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At least 577 records · Page 32Linked to original sources
USGS2008

The human footprint in the west: a large-scale analysis of anthropogenic impacts.

Anthropogenic features such as urbanization, roads, and power lines, are increasing in western United States landscapes in response to rapidly growing human populations. However, their spatial effects have not been evaluated. Our goal was to model the human footprint across the western United States. We first delineated the actual area occupied by anthropogenic features, the physical effect area. Next, we developed the human footprint model based on the ecological effect area, the zone influenced by features beyond their physical presence, by combining seven input models: three models quantified top-down anthropogenic influences of synanthropic predators (avian predators, domestic dog and cat presence risk), and four models quantified bottom-up anthropogenic influences on habitat (invasion of exotic plants, human-caused fires, energy extraction, and anthropogenic wildland fragmentation). Using independent bird population data, we found bird abundance of four synanthropic species to correlate positively with human footprint intensity and negatively for three of the six species influenced by habitat fragmentation. We then evaluated the extent of the human footprint in relation to terrestrial (ecoregions) and aquatic systems (major rivers and lakes), regional management and conservation status, physical environment, and temporal changes in human actions. The physical effect area of anthropogenic features covered 13% of the western United States with agricultural land (9.8%) being most dominant. High-intensity human footprint areas (class 8 – 10) overlapped highly productive low-elevation private landholdings and covered 7% of the western United States compared to 48% for low-intensity areas (class 1 – 3), which were confined to low-productivity high-elevation federal landholdings. Areas within 1 km of rivers were more affected by the human footprint compared to lakes. Percentage human population growth was higher in low-intensity human footprint areas. The disproportional regional effects of the human footprint on landscapes in the western United States create a challenge to management of ecosystems and wildlife populations. Using footprint models, managers can plan land use actions, develop restoration scenarios, and identify areas of high conservation value at local landscapes within a regional context. Moreover, human footprint models serve as a tool to stratify landscapes for studies investigating floral and faunal response to human disturbance intensity gradients.

Ecological Applications↗
USGS2013

Ecological prediction with nonlinear multivariate time-frequency functional data models

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Lower Missouri River↗
USGS2026

Environmental and demographic mechanisms underlying population dynamics provide relative stability in an island songbird

Understanding demographic and ecological mechanisms underlying population dynamics is a key goal in population ecology and can inform effective conservation and management. Species may be well adapted to persist under normal ranges of environmental conditions, but increasingly novel conditions due to climate change may strain their demographic buffering mechanisms. Small isolated populations on islands are expected to be especially vulnerable to declines caused by novel environmental conditions, but the ecological drivers of population dynamics on islands are often unknown. We used an integrated population model to (1) examine ecological drivers of population growth and (2) assess global population trends of the Island Scrub-Jay ( Aphelocoma insularis ) from 2009 to 2019. Our results suggest that population size increased slightly over this interval despite declines during severe drought. We also found evidence that density dependence, precipitation, and food availability affected fecundity and the survival of nonbreeding individuals. Breeder survival was relatively stable and had a weak effect on population growth as expected for long-lived species. Overall, our results provide an optimistic snapshot for this species by demonstrating resilience to contemporary drought but also emphasize the species' potential vulnerability due to its small population size.

California↗
USGS2019

Decision-making in agent-based modeling: A current review and future prospectus

All basic processes of ecological populations involve decisions; when and where to move, when and what to eat, and whether to fight or flee. Yet decisions and the underlying principles of decision-making have been difficult to integrate into the classical population-level models of ecology. Certainly, there is a long history of modeling individuals' searching behavior, diet selection, or conflict dynamics within social interactions. When all the individuals are given certain simple rules to govern their decision-making processes, the resultant population–level models have yielded important generalizations and theory. But it is also recognized that such models do not represent the way real individuals decide on actions. Factors that influence a decision include the organism's environment with its dynamic rewards and risks, the complex internal state of the organism, and its imperfect knowledge of the environment. In the case of animals, it may also involve complex social factors, and experience and learning, which vary among individuals. The way that all factors are weighed and processed to lead to decisions is a major area of behavioral theory. While classic population-level modeling is limited in its ability to integrate decision-making in its actual complexity, the development of individual- or agent-based models (IBM/ABMs) (we use ABM throughout to designate both “agent-based modeling” and an “agent-based model”) has opened the possibility of describing the way that decisions are made, and their effects, in minute detail. Over the years, these models have increased in size and complexity. Current ABMs can simulate thousands of individuals in realistic environments, and with highly detailed internal physiology, perception and ability to process the perceptions and make decisions based on those and their internal states. The implementation of decision-making in ABMs ranges from fairly simple to highly complex; the process of an individual deciding on an action can occur through the use of logical and simple (if-then) rules to more sophisticated neural networks and genetic algorithms. The purpose of this paper is to give an overview of the ways in which decisions are integrated into a variety of ABMs and to give a prospectus on the future of modeling of decisions in ABMs.

Frontiers in Ecology and Evolution↗
USGS2015

Adaptive invasive species distribution models: A framework for modeling incipient invasions

The utilization of species distribution model(s) (SDM) for approximating, explaining, and predicting changes in species’ geographic locations is increasingly promoted for proactive ecological management. Although frameworks for modeling non-invasive species distributions are relatively well developed, their counterparts for invasive species—which may not be at equilibrium within recipient environments and often exhibit rapid transformations—are lacking. Additionally, adaptive ecological management strategies address the causes and effects of biological invasions and other complex issues in social-ecological systems. We conducted a review of biological invasions, species distribution models, and adaptive practices in ecological management, and developed a framework for adaptive, niche-based, invasive species distribution model (iSDM) development and utilization. This iterative, 10-step framework promotes consistency and transparency in iSDM development, allows for changes in invasive drivers and filters, integrates mechanistic and correlative modeling techniques, balances the avoidance of type 1 and type 2 errors in predictions, encourages the linking of monitoring and management actions, and facilitates incremental improvements in models and management across space, time, and institutional boundaries. These improvements are useful for advancing coordinated invasive species modeling, management and monitoring from local scales to the regional, continental and global scales at which biological invasions occur and harm native ecosystems and economies, as well as for anticipating and responding to biological invasions under continuing global change.

Biological Invasions↗
USGS2006

GIS-based niche modeling for mapping species' habitats

Ecological a??niche modelinga?? using presence-only locality data and large-scale environmental variables provides a powerful tool for identifying and mapping suitable habitat for species over large spatial extents. We describe a niche modeling approach that identifies a minimum (rather than an optimum) set of basic habitat requirements for a species, based on the assumption that constant environmental relationships in a species' distribution (i.e., variables that maintain a consistent value where the species occurs) are most likely to be associated with limiting factors. Environmental variables that take on a wide range of values where a species occurs are less informative because they do not limit a species' distribution, at least over the range of variation sampled. This approach is operationalized by partitioning Mahalanobis D2 (standardized difference between values of a set of environmental variables for any point and mean values for those same variables calculated from all points at which a species was detected) into independent components. The smallest of these components represents the linear combination of variables with minimum variance; increasingly larger components represent larger variances and are increasingly less limiting. We illustrate this approach using the California Gnatcatcher (Polioptila californica Brewster) and provide SAS code to implement it.

Ecology↗
USGS2014

Land-margin ecosystem hydrologic data for the coastal Everglades, Florida, water years 1996-2012

Mangrove forests and salt marshes dominate the landscape of the coastal Everglades (Odum and McIvor, 1990). However, the ecological effects from potential sea-level rise and increased water flows from planned freshwater Everglades restoration on these coastal systems are poorly understood. The National Park Service (NPS) proposed the South Florida Global Climate Change Project (SOFL-GCC) in 1990 to evaluate climate change and the effect from rising sea levels on the coastal Everglades, particularly at the marsh/mangrove interface or ecotone (Soukup and others, 1990). A primary objective of SOFL-GCC project was to monitor and synthesize the hydrodynamics of the coastal Everglades from the upstream freshwater marsh to the downstream estuary mangrove. Two related hypotheses were set forward (Nuttle and Cosby, 1993): 1. There exists hydrologic conditions (tide, local rainfall, and upstream water deliveries), which characterize the location of the marsh/mangrove ecotone along the marine and terrestrial hydrologic gradient; and 2. The marsh/mangrove ecotone is sensitive to fluctuations in sea level and freshwater inflow from inland areas. Hydrologic monitoring of the SOFL-GCC network began in 1995 after startup delays from Hurricane Andrew (August 1992) and organizational transfers from the NPS to the National Biological Survey (October 1993) and the merger with the U.S. Geological Survey (USGS) Biological Research Division in 1996 (Smith, 2004). As the SOFL-GCC project progressed, concern by environmental scientists and land managers over how the diversion of water from Everglades National Park would affect the restoration of the greater Everglades ecosystem. Everglades restoration scenarios were based on hydrodynamic models, none of which included the coastal zone (Fennema and others, 1994). Modeling efforts were expanded to include the Everglades coastal zone (Schaffranek and others, 2001) with SOFL-GCC hydrologic data assisting the ecological modeling needs. In 2002, as a response for a more interdisciplinary science approach to understanding the coastal Everglades ecological system, the SOFL-GCC hydrology project was integrated into the “Dynamics of Land-Margin Ecosystems: Historical Change, Hydrology, Vegetation, Sediment, and Climate” study (Smith and others, 2002). Data from the ongoing study has been useful in providing an empirical hydrologic baseline for the greater Everglades ecosystem restoration science and management needs. The hydrology network consisted of 13 hydrologic gaging stations installed in the southwestern coastal region of Everglades National Park along three transects: Shark River (Shark or SH) transect, Lostmans River (Lostmans or LO) transect, and Chatham River (Chatham or CH) transect (fig. 1). There were five paired surface-water/groundwater gaging stations on the Shark transect (SH1, SH2, SH3, SH4, and SH5) and one stage gaging station (BSC) in the Big Sable Creek; four paired surface-water/groundwater gaging stations on the Lostmans transect (LO1, LO2, LO3, and LO4); and three paired surface-water/groundwater gaging stations on the Chatham transect (CH1, CH2, and CH3). Both surface-water and groundwater levels, salinities, and temperatures were monitored at the paired gaging stations. Rainfall was recorded at marsh and open canopy gaging stations. This report details the study introduction, method, and description of data collected, which are accessible through the final instantaneous hydrologic dataset stored in the USGS South Florida Information Access (SOFIA) South Florida Hydrology Database website, http://sofia.usgs.gov/exchange/sfl_hydro_data/location.html#brdlandmargin .

Florida↗
USGS2017

An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species. Our method accounts for several issues that are common when modeling spatiotemporal ecological data including multiple levels of detection probability, multiple data sources, and computational limitations that occur when making fine-scale inference over a large spatiotemporal domain. We apply the model to estimate the colonization dynamics of sea otters ( Enhydra lutris ) in Glacier Bay, in southeastern Alaska.

Ecology↗
USGS2006

Mussel dynamics model: A hydroinformatics tool for analyzing the effects of different stressors on the dynamics of freshwater mussel communities

A model for simulating freshwater mussel population dynamics is presented. The model is a hydroinformatics tool that integrates principles from ecology, river hydraulics, fluid mechanics and sediment transport, and applies the individual-based modelling approach for simulating population dynamics. The general model layout, data requirements, and steps of the simulation process are discussed. As an illustration, simulation results from an application in a 10 km reach of the Upper Mississippi River are presented. The model was used to investigate the spatial distribution of mussels and the effects of food competition in native unionid mussel communities, and communities infested by Dreissena polymorpha, the zebra mussel. Simulation results were found to be realistic and coincided with data obtained from the literature. These results indicate that the model can be a useful tool for assessing the potential effects of different stressors on long-term population dynamics, and consequently, may improve the current understanding of cause and effect relationships in freshwater mussel communities. ?? 2006 Elsevier B.V. All rights reserved.

Ecological Modelling↗
USGS2007

The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling

A wide variety of ecological applications require spatially explicit, historic, current, and projected land use and land cover data. The U.S. Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States. The newly developed FORE-SCE model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land cover change through 2020 for multiple plausible scenarios. Projected proportions of future land use were initially developed, and then sited on the lands with the highest potential for supporting that land use and land cover using a statistically based stochastic allocation procedure. Three scenarios of 2020 land cover were mapped for the western Great Plains in the US. The model provided realistic, high-resolution, scenario-based land-cover products suitable for multiple applications, including studies of climate and weather variability, carbon dynamics, and regional hydrology.

Journal of Land Use Science↗
USGS2019

The importance of simulation assumptions when evaluating detectability in population models

Population monitoring is important for investigating a variety of ecological questions, and N-mixture models are increasingly used to model population size (N) and trends (lambda) while estimating detectability (p) from repeated counts within primary periods (when populations are closed to changes). Extending these models to dynamic processes with serial dependence across primary periods may relax the closure assumption, but simulations to evaluate models and inform effort (e.g., number of repeated counts) typically assume p is constant or random across sites and years. Thus, it is unknown how these models perform under scenarios where trends in p confound inferences on N and lambda, and conclusions regarding effort may be overoptimistic. Here, we used global positioning system (GPS) data from greater sage-grouse (Centrocercus urophasianus) to inform simulations of the detection process for lek counts of this species, and we created scenarios with and without linear annual trends in p. We then compared estimates of N and lambda from hierarchical population models either fit with single maximum counts or with detectability estimated from repeated counts (dynamic N-mixture models). We also explored using auxiliary data to correct counts for variation in detectability. Uncorrected count models consistently underestimated N by >50% whereas N-mixture models without auxiliary data underestimated N to a lesser degree due to unmodeled heterogeneity in p such as age. Nevertheless, estimates of lambda from both types of models were unbiased and similar for scenarios without trends in p. When p declined systematically across years, uncorrected count models underestimated lambda whereas N-mixture models estimated lambda with little bias when all sites were counted repeatedly. Auxiliary data also reduced bias in parameter estimates. Evaluating population models using scenarios with systematic variation in p may better reveal potential biases and inform effort than simulations that assume p is constant or random. Dynamic N-mixture models can distinguish between trends in p and N, but also require repeated counts within primary periods for accurate estimates. Auxiliary data may be useful when researchers lack repeated counts, wish to monitor more sites less intensively, or require unbiased estimates of N.

Ecosphere↗
USGS2004

Salt-Pond Box Model (SPOOM) and Its Application to the Napa-Sonoma Salt Ponds, San Francisco Bay, California

A box model to simulate water volume and salinity of a salt pond has been developed by the U.S. Geological Survey to obtain water and salinity budgets. The model, SPOOM, uses the principle of conservation of mass to calculate daily pond volume and salinity and includes a salt crystallization and dissolution algorithm. Model inputs include precipitation, evaporation, infiltration, and water transfers. Salinity and water-surface-elevation data were collected monthly in the Napa-Sonoma Salt-Pond Complex from February 1999 through September 2001 and were used to calibrate and validate the model. The months when water transfers occurred were known but the magnitudes were unknown, so the magnitudes of water transfers were adjusted in the model to calibrate simulated pond volumes to measured pond volumes for three ponds. Modeled salinity was then compared with measured salinity, which remained a free parameter, in order to validate the model. Comparison showed good correlation between modeled and measured salinity. Deviations can be attributed to lack of water-transfer information. Water and salinity budgets obtained through modeling will be used to help interpret ecological data from the ponds. This model has been formulated to be applicable to the Napa-Sonoma salt ponds, but can be applied to other salt ponds.

Water-Resources Investigations Report↗
USGS2018

Impacts of temporal revisit designs on the power to detect trend with a linear mixed model: An application to long-term monitoring of Sierra Nevada lakes

Long-term ecological monitoring programs often use linear mixed models to estimate trend in an ecological indicator sampled across large landscapes. A linear mixed model is versatile for estimating a linear trend in time as well as components of spatial and temporal variationin the case of unbalanced data structures , which are common in complex monitoring designs where limited sampling effort must be optimized over time and space. A power analysis was used to inform a lake chemistry monitoring design, including selecting the most appropriate temporal revisit design. Pilot data from surveys of lakes across large wilderness national parks (Sequoia, Kings Canyon, and Yosemite national parks) were used to obtain variance components for a Monte Carlo power simulation. Using a linear mixed model for a range of temporal revisit designs, sample sizes, and trend magnitudes, we evaluated the power to detect trend, the trend test size, and the relative bias of trend coefficient estimates for four continuous and normally distributed indicators. Contrary to prior research based on large-sample approximations that identified a single panel of sites visited annually as the revisit design generating the highest power, we found that the power to detect a 12-year trend based on the Wald t-test from a linear mixed model may be optimized by obtaining unbalanced data sets with limited to no annual replication. We emphasize the importance of examining variance composition, sample size, and the power and size of the trend test with Monte Carlo simulation when allocating sampling effort over time and space.

Ecological Indicators↗
USGS2019

The Laurentian Great Lakes: A case study in ecological disturbance and climate change

Climate change effects are already significant, but can also magnify other ecological problems. This can be clearly seen in the Laurentian Great Lakes, which have suffered habitat degradation, fishery overharvest and dramatic alterations by invasive species. Thermal changes are expected to cause extensive loss of suitable fish habitat, and changing precipitation patterns will aggravate the problems with our highly modified lotic and lentic systems. A brief summary of the historic ecological context provided by the Great Lakes case is presented, followed by the descriptions of selected tools that help to understand and evaluate both ecological and climate change problems. Species distribution models and habitat classification combined with climate change predictions can identify the distribution and extent of optimal habitats, and identify which are most vulnerable to climate change. Ecological flow modelling can help to identify when critical flow changes are likely. Mechanistic simulation modelling specifies understanding of how aquatic systems function and can reveal cause and effect relationships. These tools can be used to help managers to protect optimal habitat, resist climate change effects to other habitats and adapt cultural systems to climate‐altered aquatic systems.

Great Lakes↗
USGS2002

Artificial intelligence based decision support for trumpeter swan management

The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-State area where Montana, Idaho, and Wyoming come together has declined to just a few hundred pairs. However, these birds are part of the Rocky Mountain Population which additionally has over 3,500 birds breeding in Alberta, British Columbia, Northwest Territories, and Yukon Territory. To a large degree, these birds seem to have abandoned traditional migratory pathways in the flyway. Waterfowl managers have been interested in decision support tools that would help them explore simulated management scenarios in their quest towards reaching population recovery and the reestablishment of traditional migratory pathways. I have developed a decision support system to assist biologists with such management, especially related to wetland ecology. Decision support systems use a combination of models, analytical techniques, and information retrieval to help develop and evaluate appropriate alternatives. Swan management is a domain that is ecologically complex, and this complexity is compounded by spatial and temporal issues. As such, swan management is an inherently distributed problem. Therefore, the ecological context for modeling swan movements in response to management actions was built as a multiagent system of interacting intelligent agents that implements a queuing model representing swan migration. These agents accessed ecological knowledge about swans, their habitats, and flyway management principles from three independent expert systems. The agents were autonomous, had some sensory capability, and could respond to changing conditions. A key problem when developing ecological decision support systems is empirically determining that the recommendations provided are valid. Because Rocky Mountain trumpeter swans have been surveyed for a long period of time, I was able to compare simulated distributions provided by the system with actual field observations across 20 areas for the period 1988-2000. Applying the Matched Pairs Multivariate Permutation Test as a statistical tool was a new approach for comparing flyway distributions of waterfowl over time that seemed to work well. Based on this approach, the empirical evidence that I gathered led me to conclude that the base queuing model does accurately simulate swan distributions in the flyway. The system was insensitive to almost all model parameters tested. That remains perplexing, but might result from the base queuing model, itself, being particularly effective at representing the actual ecological diversity in the world of Rocky Mountain trumpeter swans, both spatial and temporally.

Idaho, Montana, Utah, Wyoming↗
USGS2026

Understanding co-invading species’ ecological strategies and interactions with causal inference and simple dynamic models: Bighead (Hypophthalmichthys nobilis) and Silver carp (H. molitrix) in the Illinois River, USA

Species interactions among multiple invasive species can present hidden challenges for management and eradication efforts. Bighead and Silver carp ( Hypophthalmichthys nobilis and H. molitrix , respectively) are closely related species that have co-invaded the Mississippi River and many of its tributaries, including the Illinois River. We used a combination of causal inference and dynamic models to test for interspecific interactions in a 10-year data set of hydroacoustic density data from the Illinois River. The results suggest that Bighead and Silver carp are exploiting different ecological strategies; where Bighead carp shows fast initial growth but low equilibrium density and Silver carp had a lower low-density growth rate but larger equilibrium population density. Further, we found evidence for a positive interspecific effect of Silver carp on Bighead carp. The mechanism leading to the observed positive interactions is unknown, but could be an effect of ecological facilitation, differential responses to harvest of both species, or hybridization and introgression. Finally, while these results provide valuable information in the ecological strategies and relationships between these species, simulations of the resulting models suggest that the magnitude of the positive interaction is likely too small to be exploited for innovative multispecies management interventions.

Illinois↗
USGS2020

Assessment of existing groundwater quality data in the Green-Duwamish watershed, Washington

The United States Geological Survey (USGS) provided technical support to the Washington Department of Ecology (Ecology) in their assessment of the role groundwater plays in contributing pollutant loading to the Green-Duwamish River near Seattle, Washington. Ecology is developing watershed hydrology models of the Green-Duwamish watershed, and need to assign realistic contaminant concentrations to the various Hydrologic Response Units represented in their models. The USGS compiled existing groundwater quality data in the Green-Duwamish watershed, and this report summarizes results and interpretation of the dataset, including identifying data gaps and needs for further research and monitoring. The sources of existing data were the USGS’s National Water Information System, Ecology’s Environmental Information Management System, and a compilation of several studies by Leidos, a scientific research company. The water-quality parameters of interest included polychlorinated biphenyl (PCB) Aroclors and congeners, phthalates, carcinogenic polycyclic aromatic hydrocarbons (cPAHs), arsenic, copper, and zinc. Results were grouped into the four subwatersheds delineated in Ecology’s hydrology models: Duwamish, Lower Green, Soos, and Upper Green. Results from the Duwamish subwatershed were further sub-divided by the USGS into the Lower Duwamish, containing land adjacent to the Lower Duwamish Waterway Superfund site, and the Upper Duwamish, containing the remaining area of the Duwamish subwatershed. Groundwater quality data in the Lower Duwamish were treated separately because there is known contamination in this area. The availability of water quality data varied by subwatershed as follows: phthalate data was only available within the Duwamish, PCB data was available within the Duwamish and Lower Green, cPAH data was available within the Duwamish, Lower Green, and Soos, and data for arsenic, copper, and zinc were available within all four subwatersheds. More than 99 percent of the available data was within the Duwamish subwatershed, identifying a need for additional monitoring of groundwater quality in the other subwatersheds.

Washington↗
USGS2010

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

Journal of Applied Ecology↗