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At least 541 records · Page 30Linked to original sources
USGS2022

Industrial energy development decouples ungulate migration from the green wave

The ability to freely move across the landscape to track the emergence of nutritious spring green-up (termed ‘green-wave surfing’) is key to the foraging strategy of migratory ungulates. Across the vast landscapes traversed by many migratory herds, habitats are being altered by development with unknown consequences for surfing. Using a unique long-term tracking dataset, we found that when energy development occurs within mule deer ( Odocoileus hemionus ) migration corridors, migrating animals become decoupled from the green wave. During the early phases of a coalbed natural gas development, deer synchronized their movements with peak green-up. But faced with increasing disturbance as development expanded, deer altered their movements by holding up at the edge of the gas field and letting the green wave pass them by. Development often modified only a small portion of the migration corridor but had far-reaching effects on behaviour before and after migrating deer encountered it, thus reducing surfing along the entire route by 38.65% over the 14-year study period. Our study suggests that industrial development within migratory corridors can change the behaviour of migrating ungulates and diminish the benefits of migration. Such disruptions to migratory behaviour present a common mechanism whereby corridors become unprofitable and could ultimately be lost on highly developed landscapes.

Wyoming↗
USGS2025

Sea star wasting disease mystery finally solved

A decade after a marine epidemic killed off sea stars and triggered ecosystem-wide effects along the Pacific Coast of North America, researchers have identified the bacterial pathogen that is responsible for sea star wasting disease.

Nature Ecology and Evolution↗
USGS2026

Abiotic and biotic controls of non-native perennial plant success in drylands

Drivers of non-native plant success in drylands are poorly understood. Here we identify functional differences between dryland native and non-native perennial plants and assess how biotic, abiotic and anthropogenic factors shape the success of the latter. On the basis of plant community and functional trait data from 98 sites across 25 countries, we report a total of 41 non-native plant species at 31 sites. Non-natives tend towards faster growth strategies than natives. Non-native plant richness is higher at sites with greater grazing pressure and under environmental conditions associated with higher soil fertility, decomposition and fungal richness—conditions that tend to occur in less arid regions—and lower where native plant and herbivore richness are greater. Non-native plant cover correlates positively with grazing pressure and negatively with native plant richness. Taken together, our results suggest that non-native plant success in drylands is facilitated when high grazing pressure coincides with elevated resource availability. Such context-dependence of non-native plant success and linkages with native plant and herbivore diversity highlight the need for managing grazing and conserving biodiversity across the world’s drylands.

Nature Ecology and Evolution↗
USGS2026

Growth form and lifespan of herbaceous species mediate the role of traits in short-term drought response

Increased climate variability is expected to intensify short-term drought events. Plants have evolved stress tolerance strategies involving trade-offs in resource conservation, mycorrhizal collaboration and plant size, yet how these strategies promote drought resistance across different herbaceous plant groups remains unknown. Leveraging 63 globally distributed grassland and shrubland sites from the International Drought Experiment, we identified plant traits linked to drought resistance in 661 populations of 421 species after 1 year of extreme drought. We assessed how traits, site precipitation and drought severity affected cover change across growth forms and lifespans, and how trait–environment interactions influenced drought resistance. Across all species, leaf N (an acquisitive trait) was associated with drought resistance, whereas in forbs, drought resistance was also associated with a conservative root trait and plant size. In addition, interactions among traits mediated drought resistance; root traits predicted performance only in concert with other traits. Environmental variables influenced trait effects on drought resistance, notably for annuals in wetter sites, suggesting that drought-escape strategies in annuals may be advantageous only under mild stress. Our study highlights variability in traits that predict drought resistance across herbaceous plant groups, emphasizing the importance of species context, environmental stress and the selection of traits in research and management.

Nature Ecology and Evolution↗
USGS2010

Morning ambush attacks by black-footed ferrets on emerging prairie dogs

Black-footed ferrets ( Mustela nigripes ) often hunt at night, attacking normally diurnal prairie dogs ( Cynomys spp.) in underground burrow systems. While monitoring black-footed ferrets in South Dakota during morning daylight hours, we observed an adult female ferret ambush a black-tailed prairie dog ( C. ludovicianus ) emerging from a burrow. On a neighboring colony, we observed a second adult female ferret engaging in similar ambush behaviors on 12 occasions, although prey was not visible. We retrospectively assessed radio-telemetry data on white-tailed prairie dogs ( C. leucurus ) and a male and a female ferret to evaluate ferret activity in relation to timing of prairie dog emergence. Activity of radio-collared ferrets was high during the hourly period when prairie dogs first emerged and the following 2 hr, relative to later daylight hours. Such behavior is consistent with behaviors observed in South Dakota. Nighttime movements by ferrets might involve hunting but also reconnaissance of prey preparatory to morning ambush attacks.

South Dakota↗
USGS2016

Influence of species, size and relative abundance on the outcomes of competitive interactions between brook trout and juvenile coho salmon

Resource competition between animals is influenced by a number of factors including the species, size and relative abundance of competing individuals. Stream-dwelling animals often experience variably available food resources, and some employ territorial behaviors to increase their access to food. We investigated the factors that affect dominance between resident, non-native brook trout and recolonizing juvenile coho salmon in the Elwha River, WA, USA, to see if brook trout are likely to disrupt coho salmon recolonization via interference competition. During dyadic laboratory feeding trials, we hypothesized that fish size, not species, would determine which individuals consumed the most food items, and that species would have no effect. We found that species, not size, played a significant role in dominance; coho salmon won 95% of trials, even when only 52% the length of their brook trout competitors. As the pairs of competing fish spent more time together during a trial sequence, coho salmon began to consume more food, and brook trout began to lose more, suggesting that the results of early trials influenced fish performance later. In group trials, we hypothesized that group composition and species would not influence fish foraging success. In single species groups, coho salmon consumed more than brook trout, but the ranges overlapped. Brook trout consumption remained constant through all treatments, but coho salmon consumed more food in treatments with fewer coho salmon, suggesting that coho salmon experienced more intra- than inter-specific competition and that brook trout do not pose a substantial challenge. Based on our results, we think it is unlikely that competition from brook trout will disrupt Elwha River recolonization by coho salmon.

Washington↗
USGS2017

Using interviews and biological sign surveys to infer seasonal use of forested and agricultural portions of a human-dominated landscape by Asian elephants in Nepal

Understanding how wide-ranging animals use landscapes in which human use is highly heterogeneous is important for determining patterns of human–wildlife conflict and designing mitigation strategies. Here, we show how biological sign surveys in forested components of a human-dominated landscape can be combined with human interviews in agricultural portions of a landscape to provide a full picture of seasonal use of different landscape components by wide-ranging animals and resulting human–wildlife conflict. We selected Asian elephants ( Elephas maximus ) in Nepal to illustrate this approach. Asian elephants are threatened throughout their geographic range, and there are large gaps in our understanding of their landscape-scale habitat use. We identified all potential elephant habitat in Nepal and divided the potential habitat into sampling units based on a 10 km by 10 km grid. Forested areas within grids were surveyed for signs of elephant use, and local villagers were interviewed regarding elephant use of agricultural areas and instances of conflict. Data were analyzed using single-season and multi-season (dynamic) occupancy models. A single-season occupancy model applied to data from 139 partially or wholly forested grid cells estimated that 0.57 of grid cells were used by elephants. Dynamic occupancy models fit to data from interviews across 158 grid cells estimated that monthly use of non-forested, human-dominated areas over the preceding year varied between 0.43 and 0.82 with a minimum in February and maximum in October. Seasonal patterns of crop raiding by elephants coincided with monthly elephant use of human-dominated areas, and serious instances of human–wildlife conflict were common. Efforts to mitigate human–elephant conflict in Nepal are likely to be most effective if they are concentrated during August through December when elephant use of human-dominated landscapes and human–elephant conflict are most common.

Ethology Ecology and Evolution↗
USGS2021

First documentation of long-distance travel by a Florida manatee to the Mexican Caribbean

West Indian manatees ( Trichechus manatus ) are separated into two allopatric subspecies: the Florida manatee ( T. m. latirostris ) and the Antillean manatee ( T. m. manatus ). In the winter of 2020–2021, an adult manatee was sighted off the coast of Cancun, Quintana Roo, Mexico, in areas where Antillean manatees are not typically seen. The individual had distinct watercraft scars on its body, which were matched using photo-identification to a known male Florida manatee (PE424) that had been repeatedly photographed in Florida since 1998. This is the first record of a Florida manatee visiting the Mexican Caribbean. Previous reports of individuals from this subspecies in Cuba, combined with genetic evidence, suggest some level of connectivity among geographically separated manatee populations. We present the first evidence of along-distance movement by amanatee from Florida to the Yucatan Peninsula (Quintana Roo, Mexican Caribbean). This case, previous reports of Florida manatees in Cuba, and genetic evidence, suggest acertain degree of genetic mixture among the two subspecies.

Yucatan Peninsula↗
USGS1994

The effects of drought on population structure, activity, and orientation of toads Bufo quercicus and B. terrestris at a temporary pond

From 1985 through 1990, I monitored the populations of two species of toads, Bufo quercicus and B. terrestris , at a temporary pond in the xeric uplands of north-central Florida. A drift fence with pitfall traps completely encircled the pond basin; the fence was monitored 5 days per week throughout the year. The 5-year study coincided with a severe regional drought that resulted in generally short hydroperiods at unpredictable times of the year. More than 800 toads were captured. Successful metamorphosis never occurred at the pond although toads continued to visit it throughout the study. The sex ratio was male biased in B. quercicus but not in B. terrestris , although significant variation was observed from one year to the next. Likewise, the size-class structure and length-weight patterns varied among species, sexes, and years. Although fewer toads entered the pond basin as the study progressed, toads may have gone elsewhere to breed or they may have remained in refugia. Thus, decreased capture does not necessarily indicate that a drought-related population decline occurred. Drought may have disrupted normal arrival patterns and length of stay within the pond basin. Drought also could be responsible for variation in annual size-class structure of captured toads. The uncertainty of the hydroperiod both spatially and temporally in adjacent breeding sites, the ability of toads to move long distances with the potential for migration between breeding sites, and the lack of specificity in the choice of breeding sites (i.e. permanent versus different types of temporary wetlands) may lead to the formation of metapopulations in the xeric upland habitats of north-central Florida. Long-term monitoring under a variety of climatic conditions is needed to assess the effects of drought and other types of environmental stresses on toad populations.

Ethology Ecology and Evolution↗
USGS2012

Presence-only modeling using MAXENT: when can we trust the inferences?

1. Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence-only data and the deployment of these methods in user-friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence of random or representative sampling and that detectability during sampling does not vary with the covariates that determine occurrence probability. 2. Based on our interactions with researchers using these tools, we hypothesized that many presence-only studies were ignoring important assumptions of presence-only modelling. We tested this hypothesis by reviewing 108 articles published between 2008 and 2012 that used the MAXENT algorithm to analyse empirical (i.e. not simulated) data. We chose to focus on these articles because MAXENT has been the most popular algorithm in recent years for analysing presence-only data. 3. Many articles (87%) were based on data that were likely to suffer from sample selection bias; however, methods to control for sample selection bias were rarely used. In addition, many analyses (36%) discarded absence information by analysing presence–absence data in a presence-only framework, and few articles (14%) mentioned detection probability. We conclude that there are many misconceptions concerning the use of presence-only models, including the misunderstanding that MAXENT, and other presence-only methods, relieve users from the constraints of survey design. 4. In the process of our literature review, we became aware of other factors that raised concerns about the validity of study conclusions. In particular, we observed that 83% of articles studies focused exclusively on model output (i.e. maps) without providing readers with any means to critically examine modelled relationships and that MAXENT's logistic output was frequently (54% of articles) and incorrectly interpreted as occurrence probability. 5. We conclude with a series of recommendations foremost that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences can be made about clearly defined parameters such as occurrence probability.

Methods in Ecology and Evolution↗
USGS2013

Integrating resource selection information with spatial capture--recapture

1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection. 2. In this paper we develop the first statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models. 3. Our method leads to increases in precision for estimating parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of RSFs and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. 4. Finally, we find that SCR models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. Consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture–recapture methods.

Methods in Ecology and Evolution↗
USGS2013

Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data.

Methods in Ecology and Evolution↗
USGS2013

Advances and applications of occupancy models

Summary: The past decade has seen an explosion in the development and application of models aimed at estimating species occurrence and occupancy dynamics while accounting for possible non-detection or species misidentification. We discuss some recent occupancy estimation methods and the biological systems that motivated their development. Collectively, these models offer tremendous flexibility, but simultaneously place added demands on the investigator. Unlike many mark–recapture scenarios, investigators utilizing occupancy models have the ability, and responsibility, to define their sample units (i.e. sites), replicate sampling occasions, time period over which species occurrence is assumed to be static and even the criteria that constitute ‘detection’ of a target species. Subsequent biological inference and interpretation of model parameters depend on these definitions and the ability to meet model assumptions. We demonstrate the relevance of these definitions by highlighting applications from a single biological system (an amphibian–pathogen system) and discuss situations where the use of occupancy models has been criticized. Finally, we use these applications to suggest future research and model development.

Methods in Ecology and Evolution↗
USGS2013

Estimating true instead of apparent survival using spatial Cormack-Jolly-Seber models

Survival is often estimated from capture–recapture data using Cormack–Jolly–Seber (CJS) models, where mortality and emigration cannot be distinguished, and the estimated apparent survival probability is the product of the probabilities of true survival and of study area fidelity. Consequently, apparent survival is lower than true survival unless study area fidelity equals one. Underestimation of true survival from capture–recapture data is a main limitation of the method. We develop a spatial version of the CJS model that allows estimation of true survival. Besides the information about whether a specific individual was encountered at a given occasion, it is often recorded where the encounter occurred. Thus, information is available about the fraction of dispersal that occurs within the study area, and we use it to model dispersal and estimate true survival. Our model is formulated hierarchically and consists of survival, dispersal and observation submodels, assuming that encounters are possible anywhere within a study area. In a simulation study, our new spatial CJS model produced accurate estimates of true survival and dispersal behaviour for various sizes and shapes of the study area, even if emigration is substantial. However, when the information about dispersal is scarce due to low survival, low recapture probabilities and high emigration, the estimators are positively biased. Moreover, survival estimates are sensitive to the assumed dispersal kernel. We applied the spatial CJS model to a data set of adult red-backed shrikes ( Lanius collurio ). Apparent survival of males ( c . 0·5) estimated with the CJS model was larger than in females ( c . 0·4), but the application of the spatial CJS model revealed that both sexes had similar survival probabilities ( c . 0·6). The mean breeding dispersal distance in females was c . 700 m, while males dispersed only c . 250 m between years. Spatial CJS models enable study of dispersal and survival independent of study design constraints such as imperfect detection and size of the study area provided that some of the dispersing individuals remain in the study area. We discuss possible extensions of our model: alternative dispersal models and the inclusion of covariates and of a habitat suitability map.

Methods in Ecology and Evolution↗
USGS2013

Hierarchical spatial capture-recapture models: Modeling population density from stratified populations

Capture–recapture studies are often conducted on populations that are stratified by space, time or other factors. In this paper, we develop a Bayesian spatial capture–recapture (SCR) modelling framework for stratified populations – when sampling occurs within multiple distinct spatial and temporal strata. We describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture–recapture sampling, and also for modelling variation in density among strata. We use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular BUGS software packages. We provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids. Many capture–recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. We propose a hierarchical model that allows explicit modelling of group or strata effects. Because the model is formulated for individual encounter histories and is easily implemented in the BUGS language and other free software, it also provides a general framework for modelling individual effects, such as are present in SCR models.

Methods in Ecology and Evolution↗
USGS2013

Mark-resight abundance estimation under incomplete identification of marked individuals

Often less expensive and less invasive than conventional mark–recapture, so-called 'mark-resight' methods are popular in the estimation of population abundance. These methods are most often applied when a subset of the population of interest is marked (naturally or artificially), and non-invasive sighting data can be simultaneously collected for both marked and unmarked individuals. However, it can often be difficult to identify marked individuals with certainty during resighting surveys, and incomplete identification of marked individuals is potentially a major source of bias in mark-resight abundance estimators. Previously proposed solutions are ad hoc and will tend to underperform unless marked individual identification rates are relatively high (>90%) or individual sighting heterogeneity is negligible. Based on a complete data likelihood, we present an approach that properly accounts for uncertainty in marked individual detection histories when incomplete identifications occur. The models allow for individual heterogeneity in detection, sampling with (e.g. Poisson) or without (e.g. Bernoulli) replacement, and an unknown number of marked individuals. Using a custom Markov chain Monte Carlo algorithm to facilitate Bayesian inference, we demonstrate these models using two example data sets and investigate their properties via simulation experiments. We estimate abundance for grassland sparrow populations in Pennsylvania, USA when sampling was conducted with replacement and the number of marked individuals was either known or unknown. To increase marked individual identification probabilities, extensive territory mapping was used to assign incomplete identifications to individuals based on location. Despite marked individual identification probabilities as low as 67% in the absence of this territorial mapping procedure, we generally found little return (or need) for this time-consuming investment when using our proposed approach. We also estimate rookery abundance from Alaskan Steller sea lion counts when sampling was conducted without replacement, the number of marked individuals was unknown, and individual heterogeneity was suspected as non-negligible. In terms of estimator performance, our simulation experiments and examples demonstrated advantages of our proposed approach over previous methods, particularly when marked individual identification probabilities are low and individual heterogeneity levels are high. Our methodology can also reduce field effort requirements for marked individual identification, thus, allowing potential investment into additional marking events or resighting surveys.

Methods in Ecology and Evolution↗
USGS2014

Multiseason occupancy models for correlated replicate surveys

Occupancy surveys collecting data from adjacent (sometimes correlated) spatial replicates have become relatively popular for logistical reasons. Hines et al . ( 2010 ) presented one approach to modelling such data for single-season occupancy surveys. Here, we present a multiseason analogue of this model (with corresponding software) for inferences about occupancy dynamics. We include a new parameter to deal with the uncertainty associated with the first spatial replicate for both single-season and multiseason models. We use a case study, based on the brown-headed nuthatch, to assess the need for these models when analysing data from the North American Breeding Bird Survey (BBS), and we test various hypotheses about occupancy dynamics for this species in the south-eastern United States. The new model permits inference about local probabilities of extinction, colonization and occupancy for sampling conducted over multiple seasons. The model performs adequately, based on a small simulation study and on results of the case study analysis. The new model incorporating correlated replicates was strongly favoured by model selection for the BBS data for brown-headed nuthatch ( Sitta pusilla ). Latitude was found to be an important source of variation in local colonization and occupancy probabilities for brown-headed nuthatch, with both probabilities being higher near the centre of the species range, as opposed to more northern and southern areas. We recommend this new occupancy model for detection–nondetection studies that use potentially correlated replicates.

North Carolina, South Carolina, Florida, Georgia↗
USGS2014

Using mark-recapture distance sampling methods on line transect surveys

Mark–recapture distance sampling (MRDS) methods are widely used for density and abundance estimation when the conventional DS assumption of certain detection at distance zero fails, as they allow detection at distance zero to be estimated and incorporated into the overall probability of detection to better estimate density and abundance. However, incorporating MR data in DS models raises survey and analysis issues not present in conventional DS. Conversely, incorporating DS assumptions in MR models raises issues not present in conventional MR. As a result, being familiar with either conventional DS methods or conventional MR methods does not on its own put practitioners in good a position to apply MRDS methods appropriately. This study explains the sometimes subtly different varieties of MRDS survey methods and the associated concepts underlying MRDS models. This is done as far as possible without giving mathematical details – in the hope that this will make the key concepts underlying the methods accessible to a wider audience than if we were to present the concepts via equations. We illustrate use of the two main types of MRDS model by using data collected on two different types of survey: a survey of ungulate faecal pellets where two observers searched independently of each other; and a cetacean survey that used a search protocol that could accommodate responsive movement, with only one observer searching independently and the other being aware of all detections. Synthesis and applications . Mark–recapture DS is a widely used method for estimating animal density and abundance when detection of animals at distance zero is not certain. Two observer configurations and three statistical models are described, and it is important to choose the most appropriate model for the observer configuration and target species in question. By way of making the methods more accessible to practicing ecologists, we describe the key ideas underlying MRDS methods, the sometimes subtle differences between them, and we illustrate these by applying different kinds of MRDS method to surveys of two different target species using different survey configurations.

Methods in Ecology and Evolution↗