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The quantity and nutritional quality of forage are key drivers for ungulate populations, including mule deer (Odocoileus hemionus) and Rocky Mountain elk (Cervus elaphus nelsoni), in the western U.S., but current vegetation maps are too coarse spatially and temporally to effectively characterize fine-scale habitat. To address some of these gaps, we tested a novel approach using existing vegetation surveys, maps, and remotely sensed data to develop fine-scale forage species distribution models (SDMs) across Idaho, USA. We modelled 20 forage species that are suitable for mule deer and Rocky Mountain elk. Climatic, topographic, soil, vegetation, and disturbance variables were attributed to approximately 44.3 million habitat patches generated using multi-scale object-oriented image analysis. Lasso logistic regression was implemented to produce predictive SDMs. We evaluated if the inclusion of distal environmental variables (i.e., indirect effects) improved model performance beyond the inclusion of proximal variables (i.e., direct physiological effect) only. Our results showed that all models provided higher predictive accuracy than chance, with an average AUC across the 20 forage species of 0.84 for distal and proximal variables and 0.81 for proximal variables only. This indicated that the addition of distal variables improved model performance. We validated the models using two independent datasets from two regions of Idaho. We found that predicted forage species occurrence was on average within 10% of observed occurrence at both sites. However, predicted occurrences had much less variability between habitat patches than the validation data, implying that the models did not fully capture fine-scale heterogeneity. We suggest that future efforts will benefit from additional fine resolution (i.e., less than 30 m) environmental predictor variables and greater accounting of environmental disturbances (i.e., wildfire, grazing) in the training data. Our approach was novel both in methodology and spatial scale (i.e., resolution and extent). Our models can inform ungulate nutrition by predicting the occurrence of forage species and aide habitat management strategies to improve nutritional quality.

Sonoran pronghorn ( Antilocapra americana sonoriensis ), an endangered subspecies of American pronghorn, are of great conservation concern in the southwestern U. S. Following a dramatic population decline in 2002, the U.S. Fish and Wildlife Service (USFWS) began a captive breeding program that has subsequently been used to supplement the wild population. Additionally, in 2009 the USFWS proposed to establish another, self-sustaining population outside of their range at that time. We modeled Sonoran pronghorn population dynamics based on time-series of abundance and conducted a population viability analysis (PVA) to evaluate the benefit of these management actions. We found that rates of change in the Sonoran pronghorn population were closely tied to the amount of precipitation in the area but that viability was greatly enhanced by conservation actions including the maintenance of a captive population, as well as the establishment of two additional populations outside the current range.

The western population of the American White Pelican (Pelecanus erythrorhynchos) is of conservation concern because its breeding colonies are few and relatively small, making it susceptible to population declines or local extirpation. Biologists have counted nests and fledglings at western colonies, with varying levels of regularity, since the 1950s. We compiled available count data to assess trends in the abundance and productivity of breeding pelicans and investigate latitudinal changes in its breeding distribution. Population-wide, the rate of change in breeding pelicans’ abundance was negatively related to the previous year’s abundance and positively related to time, suggesting density dependence and a temporally increasing carrying capacity. The proportion of pelicans that bred at new or reestablished colonies, versus consistently occupied historic colonies, increased over time. Production (fledglings per nest) was not related to the abundance of nests, either by colony or population-wide, and declined over time across all colonies. Since the 1970s most new colonies have been established in the northern part of the range; the median latitude of active colonies from 2010 to 2014 lies approximately 114 km north of that of colonies active prior to 1970. In addition, six colonies established since 1990 are 359 km north of the median of all currently active colonies. Our results suggest that the pelican’s population dynamics in the western United States are influenced by long-term trends in abundance and production as well as short-term, densitydependent processes that influence annual rates of change in the abundance of breeding birds. Future research should seek to clarify the mechanism for this apparent density dependence and for the potential temporal increase in carrying capacity. Our results also suggest that efforts to monitor the American White Pelican should be modified to accommodate the apparent density-dependent dynamics of the population. We recommend that colonies be censused simultaneously and in consecutive years so that productivity and the rate of change in the abundance of breeding pelicans can be estimated.

a b s t r a c t The growing application of GPS telemetry in wildlife studies created need for analytical methods to meet both practical and theoretical concerns when conducting analyses of habitat or resource selection. We devised a new analysis approach of individual-based movement models for estimation of resource selec- tion based on probability of use. We merged the Brownian bridge model of space use with the synoptic model of habitat selection to describe and estimate patterns of habitat selection from GPS telemetry data. In doing so, our approach implicitly defines availability based on movement data when conducting analysis of GPS telemetry data. To do so, we employed a step-by-step approach, based on sequential triplets of observations of the animals' movements. Availability was portrayed as a circular normal dis- tribution at every middle GPS location, based on the existing Brownian bridge model of space use. This middle observation within the sequential triplet also reflected habitat selection, estimated by maxi- mum likelihoods, based on the deviation from otherwise random movement between the first and third observations. This approach allowed each triplet across time to be treated as independent, identically distributed observations when estimating habitat selection. To demonstrate the utility of the model, we analyzed GPS location data collected from free-ranging mountain goats (Oreamnos americanus) in the Cascade Mountains of the western United States to evaluate patterns of habitat selection while foraging during late spring and early summer. Slope of the terrain was the primary factor influencing resource selection by mountain goats in our study, with females selecting steeper areas closer to escape terrain than males. Finally, we derived a resource selection function applicable over a broad geographic extent to evaluate sites for potential release of mountain goats to augment the population in Washington, which has declined over the last 50 years.

Summary Typically in resource selection studies, the spatial extent of a home range is defined first and then the available resources within that perimeter are estimated. However, the home ranges (or habitats) of some animals are constrained by linear environmental features (e.g. rivers, shorelines). Traditional home range estimators often overestimate home range extent for such species, which can lead to spurious estimation of resource availability and selection. We used a synoptic model of space use to explicitly account for resource selection of a species constrained by linear features in its environment to compare with traditional home range estimators. We used the endangered Blakiston's fish owl Bubo blakistoni in the Russian Far East as our example. Mean annual home range size (± standard error) was more than three times larger when using kernel methods (30·3 ± 15·1 km2) than when using the synoptic model (9·4 ± 2·0 km2, n = 7). Fish owls showed strong selection for areas within valleys, closer to waterways, closer to patches of permanently open water and with greater channel complexity than available sites. Synthesis and applications. The synoptic model solves a long-standing problem in home range and resource selection studies because it provides an objective way to estimate the space use of a species whose habitat is constrained by linear features in its environment. Improvements in the accuracy of such estimations can lead to identification of important resources across landscapes, the development of more rigorous site-specific or landscape-scale management plans, and to scientifically defensible conservation or threat mitigation measures.

Due to concern over declining elk populations and the unknown effects of wolf predation, Idaho Department of Fish and Game initiated a survival study of elk in north-central Idaho. A total of 82 adult and 80 calves (~6 months old) were captured and fitted with GPS radio-collars from the winter of 2009 – winter 2014. Adult survival rates averaged 86% (range 76% 97%). Calf survival rates averaged 44% but varied dramatically from year to year. We used a stochastic model of elk-wolf dynamics to investigate the implications of a highly stochastic growth rates indicative of the observed survival rates. In particular, we were interested in the interaction between elk density-dependent growth, carrying capacity and the amount of stochasticity in predation rates. We found that when predation rates were highly stochastic and carrying capacities were low, populations behave as if they were in a predator pit but if carrying capacity was high or stochasticy low, populations grew out of the predator pit. These results suggest that in a stochastic environment, populations may actually be influenced by both bottom-up and top-down forcing simultaneously as opposed to being governed by one or the other. Jon Horne Background Jon is a Senior Research Biologist with Idaho Department of Fish and Game (IDFG). Jon has worked for IDFG since 2013 on research projects related to understanding elk population dynamics, especially in relation to predation. Other projects include improved approaches for abundance estimation and modeling wildlife space use. Jon obtained his Ph.D. from the University of Idaho in 2005 where he developed new approaches for studying animal space use. After obtaining his Ph.D., Jon continued working for the University of Idaho as a Post-doc and later a Research Scientist on several projects related to modeling space use and population dynamics of wildlife. PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.1933v1 | CC-BY 4.0 Open Access | rec: 7 Apr 2016, publ: 7 Apr 2016

Migration is an important component of the life history of many animals, but persistence of large-scale terrestrial migrations is being challenged by environmental changes that fragment habitats and create obstacles to animal movements. In northern Alaska, the Central Arctic herd (CAH) of barren-ground caribou (Rangifer tarandus granti) is known to migrate over large distances, but the herd's seasonal distributions and migratory movements are not well documented. From 2003-2007, we used GPS radio-collars to determine seasonal ranges and migration routes of 54 female caribou from the CAH. We calculated Brownian bridges to model fall and spring migrations for each year and used the mean of these over all 4 years to identify areas that were used repeatedly. Annual estimates of sizes of seasonal ranges determined by 90% fixed kernel utilization distributions were similar between summer and winter (X̅ = 27,929 SE = 1,064 and X̅ = 26,585 SE = 4912 km2, respectively). Overlap between consecutive summer and winter ranges varied from 3.3-18.3%. Percent overlap between summer ranges used during consecutive years (X̅ = 62.4% SE = 3.7%) was higher than for winter ranges (X̅ = 42.8% SE = 5.9%). Caribou used multiple migration routes each year, but some areas were used by caribou during all years, suggesting that these areas should be managed to allow for continued utilization by caribou. Restoring migration routes after they have been disturbed or fragmented is challenging. However, prior knowledge of movements and threats may facilitate maintenance of migratory paths and seasonal ranges necessary for long-term persistence of migratory species.

A growing number of programs seek to facilitate species conservation using incentive-based mechanisms. Recently, a market-based incentive program for the federally endangered Golden-cheeked Warbler (Dendroica chrysoparia) was implemented on a trial basis at Fort Hood, an Army training post in Texas, USA. Under this program, recovery credits accumulated by Fort Hood through contracts with private landowners are used to offset unintentional loss of breeding habitat of Golden-cheeked Warblers within the installation. Critical to successful implementation of such programs is the ability to value, in terms of changes to overall species viability, both habitat loss and habitat restoration or protection. In this study, we sought to answer two fundamental questions: Given the same amount of change in breeding habitat, does the change in some patches have a greater effect on metapopulation persistence than others? And if so, can characteristics of a patch (e.g., size or spatial location) be used to predict how the metapopulation will respond to these changes? To answer these questions, we describe an approach for using sensitivity analysis of a metapopulation projection model to predict how changes to specific habitat patches would affect species viability. We used a stochastic, discrete-time projection model based on stage-specific estimates of survival and fecundity, as well as various assumptions about dispersal among populations. To assess a particular patch's leverage, we quantified how much metapopulation viability was expected to change in response to changing the size of that patch. We then related original patch size and distance from the largest patch to each patch's leverage to determine if general patch characteristics could be used to develop guidelines for valuing changes to patches within a metapopulation. We found that both the characteristic that best predicted patch leverage and the magnitude of the relationship changed under different model scenarios. Thus, we were unable to find a consistent set of relationships, and therefore we emphasize the dangers in relying on general guidelines to assess patch value. Instead, we provide an approach that can be used to quantitatively evaluate patch value and identify critical needs for future research.

Roads and associated human development are prevalent features in many landscapes and can have significant impacts on wildlife populations, including the creation of barriers to movement that can reduce connectivity within and among populations. We evaluated how a highway in the northern Rocky Mountains influenced movements of American black bears (Ursus americanus) in a landscape modified by humans, resulting from human residences, recreation, and resource extraction. The objectives of the study were to determine habitat characteristics that were selected at locations where bears crossed the highway and develop and evaluate a predictive model for road-crossing locations based on habitat characteristics. Eleven out of 23 black bears fitted with GPS collars crossed highway 95 at least once. When bears crossed the highway they selected for specific habitat attributes, at both roadside and landscape scales, characterized by forested areas away from human development, with additional important habitat features including distance to cover, amount of shrub along the highway, and distance to water. Validation of our model with 24 independent highway crossing events from 2 years demonstrated high model predictability. We described how animal movement data can be used to identify and predict important road crossing areas for wildlife, which can assist in designing planning strategies for roadways and landscapes to facilitate wildlife movement to maintain connectivity among populations and to reduce wildlife–vehicle collisions.

Estimation of a population trend from a time series of abundance data is an important task in ecology, yet such estimation remains logistically and conceptually challenging in practice. First, the extent to which unequal intervals in the time series, due to missing observations or irregular sampling, compromise trend estimation is not well-known. Furthermore, the predominant trend estimation method (loglinear regression of abundance data against time) ignores the possibility of process noise, while an alternative method (the ‘diffusion approximation’) ignores observation error in the abundance data. State-space models that account for both process noise and observation error exist but have been little used. We study an adaptation of the exponential growth state-space (EGSS) model for use with missing data in the time series, and we compare its trend estimation to the status quo methods. The EGSS model provides superior estimates of trend across wide ranges of time series length and sources of variation. The performance of the EGSS model even with half of the counts in the time series missing implies that trend estimates may be improved by diverting effort away from annual monitoring and towards increasing time series length or improving precision of the abundance estimates for years that data are collected.

As habitat loss and fragmentation increase across ungulate ranges, identifying and prioritizing migration routes for conservation has taken on new urgency. Here we present a general framework using the Brownian bridge movement model (BBMM) that: (1) provides a probabilistic estimate of the migration routes of a sampled population, (2) distinguishes between route segments that function as stopover sites vs. those used primarily as movement corridors, and (3) prioritizes routes for conservation based upon the proportion of the sampled population that uses them. We applied this approach to a migratory mule deer (Odocoileus hemionus) population in a pristine area of southwest Wyoming, USA, where 2000 gas wells and 1609 km of pipelines and roads have been proposed for development. Our analysis clearly delineated where migration routes occurred relative to proposed development and provided guidance for on-the-ground conservation efforts. Mule deer migration routes were characterized by a series of stopover sites where deer spent most of their time, connected by movement corridors through which deer moved quickly. Our findings suggest management strategies that differentiate between stopover sites and movement corridors may be warranted. Because some migration routes were used by more mule deer than others, proportional level of use may provide a reasonable metric by which routes can be prioritized for conservation. The methods we outline should be applicable to a wide range of species that inhabit regions where migration routes are threatened or poorly understood.

Populations of ocelots (Leopardus pardalis) have declined during the past century due mainly to loss of habitat resulting in the ocelot being listed as endangered by the United States Fish and Wildlife Service. In southern Texas, the northern distribution of the ocelot overlaps the southern distribution of the bobcat (Lynx rufus). Because bobcats could adversely affect populations of ocelots through interspecific competition, we examined habitat selection of sympatric ocelots and bobcats to determine if habitat partitioning could be functioning to reduce interspecific interactions. Using radiotelemetry, we analyzed macro-scale (vegetative communities) and micro-scale (structural components) selection of habitats by sympatric ocelots and bobcats on Laguna Atascosa National Wildlife Refuge, Cameron County, Texas. We looked for differences in placement of home ranges within the general study area, selection of cover within home ranges, and use of structural components of vegetation within types of cover. There was substantial evidence for habitat partitioning with ocelots selecting areas with .75% canopy cover, while bobcats selected areas with ,75% canopy cover. Thus, coexistence between these two species might be facilitated by resource partitioning of habitat. RESUMEN—Las poblaciones de ocelote (Leopardus pardalis) han disminuido durante el ultimo siglo debido principalmente a la perdida de habitat. Como resultado, esta especie ha sido enlistada en peligro de extincion por el United States Fish and Wildlife Service. En el sur de Texas, la distribucion septentrional del ocelote se sobrepone con la distribucion meridional del lince rojo (Lynx rufus). Debido a que el lince rojo podro ´a afectar negativamente las poblaciones del ocelote con la competencia interespeco ´fica, examinamos la seleccion de habitat de las dos especies simpatricas para determinar si la particion de ha podo ´a reducir las interacciones interespeco ´ficas. Usando radiotelemetro ´a, analizamos la seleccion de habitat a una macro escala (comunidades vegetales) y a una micro escala (componentes estructurales) en Laguna Atascosa National Wildlife Refuge, condado de Cameron, Texas. Buscamos diferencias en la ubicacion de rangos de hogar dentro del area general de estudio, la seleccion del tipo de cobertura dentro del rango de hogar, y el uso de componentes estructurales de vegetacion dentro del tipo de cobertura. Encontramos evidencia substancial de la particion de habitat, con ocelotes seleccionando a con .75% de cobertura del dosel mientras que el lince rojo seleccionoareas con ,75% de cobertura del dosel. Por lo tanto, la particion de los recursos del habitat podro ´a facilitar la co-existencia entre estas dos especies.

We propose a simple multivariate model for describing and understanding animal space use that estimates an animal's probability of occurrence as an explicit function of the animal's association with a fixed spatial area (i.e., home range), the spatial distribution of resources within that area, and the occurrence of other animals. We begin with a null model of space use to describe an animal's utilization distribution in the absence of effects from environmental covariates. We then use this null model as the foundation for a set of candidate models of space use that incorporate different combinations of environmental covariates where each model is chosen to reflect various hypotheses about important drivers of space use. Models are parameterized via maximum likelihood using location data collected from individuals at discrete times (e.g., telemetry) and spatially explicit environmental covariates. Information theoretic criteria are used to select the model(s) with most support from the data. The best model(s) is then used for both estimating the animal's home range and for inferring the relative importance of various environmental factors on space use. As an example, we applied our approach using male white rhino (Ceratotherium simum) location data collected in Matobo National Park, Zimbabwe. The best synoptic model was able to capture the complexities of the utilization distribution while the model structure and parameter estimates provided a basis to infer the importance of various ecological factors affecting male rhino space use.

By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding population-level dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal, using discrete location data obtained at relatively short time intervals. The BBMM is based on the properties of a conditional random walk between successive pairs of locations, dependent on the time between locations, the distance between locations, and the Brownian motion variance that is related to the animal's mobility. We describe two critical developments that enable widespread use of the BBMM, including a derivation of the model when location data are measured with error and a maximum likelihood approach for estimating the Brownian motion variance. After the BBMM is fitted to location data, an estimate of the animal's probability of occurrence can be generated for an area during the time of observation. To illustrate potential applications, we provide three examples: estimating animal home ranges, estimating animal migration routes, and evaluating the influence of fine-scale resource selection on animal movement patterns.

Home-range models implicitly assume equal observation rates across the study area. Because this assumption is frequently violated, we describe methods for correcting home-range models for observation bias. We suggest corrections for 3 general types of home-range models including those for which parameters are estimated using least-squares theory, models utilizing maximum likelihood for parameter estimation, and models based on kernel smoothing techniques. When applied to mule deer (Odocoileus hemionus) location data, we found that uncorrected estimates of the utilization distribution were biased low by as much as 18.4% and biased high by 19.2% when compared to corrected estimates. Because the magnitude of bias is related to several factors, future research should determine the relative influence of each of these factors on home-range bias.

Strategies are needed to recover the ocelot Leopardus pardalis from the endangered species list. Recently, a population viability analysis (PVA) was developed which concluded that combinations of different recovery strategies were needed to effectively reduce ocelot extinction probability in the United States (US), with habitat protection and restoration identified as the most effective recovery scenario. We expanded this PVA model by incorporating landscape data to develop a more realistic habitat-based PVA for ocelots in southern Texas. We used RAMAS/ gis software to conduct a habitat-based PVA by linking landscape data with a demographic metapopulation model. The primary goal of this study was to provide a model for evaluating ocelot recovery strategies in the US. Each model scenario was simulated 1000 times over 50 years and we defined extinction as one individual remaining. Using the RAMAS/ gis program we identified 11 possible ocelot habitat patches (i.e., subpopulations) occurring in southern Texas. In addition, based on the habitat-based PVA model we found that combinations of different recovery strategies were needed to effectively reduce ocelot extinction probability in the US, with reducing road mortality the single most effective strategy. Short-term recovery strategies should include reducing ocelot road mortality, and translocation of ocelots into the US from northern Mexico. Long-term recovery strategies should include the restoration of habitat between and around existing ocelot habitat patches and the establishment of a dispersal corridor between ocelot breeding populations.

Fixed kernel density analysis with least squares cross-validation (LSCVh) choice of the smoothing parameter is currently recommended for home-range estimation. However, LSCVh has several drawbacks, including high variability, a tendency to undersmooth data, and multiple local minima in the LSCVh function. An alternative to LSCVh is likelihood cross-validation (CVh). We used computer simulations to compare estimated home ranges using fixed kernel density with CVh and LSCVh to true underlying distributions. Likelihood cross-validation generally performed better than LSCVh, producing estimates with better fit and less variability, and it was especially beneficial at sample sizes

Choosing an appropriate home range model is important for describing space use by animals and understanding the ecological processes affecting animal movement. Traditional approaches for choosing among home range models have not resulted in general, consistent, and unambiguous criteria that can be applied to individual data sets. We present a new application of information-theoretic model selection that overcomes many of the limitations of traditional approaches, as follows. (1) It alleviates the need to know the true home range to assess home range models, thus allowing performance to be evaluated with data on individual animals. (2) The best model can be chosen from a set of candidate models with the proper balance between fit and complexity. (3) If candidate home range models are based on underlying ecological processes, researchers can use the selected model not only to describe the home range, but also to infer the importance of various ecological processes affecting animal movements within the home range.

The overall objective of this program was to develop a set of models and methods for prioritizing management of threatened, endangered, and at-risk species. Specifically, we sought to develop tools for evaluating effects of management on population viability using varying levels of data: 1) life history data when little is known about a species, 2) count data when only estimates of abundance through time are available, and 3) metapopulation models when we have a lot of demographic data about multiple populations.

Although sea ice loss is the primary threat to polar bears (Ursus maritimus), little can be done to mitigate its effects without global efforts to reduce greenhouse gas emissions. Other factors, however, could exacerbate the impacts of sea ice loss on polar bears, such as exposure to increased industrial activity. The Arctic Ocean has enormous oil and gas potential, and its development is expected to increase in the coming decades. Estimates of polar bear resource selection will inform managers how bears use areas slated for oil development and to help guide conservation planning. We estimated temporally-varying resource selection patterns for non-denning adult female polar bears in the Chukchi Sea population (2008–2012) at two scales (i.e., home range and weekly steps) to identify factors predictive of polar bear use throughout the year, before any offshore development. From the best models at each scale, we estimated scale-integrated resource selection functions to predict polar bear space use across th...