Richard B. Chandler

Spatially explicit models for inference about density in unmarked or partially marked populations

Recently developed spatial capture-recapture (SCR) models represent a major advance over traditional capture-recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5-10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19-1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density - rather, spatial dependence can be informative about individual distribution and density.

A small-scale land-sparing approach to conserving biological diversity in tropical agricultural landscapes

Two contrasting strategies have been proposed for conserving biological diversity while meeting the increasing demand for agricultural products: land sparing and land sharing production systems. Land sparing involves increasing yield to reduce the amount of land needed for agriculture, whereas land-sharing agricultural practices incorporate elements of native ecosystems into the production system itself. Although the conservation value of these systems has been extensively debated, empirical studies are lacking. We compared bird communities in shade coffee, a widely practiced land-sharing system in which shade trees are maintained within the coffee plantation, with bird communities in a novel, small-scale, land-sparing coffee-production system (integrated open canopy or IOC coffee) in which farmers obtain higher yields under little or no shade while conserving an area of forest equal to the area under cultivation. Species richness and diversity of forest-dependent birds were higher in the IOC coffee farms than in the shade coffee farms, and community composition was more similar between IOC coffee and primary forest than between shade coffee and primary forest. Our study represents the first empirical comparison of well-defined land sparing and land sharing production systems. Because IOC coffee farms can be established by allowing forest to regenerate on degraded land, widespread adoption of this system could lead to substantial increases in forest cover and carbon sequestration without compromising agricultural yield or threatening the livelihoods of traditional small farmers. However, we studied small farms (<5 ha); thus, our results may not generalize to large-scale land-sharing systems. Furthermore, rather than concluding that land sparing is generally superior to land sharing, we suggest that the optimal approach depends on the crop, local climate, and existing land-use patterns.

Hierarchical distance-sampling models to estimate population size and habitat-specific abundance of an island endemic

Population size and habitat-specific abundance estimates are essential for conservation management. A major impediment to obtaining such estimates is that few statistical models are able to simultaneously account for both spatial variation in abundance and heterogeneity in detection probability, and still be amenable to large-scale applications. The hierarchical distance-sampling model of J. A. Royle, D. K. Dawson, and S. Bates provides a practical solution. Here, we extend this model to estimate habitat-specific abundance and rangewide population size of a bird species of management concern, the Island Scrub-Jay (Aphelocoma insularis), which occurs solely on Santa Cruz Island, California, USA. We surveyed 307 randomly selected, 300 m diameter, point locations throughout the 250-km2 island during October 2008 and April 2009. Population size was estimated to be 2267 (95% CI 1613-3007) and 1705 (1212-2369) during the fall and spring respectively, considerably lower than a previously published but statistically problematic estimate of 12 500. This large discrepancy emphasizes the importance of proper survey design and analysis for obtaining reliable information for management decisions. Jays were most abundant in low-elevation chaparral habitat; the detection function depended primarily on the percent cover of chaparral and forest within count circles. Vegetation change on the island has been dramatic in recent decades, due to release from herbivory following the eradication of feral sheep (Ovis aries) from the majority of the island in the mid-1980s. We applied best-fit fall and spring models of habitat-specific jay abundance to a vegetation map from 1985, and estimated the population size of A. insularis was 1400-1500 at that time. The 20-30% increase in the jay population suggests that the species has benefited from the recovery of native vegetation since sheep removal. Nevertheless, this jays tiny range and small population size make it vulnerable to natural disasters and to habitat alteration related to climate change. Our results demonstrate that hierarchical distance-sampling models hold promise for estimating population size and spatial density variation at large scales. Our statistical methods have been incorporated into the R package unmarked to facilitate their use by animal ecologists, and we provide annotated code in the Supplement.

Spatially explicit integrated population models

Summary Studies of demographic processes are typically restricted to small geographic areas and short time periods due to the costs of marking and monitoring individuals. However, environmental changes are occurring at much broader spatial and temporal scales, and thus, inferences about the mechanisms governing population dynamics need to be scaled accordingly. Recently developed integrated population models (IPMs) represent an approach for doing so, by jointly analysing survey data and capture–recapture data. Although promising, several shortcomings of conventional IPMs exist, including difficulties accounting for spatial variation in demographic, movement and detection parameters; limited ability to make spatially explicit predictions of abundance or vital rates; and a requirement that the survey data and the capture–recapture data are independent. We demonstrate how each of these limitations can be resolved by adopting a spatial population dynamics model upon which both the survey data and the capture–recapture data are conditioned. We applied the model to 6 years of hair data collected on the threatened Louisiana black bear Ursus americanus luteolus. For years in which the hair samples were genotyped, the resulting data are information-rich (but expensive) spatial capture–recapture (SCR) data. For the remaining years, the data are binary detection data, of the type often analysed using occupancy models. We compared estimates of demographic parameters and annual abundance using various combinations of the SCR and detection data, and found that combining the SCR data and the detection data resulted in more precise estimates of abundance relative to estimates that did not use the detection data. A simulation study provided additional evidence of increased precision, as well as evidence that the estimators of annual abundance are approximately unbiased. The ability to combine survey data and capture–recapture data using a spatially explicit model opens many possibilities for designing cost effective studies and scaling up inferences about the demographic processes influencing spatial and temporal population dynamics.

Integrating resource selection information with spatial capture–recapture

Summary 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.

Scrub-shrub bird habitat associations at multiple spatial scales in beaver meadows in Massachusetts.

Abstract.— Most scrub-shrub bird species are declining in the northeastern United States, and these declines are largely attributed to regional declines in habitat availability. American Beaver (Castor canadensis; hereafter “beaver”) populations have been increasing in the Northeast in recent decades, and beavers create scrub-shrub habitat through their dam-building and foraging activities. Few systematic studies have been conducted on the value of beaver-modified habitats for scrub-shrub birds, and these data are important for understanding habitat selection of scrub-shrub birds as well as for assessing regional habitat availability for these species. We conducted surveys in 37 beaver meadows in a 2,800-km2 study area in western Massachusetts during 2005 and 2006 to determine the extent to which these beaver-modified habitats are used by scrub-shrub birds, as well as the characteristics of beaver meadows most closely related to bird use. We modeled bird abundance in relation to microhabitat-, patch-, and landscape-context variables while adjusting for survey-specific covariates affecting detectability using N-mixture models. We found that scrub-shrub birds of regional conservation concern occupied these sites and that birds responded differently to microhabitat, patch, and landscape characteristics of beaver meadows. Generally, scrub-shrub birds increased in abundance along a gradient of increasing vegetation complexity, and three species were positively related to patch size. We conclude that these habitats can potentially play an important role in regional conservation of scrub-shrub birds and recommend that conservation priority be given to larger beaver meadows with diverse vegetation structure and composition.

Modeling structured population dynamics using data from unmarked individuals

The study of population dynamics requires unbiased, precise estimates of abundance and vital rates that account for the demographic structure inherent in all wildlife and plant populations. Traditionally, these estimates have only been available through approaches that rely on intensive mark-recapture data. We extended recently developed N-mixture models to demonstrate how demographic parameters and abundance can be estimated for structured populations using only stage-structured count data. Our modeling framework can be used to make reliable inferences on abundance as well as recruitment, immigration, stage-specific survival, and detection rates during sampling. We present a range of simulations to illustrate the data requirements, including the number of years and locations necessary for accurate and precise parameter estimates. We apply our modeling framework to a population of northern dusky salamanders (Desmognathus fuscus) in the mid-Atlantic region (USA) and find that the population is unexpectedly declining. Our approach represents a valuable advance in the estimation of population dynamics using multistate data from unmarked individuals and should additionally be useful in the development of integrated models that combine data from intensive (e.g., mark-recapture) and extensive (e.g., counts) data sources.

Regional synthesis of habitat relationships in shrubland birds.

Abstract Shrubland birds are declining throughout the eastern United States. To manage scrub-shrub habitats for birds, managers need information on avian habitat relationships. Past studies have produced contradictory results in some cases and may be of limited generality because of site- and habitat-specific factors. We studied shrubland birds across 6 habitats in 3 New England states to provide more general information on habitat relationships than has been possible in past studies. Our study sites included all major scrub-shrub habitats in New England: wildlife openings, regenerating clear-cuts, beaver ponds, utility rights-of-way, pitch pine (Pinus rigida) woodlands, and scrub oak (Quercus ilicifolia) barrens and ranged from Connecticut to northern New Hampshire, with research conducted from 2002 to 2007. Using N-mixture models of repeated point counts, we found that 6 of 12 shrubland birds preferred areas with greater shrub cover. An additional 4 species appeared to prefer areas with lower-stature vegetation and greater forb cover. Eight of 10 bird species showed relationships with cover of individual plant species, with Spiraea spp., willows (Salix spp.), alders (Alnus spp.), and invasive exotics being the most important. We recommend that shrubland management for birds focus on providing 2 distinct habitats: 1) areas of tall (>1.5 m) vegetation with abundant shrub cover and 2) areas of lower (<1.5 m) vegetation with abundant forb cover but fewer shrubs.

Estimating landscape resistance to dispersal

Dispersal is an inherently spatial process that can be affected by habitat conditions in sites encountered by dispersers. Understanding landscape resistance to dispersal is important in connectivity studies and reserve design, but most existing methods use resistance functions with cost parameters that are subjectively chosen by the investigator. We develop an analytic approach allowing for direct estimation of resistance parameters that folds least cost path methods typically used in simulation approaches into a formal statistical model of dispersal distributions. The core of our model is a frequency distribution of dispersal distances expressed as least cost distance rather than Euclidean distance, and which includes terms for feature-specific costs to dispersal and sex (or other traits) of the disperser. The model requires only origin and settlement locations for multiple individuals, such as might be obtained from mark–recapture studies or parentage analyses, and maps of the relevant habitat features. To evaluate whether the model can estimate parameters correctly, we fit our model to data from simulated dispersers in three kinds of landscapes (in which resistance of environmental variables was categorical, continuous with a patchy configuration, or continuous in a trend pattern). We found maximum likelihood estimators of resistance and individual trait parameters to be approximately unbiased with moderate sample sizes. We applied the model to a small grizzly bear dataset to demonstrate how this approach could be used when the primary interest is in the prediction of costs and found that estimates were consistent with expectations based on bear ecology. Our method has important practical applications for testing hypotheses about dispersal ecology and can be used to inform connectivity planning efforts, via the resistance estimates and confidence intervals, which can be used to create a data-driven resistance surface.

Spatial occupancy models for predicting metapopulation dynamics and viability following reintroduction

Summary 1. The reintroduction of a species into its historic range is a critical component of conservation programmes designed to restore extirpated metapopulations. However, many reintroduction efforts fail, and the lack of rigorous monitoring programmes and statistical models have prevented a general understanding of the factors affecting metapopulation viability following reintroduction. 2. Spatially explicit metapopulation theory provides the basis for understanding the dynamics of fragmented populations linked by dispersal, but the theory has rarely been used to guide reintroduction programmes because most spatial metapopulation models require presence– absence data from every site in the network, and they do not allow for observation error such as imperfect detection. 3. We develop a spatial occupancy model that relaxes these restrictive assumptions and allows for inference about metapopulation extinction risk and connectivity. We demonstrate the utility of the model using six years of data on the Chiricahua leopard frog Lithobates chiricahuensis, a threatened desert-breeding amphibian that was reintroduced to a network of sites in Arizona USA in 2003. 4. Our results indicate that the model can generate precise predictions of extinction risk and produce connectivity maps that can guide conservation efforts following reintroduction. In the case of L. chiricahuensis, many sites were functionally isolated, and 82% of sites were characterized by intermittent water availability and high local extinction probabilities (0� 84, 95% CI: 0� 64–0� 99). However, under the current hydrological conditions and spatial arrangement of sites, the risk of metapopulation extinction is estimated to be <3% over a 50-year time horizon. 5. Low metapopulation extinction risk appears to result from the high dispersal capability of the species, the high density of sites in the region and the existence of predator-free permanent wetlands with low local extinction probabilities. Should management be required, extinction risk can be reduced by either increasing the hydroperiod of existing sites or by creating new sites to increase connectivity. 6. Synthesis and applications. This work demonstrates how spatio-temporal statistical models based on ecological theory can be applied to forecast the outcomes of conservation actions such as reintroduction. Our spatial occupancy model should be particularly useful when management agencies lack the funds to collect intensive individual-level data.