individuals’ size will be unknown at many points in time. Integrating growth and capture–mark–recapture models in a Bayesian framework empowers researchers to impute missing size data, with uncertainty, and include size as a covariate of survival, capture probability, and presence on-site. If there is no theoretical expectation for the shape of the size–survival relationship, spline functions can allow for fitting flexible, data-driven estimates. We use long-term capture–mark–recapture data from the endangered San Francisco gartersnake ( Thamnophis sirtalis tetrataenia ) to fit an integrated growth–survival model. Growth models showed that females reach longer asymptotic lengths than males and that the magnitude of sexual size dimorphism differed among populations. The capture probability and availability of San Francisco gartersnakes for capture increased with snout–vent length. The survival rate of female snakes exhibits a nonlinear relationship with snout–vent length (SVL), with survival flat between 300 mm and 550 mm SVL before decreasing for females between 550 mm and 700 mm SVL. For male snakes, survival decreased for adult males >550 mm SVL. The survival rates of the smallest and largest San Francisco gartersnakes were highly uncertain because recapture rates were very low for these sizes. By integrating growth and survival models and using penalized splines, we found support for size-dependent survival in San Francisco gartersnakes. Our results have applications for devising management activities for this endangered subspecies, and our methods could be applied broadly to the study of size-dependent demography among animals.

California↗

USGS2022

Partial observability and management of ecological systems

The actual state of ecological systems is rarely known with certainty, but management actions must often be taken regardless of imperfect measurement (partial observability). Because of the difficulties in accounting for partial observability, it is usually treated in an ad hoc fashion, or simply ignored altogether. Yet incorporating partial observability into decision processes lends a realism that has the potential to improve ecological outcomes significantly. We review frameworks for dealing with partial observability, focusing specifically on dynamic ecological systems with Markovian transitions, i.e., transitions among system states that are influenced by the current system state and management action over time. Fully observable states are represented in an observable Markov decision process (MDP), whereas obscure or hidden states are represented in a partially observable process (POMDP). POMDPs can be seen as a natural extension of observable MDPs. Management under partial observability generalizes the situation for complete observability, by recognizing uncertainty about the system's state and incorporating sequential observations associated with, but not the same as, the states themselves. Decisions that otherwise would depend on the actual state must be based instead on state probability distributions (“belief states”). Partial observability requires adaptation of the entire decision process, including the use of belief states and Bayesian updates, valuation that includes expectations over observations, and optimal strategy that identifies actions for belief states over a continuous belief space. We compare MDPs and POMDPs and highlight POMDP applications to some common ecological problems. We clarify the structure and operations, approaches for finding solutions, and analytic challenges of POMDPs for practicing ecologists. Both observable and partially observable MDPs can use an inductive approach to identify optimal strategies and values, with a considerable increase in mathematical complexity with POMDPs. Better understanding of POMDPs can help decision makers manage imperfectly measured ecological systems more effectively.

Ecology and Evolution↗

USGS2022

Pleistocene–Holocene vicariance, not Anthropocene landscape change, explains the genetic structure of American black bear (Ursus americanus) populations in the American Southwest and northern Mexico

The phylogeography of the American black bear ( Ursus americanus ) is characterized by isolation into glacial refugia, followed by population expansion and genetic admixture. Anthropogenic activities, including overharvest, habitat loss, and transportation infrastructure, have also influenced their landscape genetic structure. We describe the genetic structure of the American black bear in the American Southwest and northern Mexico and investigate how prehistoric and contemporary forces shaped genetic structure and influenced gene flow. Using a suite of microsatellites and a sample of 550 bears, we identified 14 subpopulations organized hierarchically following the distribution of ecoregions and mountain ranges containing black bear habitat. The pattern of subdivision we observed is more likely a product of postglacial habitat fragmentation during the Pleistocene and Holocene, rather than a consequence of contemporary anthropogenic barriers to movement during the Anthropocene. We used linear mixed-effects models to quantify the relationship between landscape resistance and genetic distance among individuals, which indicated that both isolation by resistance and geographic distance govern gene flow. Gene flow was highest among subpopulations occupying large tracts of contiguous habitat, was reduced among subpopulations in the Madrean Sky Island Archipelago, where montane habitat exists within a lowland matrix of arid lands, and was essentially nonexistent between two isolated subpopulations. We found significant asymmetric gene flow supporting the hypothesis that bears expanded northward from a Pleistocene refugium located in the American Southwest and northern Mexico and that major highways were not yet affecting gene flow. The potential vulnerability of the species to climate change, transportation infrastructure, and the US–Mexico border wall highlights conservation challenges and opportunities for binational collaboration.

Arizona, New Mexico, Utah, Wyoming↗

USGS2022

Divergent responses of butterflies and bees to burning and grazing management in tallgrass prairies

Butterflies and bees contribute significantly to grassland biodiversity and play important roles as pollinators and herbivores. Grassland conservation and management must be seen through the lens of insect conservation and management if these species are to thrive. In North America, grasslands are a product of climate and natural disturbances such as fire and grazing. These natural disturbances have changed considerably since European colonization and subsequent landscape fragmentation. The aim of this study was to better understand the impacts of fire and grazing management on butterfly and bee communities in tallgrass prairie, enabling land managers and conservationists to better protect and manage remnant prairie. We examined butterfly and bee abundance, species richness, and diversity in Minnesota tallgrass prairies managed by grazing or fire. In 2016 and 2017, we surveyed butterflies, bees, vegetation, and surrounding land use at 20 remnant prairies (10 burned and 10 grazed) with known management histories. Butterfly and bee abundance at our study sites were significantly negatively correlated. Butterfly abundance, but not species richness, was higher in burned than grazed prairies, and prairie-associated grass-feeding butterflies were more abundant at sites with higher plant species richness. Bee abundance was unrelated to management type but was higher at sites with sandier soils; bee species richness was positively associated with forb frequency. These findings highlight the challenges of designing management plans tailored to wide groups of pollinators and the potential pitfalls of using one group of pollinators as indicators for another. They also point to the importance of a mosaic of management practices across the prairie landscape.

Minnesota↗

USGS2014

Validation of adipose lipid content as a body condition index for polar bears

Body condition is a key indicator of individual and population health. Yet, there is little consensus as to the most appropriate condition index (CI), and most of the currently used CIs have not been thoroughly validated and are logistically challenging. Adipose samples from large datasets of capture biopsied, remote biopsied, and harvested polar bears were used to validate adipose lipid content as a CI via tests of accuracy, precision, sensitivity, biopsy depth, and storage conditions and comparisons to established CIs, to measures of health and to demographic and ecological parameters. The lipid content analyses of even very small biopsy samples were highly accurate and precise, but results were influenced by tissue depth at which the sample was taken. Lipid content of capture biopsies and samples from harvested adult females was correlated with established CIs and/or conformed to expected biological variation and ecological changes. However, lipid content of remote biopsies was lower than capture biopsies and harvested samples, possibly due to lipid loss during dart retrieval. Lipid content CI is a biologically relevant, relatively inexpensive and rapidly assessed CI and can be determined routinely for individuals and populations in order to infer large-scale spatial and long-term temporal trends. As it is possible to collect samples during routine harvesting or remotely using biopsy darts, monitoring and assessment of body condition can be accomplished without capture and handling procedures or noninvasively, which are methods that are preferred by local communities. However, further work is needed to apply the method to remote biopsies.

Ecology and Evolution↗

USGS2019

Spatially-structured statistical network models for landscape genetics

A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape‐genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non‐binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape‐genetic data. We further explore a set of spatial‐statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape‐genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape‐genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.

Ecological Monographs↗

USGS2024

Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends

Estimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false-positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white-nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats ( Perimyotis subflavus ) and little brown bats ( Myotis lucifugus ), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population-wide declines probably have not occurred in big brown bats ( Eptesicus fuscus ), a species known to be less affected by WNS. This study provides the first abundance-based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false-negative and false-positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.

Ecological Monographs↗

USGS2024

An integrative paradigm for building causal knowledge

A core aspiration of the ecological sciences is to determine how systems work, which implies the challenge of developing a causal understanding. Causal inference has long been approached from a statistical perspective, which can be limited and restrictive for a variety of reasons. Ecologists and other natural scientists have historically pursued mechanistic knowledge as an alternative approach to causal understanding, though without explicit reference to the requirements of causal statistics. In this paper, I describe the premises of an expanded paradigm for causal studies, the Integrative Causal Investigation Paradigm, that subsumes causal statistics and mechanistic investigation into a multi-evidence approach. This paradigm is distinct from the one articulated by causal statistics in that it (1) focuses its attention on the long-term goal of building causal knowledge across multiple studies and (2) recognizes the essential role of mechanistic investigations in establishing a causal understanding. The Integrative Paradigm, consequentially, proposes that there are multiple methodological routes to building causal knowledge and thus represents a pluralistic perspective. This paper begins by describing the crux of the problem faced by causal statistics. To understand this problem, it should be recognized that the word causal has multiple meanings and a variety of evidential standards. An expanded vocabulary is developed so as to reduce ambiguities and clarify critical issues. I further show by example that there is an important ingredient typically omitted from consideration in causal statistics, which is the known information related to the mechanisms underlying relationships being evaluated. To address this issue, I describe a procedure, Causal Knowledge Analysis, that involves an evaluation and compilation of existing evidence indicative of causal content and the features of mechanisms. Causal Knowledge Analysis is applied to three example situations to illustrate the process and its potential for contributing to the development of causal knowledge. The implications of adopting the proposed paradigm and associated procedures are discussed and include the potential for advancing ecology, the potential for clarifying causal methodology, and the potential for contributing to predictive forecasting.

Ecological Monographs↗

USGS2015

Proximity to encroaching coconut palm limits native forest water use and persistence on a Pacific atoll

Competition for fresh water between native and introduced plants is one important challenge facing native forests as rainfall variability increases. Competition can be especially acute for vegetation on Pacific atolls, which depend upon consistent rainfall to replenish shallow groundwater stores. Patterns of sap flow, water use, and diameter growth of Pisonia grandis trees were investigated on Sand Islet, Palmyra Atoll, Line Islands, during a period of low rainfall. Sap flow in the outer sapwood was reduced by 53% for P . grandis trees growing within coconut palm ( Cocos nucifera ) stands ( n  = 9) versus away from coconut palm ( n  = 9). This suggested that water uptake was being limited by coconut palm. Radial patterns of sap flow into the sapwood of P . grandis also differed between stands with and without coconut palm, such that individual tree water use for P . grandis ranged from 14 to 67 L day −1 , averaging 47·8 L day −1 without coconut palm and 23·6 L day −1 with coconut palm. Diameter growth of P . grandis was measured from nine islets. In contrast to sap flow, competition with coconut palm increased diameter growth by 89%, equating to an individual tree basal area increment of 5·4 versus 10·3 mm 2  day −1 . Greater diameter growth countered by lower rates of water use by P . grandis trees growing in competition with coconut palm suggests that stem swell may be associated with water storage when positioned in the understory of coconut palm, and may facilitate survival when water becomes limiting until too much shading overwhelms P . grandis .

Palmyra Atoll↗

USGS2018

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

Ecohydrology combines empiricism, data analytics, and the integration of models to characterize linkages between ecological and hydrological processes. A challenge for practitioners is determining which models best generalizes heterogeneity in hydrological behaviour, including water fluxes across spatial and temporal scales, integrating environmental and socio‐economic activities to determine best watershed management practices and data requirements. We conducted a literature review and synthesis of hydrologic, hydraulic, water quality, and ecological models designed for solving interdisciplinary questions. We reviewed 1,275 papers and identified 178 models that have the capacity to answer an array of research questions about ecohydrology or ecohydraulics. Of these models, 43 were commonly applied due to their versatility, accessibility, user‐friendliness, and excellent user‐support. Forty‐one of 43 reviewed models were linked to at least 1 other model especially: Water Quality Analysis Simulation Program (linked to 21 other models), Soil and Water Assessment Tool (19), and Hydrologic Engineering Center's River Analysis System (15). However, model integration was still relatively infrequent. There was substantial variation in model applications, possibly an artefact of the regional focus of research questions, simplicity of use, quality of user‐support efforts, or a limited understanding of model applicability. Simply increasing the interoperability of model platforms, transformation of models to user‐friendly forms, increasing user‐support, defining the reliability and risk associated with model results, and increasing awareness of model applicability may promote increased use of models across subdisciplines. Nonetheless, the current availability of models allows an array of interdisciplinary questions to be addressed, and model choice relates to several factors including research objective, model complexity, ability to link to other models, and interface choice.

Ecohydrology↗

USGS2016

Discontinuities concentrate mobile predators: Quantifying organism-environment interactions at a seascape scale

Understanding environmental drivers of spatial patterns is an enduring ecological problem that is critical for effective biological conservation. Discontinuities (ecologically meaningful habitat breaks), both naturally occurring (e.g., river confluence, forest edge, drop-off) and anthropogenic (e.g., dams, roads), can influence the distribution of highly mobile organisms that have land- or seascape scale ranges. A geomorphic discontinuity framework, expanded to include ecological patterns, provides a way to incorporate important but irregularly distributed physical features into organism–environment relationships. Here, we test if migratory striped bass ( Morone saxatilis ) are consistently concentrated by spatial discontinuities and why. We quantified the distribution of 50 acoustically tagged striped bass at 40 sites within Plum Island Estuary, Massachusetts during four-monthly surveys relative to four physical discontinuities (sandbar, confluence, channel network, drop-off), one continuous physical feature (depth variation), and a geographic location variable (region). Despite moving throughout the estuary, striped bass were consistently clustered in the middle geographic region at sites with high sandbar area, close to channel networks, adjacent to complex confluences, with intermediate levels of bottom unevenness, and medium sized drop-offs. In addition, the highest striped bass concentrations occurred at sites with the greatest additive physical heterogeneity (i.e., where multiple discontinuities co-occurred). The need to incorporate irregularly distributed features in organism–environment relationships will increase as high-quality telemetry and GIS data accumulate for mobile organisms. The spatially explicit approach we used to address this challenge can aid both researchers who seek to understand the impact of predators on ecosystems and resource managers who require new approaches for biological conservation.

Ecosphere↗