Improved estimates of certainty in stable-isotope-based methods for tracking migratory animals.

The use of stable-hydrogen isotopes (deltaD) has become a common tool for estimating geographic patterns of movement in migratory animals. This method relies on broad and relatively predictable geographic patterning in deltaD values of precipitation, but these patterns are not estimated without error. In addition, deltaD measurements are relatively imprecise, particularly for organic tissue. Most models for estimating geographic locations have ignored these sources of error. Common modeling approaches include regression, range-matching, and likelihood-based assignment tests (including discriminant analysis). Here, we show the benefits of a simple stochastic extension to likelihood-based assignment tests that incorporates two estimable sources of error and describe the resulting influence on the certainty of assigning breeding origins for wintering American Redstarts (Setophaga ruticilla), a small Nearctic-Neotropical migratory bird. Through simulation, we incorporated both spatial interpolation error associated with models of deltaD in precipitation and analytical error associated with the measurement of deltaD in tissue samples. In general, assignments that did not include these sources of error fell within the ranges of the stochastic results, but the difference in proportion of birds assigned to any one breeding region varied by as much as 54%. To explore how the distribution of assignments generated from error models influenced the application of these results, we developed a simple model of winter habitat loss. We removed the proportion of Redstarts wintering at a particular site from the global population and then used the isotope-based assignments to predict the resulting population declines for each breeding region. This gave distributions of change in population sizes, some of which included no change or even a population increase. The sources of error we modeled may challenge the degree of certainty in the use of stable-isotope-based data on connectivity to predict population dynamics of migratory animals. We suggest that stronger inference will result from incorporating these sources of error into future studies that use deltaD or other stable isotopes to infer the geographic origin of individuals.     

A case study of bats and white‐nose syndrome demonstrating how to model population viability with evolutionary effects

Ecological factors generally affect population viability on rapid time scales. Traditional population viability analyses (PVA) therefore focus on alleviating ecological pressures, discounting potential evolutionary impacts on individual phenotypes. Recent studies of evolutionary rescue (ER) focus on cases in which severe, environmentally induced population bottlenecks trigger a rapid evolutionary response that can potentially reverse demographic threats. ER models have focused on shifting genetics and resulting population recovery, but no one has explored how to incorporate those findings into PVA. We integrated ER into PVA to identify the critical decision interval for evolutionary rescue (DIER) under which targeted conservation action should be applied to buffer populations undergoing ER against extinction from stochastic events and to determine the most appropriate vital rate to target to promote population recovery. We applied this model to little brown bats (Myotis lucifugus) affected by white‐nose syndrome (WNS), a fungal disease causing massive declines in several North American bat populations. Under the ER scenario, the model predicted that the DIER period for little brown bats was within 11 years of initial WNS emergence, after which they stabilized at a positive growth rate (λ = 1.05). By comparing our model results with population trajectories of multiple infected hibernacula across the WNS range, we concluded that ER is a potential explanation of observed little brown bat population trajectories across multiple hibernacula within the affected range. Our approach provides a tool that can be used by all managers to provide testable hypotheses regarding the occurrence of ER in declining populations, suggest empirical studies to better parameterize the population genetics and conservation‐relevant vital rates, and identify the DIER period during which management strategies will be most effective for species conservation.     

Cooling of bat hibernacula to mitigate white-nose syndrome

White-nose syndrome (WNS) is a fungal disease that has caused precipitous declines in several North American bat species, creating an urgent need for conservation. We examined how microclimates and other characteristics of hibernacula have affected bat populations following WNS-associated declines and evaluated whether cooling of warm, little-used hibernacula could benefit bats. During the period following mass mortality (2013–2020), we conducted 191 winter surveys of 25 unmanipulated hibernacula and 6 manipulated hibernacula across Pennsylvania (USA). We joined these data with additional datasets on historical (pre-WNS) bat counts and on the spatial distribution of underground sites. We used generalized linear mixed models and model selection to identify factors affecting bat populations. Winter counts of Myotis lucifugus were higher and increased over time in colder hibernacula (those with midwinter temperatures of 3–6 °C) compared with warmer (7–11 °C) hibernacula. Counts of Eptesicus fuscus, Myotis leibii, and Myotis septentrionalis were likewise higher in colder hibernacula (temperature effects = –0.73 [SE 0.15], –0.51 [0.18], and –0.97 [0.28], respectively). Populations of M. lucifugus and M. septentrionalis increased most over time in hibernacula surrounded by more nearby sites, whereas Eptesicus fuscus counts remained high where they had been high before WNS onset (pre-WNS high count effect = 0.59 [0.22]). Winter counts of M. leibii were higher in hibernacula with high vapor pressure deficits (VPDs) (particularly over 0.1 kPa) compared with sites with lower VPDs (VPD effect = 15.3 [4.6]). Counts of M. lucifugus and E. fuscus also appeared higher where VPD was higher. In contrast, Perimyotis subflavus counts increased over time in relatively warm hibernacula and were unaffected by VPD. Where we manipulated hibernacula, we achieved cooling of on average 2.1 °C. At manipulated hibernacula, counts of M. lucifugus and P. subflavus increased over time (years since manipulation effect = 0.70 [0.28] and 0.51 [0.15], respectively). Further, there were more E. fuscus where cooling was greatest (temperature difference effect = –0.46 [SE 0.11]), and there was some evidence there were more P. subflavus in hibernacula sections that remained warm after manipulation. These data show bats are responding effectively to WNS through habitat selection. In M. lucifugus, M. septentrionalis, and possibly P. subflavus, this response is ongoing, with bats increasingly aggregating at suitable hibernacula, whereas E. fuscus remain in previously favored sites. Our results suggest that cooling warm sites receiving little use by bats is a viable strategy for combating WNS.     

Management of the Panzootic White‐Nose Syndrome through Culling of Bats

Abstract: The probability of persistence of many species of hibernating bats in the United States is greatly reduced by an emerging infectious disease, white‐nose syndrome (WNS). In the United States WNS is rapidly spreading and is associated with a psychrophilic fungus, Geomyces destructans. WNS has caused massive mortality of bats that hibernate. Efforts to control the disease have been ineffective. The culling of bats in hibernacula has been proposed as a way to break the transmission cycle or slow the spread of WNS. We formulated a disease model to examine the efficacy of culling to abate WNS in bat populations. We based the model dynamics on disease transmission in maternity roosts, swarms, and hibernacula, which are the arenas of contact among bats. Our simulations indicated culling will not control WNS in bats primarily because contact rates are high among colonial bats, contact occurs in multiple arenas, and periodic movement between arenas occurs. In general, culling is ineffective in the control of animal diseases in the wild.     

Incorporating the perceptual range of animals into connectivity models

The perceptual range of an animal towards different landscape elements affects its movements through heterogeneous landscapes. However, empirical knowledge and modeling tools are lacking to assess the consequences of variation in the perceptual range for movement patterns and connectivity. In this study we tested how changes in the assumed perception of different landscape elements affect the outcomes of a connectivity model. We used an existing individual-based, spatially explicit model for the dispersal of Eurasian lynx (Lynx lynx). We systematically altered the perceptual range in which animals recognize forest fragments, water bodies or cities, as well as the probability that they respond to these landscape elements. Overall, increasing the perceptual range of the animals enhanced connectivity substantially, both qualitatively and quantitatively. An enhanced range of attraction to forests had the strongest impact, doubling immigration success; an enhanced range of attraction to rivers had a slightly lower impact; and an enhanced range of avoidance of cities had the lowest impact. Correcting the enhancement in connectivity by the abundance of each of the landscape elements in question reversed the results, indicating the potential sensitivity of connectivity models to rare landscape elements (in our case barriers such as cities). Qualitatively, the enhanced perception resulted in strong changes in movement patterns and connectivity. Furthermore, model results were highly parameter-specific and patch-specific. These results emphasize the need for further empirical research on the perceptual capabilities of different animals in different landscapes and conditions. They further indicate the usefulness of spatially explicit individual-based simulation models for recognizing consistent patterns that emerge, despite uncertainty regarding animals’ movement behavior. Altogether, this study demonstrates the need to extend the concept of ‘perceptual ranges’ beyond patch detection processes, to encompass the wide range of elements that can direct animal movements during dispersal through heterogeneous landscapes.     

Roads,Interrupted Dispersal,and Genetic Diversity in Timber Rattlesnakes

Abstract: Anthropogenic habitat modification often creates barriers to animal movement, transforming formerly contiguous habitat into a patchwork of habitat islands with low connectivity. Roadways are a feature of most landscapes that can act as barriers or filters to migration among local populations. Even small and recently constructed roads can have a significant impact on population genetic structure of some species, but not others. We developed a research approach that combines fine‐scale molecular genetics with behavioral and ecological data to understand the impacts of roads on population structure and connectivity. We used microsatellite markers to characterize genetic variation within and among populations of timber rattlesnakes (Crotalus horridus) occupying communal hibernacula (dens) in regions bisected by roadways. We examined the impact of roads on seasonal migration, genetic diversity, and gene flow among populations. Snakes in hibernacula isolated by roads had significantly lower genetic diversity and higher genetic differentiation than snakes in hibernacula in contiguous habitat. Genetic‐assignment analyses revealed that interruption to seasonal migration was the mechanism underlying these patterns. Our results underscore the sizeable impact of roads on this species, despite their relatively recent construction at our study sites (7 to 10 generations of rattlesnakes), the utility of population genetics for studies of road ecology, and the need for mitigating effects of roads.     

Application of tree-ring isotopic analyses to reconstruct historical water use of riparian trees.

Historical patterns of water source use by trees inferred from long-term records of tree-ring stable isotopic content could assist in evaluating the impact of human alterations to natural stream flow regimes (e.g., water impoundments, stream flow diversions, and groundwater extraction). Our objective was to assess the utility of the hydrogen stable isotopic composition (SD) of tree rings as an index of historical water source use by riparian trees. We investigated the influence of site conditions that varied in climate and hydrology on the relationship between deltaD of Populus xylem water (deltaD(xyl)) and tree-ring cellulose (deltaD(cell)). deltaD(xyl) and deltaD(cell) were strongly correlated across sites (r2 = 0.89). However, the slope of this relationship was less than 1, indicating that factors other than deltaD(xyl) influenced deltaD(cell). Inverse modeling with an isotopic fractionation model for tree-ring cellulose suggested that the lack of one-to-one correspondence between deltaD(xyl) and deltaD(cell) was due to the influence of the hydrogen isotopic content of the atmospheric water vapor (deltaD(atm)). Empirically measured values of deltaD(cell) were typically within the seasonal range of deltaD(cell) predicted from the fractionation model. Sensitivity analyses showed that changes in deltaD(xyl) generally had a greater influence at high-elevation montane sites, whereas deltaD(xyl) and deltaD(atm) had about equal influence on deltaD(cell) at low-elevation desert sites. The intrasite relationship between deltaD(cell) and deltaD(xyl) among individual trees was poor, perhaps because of the within-site spatial variation in hydrologic conditions and associated tree physiological responses. Our study suggests that historical variation in deltaD(cell) of Populus provides information on historical variation in both time-integrated water source use and atmospheric conditions; and that the influence of atmospheric conditions is not consistent over sites with large differences in temperature and humidity. Reconstruction of xylem water sources of Populus in riparian ecosystems from deltaD(cell) will be more direct at higher elevation mountain sites than at low-elevation desert sites.     

Effects of white‐nose syndrome on regional population patterns of 3 hibernating bat species

Hibernating bats have undergone severe recent declines across the eastern United States, but the cause of these regional‐scale declines has not been systematically evaluated. We assessed the influence of white‐nose syndrome (an emerging bat disease caused by the fungus Pseudogymnoascus destructans, formerly Geomyces destructans) on large‐scale, long‐term population patterns in the little brown myotis (Myotis lucifugus), the northern myotis (Myotis septentrionalis), and the tricolored bat (Perimyotis subflavus). We modeled population trajectories for each species on the basis of an extensive data set of winter hibernacula counts of more than 1 million individual bats from a 4‐state region over 13 years and with data on locations of hibernacula and first detections of white‐nose syndrome at each hibernaculum. We used generalized additive mixed models to determine population change relative to expectations, that is, how population trajectories differed with a colony's infection status, how trajectories differed with distance from the point of introduction of white‐nose syndrome, and whether declines were concordant with first local observation of the disease. Population trajectories in all species met at least one of the 3 expectations, but none met all 3. Our results suggest, therefore, that white‐nose syndrome has affected regional populations differently than was previously understood and has not been the sole cause of declines. Specifically, our results suggest that in some areas and species, threats other than white‐nose syndrome are also contributing to population declines, declines linked to white‐nose syndrome have spread across large geographic areas with unexpected speed, and the disease or other threats led to declines in bat populations for years prior to disease detection. Effective conservation will require further research to mitigate impacts of white‐nose syndrome, renewed attention to other threats to bats, and improved surveillance efforts to ensure early detection of white‐nose syndrome.     

Conservation Planning with Multiple Organizations and Objectives

Abstract: There has been a dramatic increase in the number of conservation organizations worldwide. It is now common for multiple organizations to operate in the same landscape in pursuit of different conservation goals. New objectives, such as maintenance of ecosystem services, will attract additional funding and new organizations to conservation. Systematic conservation planning helps in the design of spatially explicit management actions that optimally conserve multiple landscape features (e.g., species, ecosystems, or ecosystem services). But the methods used in its application implicitly assume that a single actor implements the optimal plan. We investigated how organizational behavior and conservation outcomes are affected by the presence of autonomous implementing organizations with different objectives. We used simulation models and game theory to explore how alternative behaviors (e.g., organizations acting independently or explicitly cooperating) affected an organization's ability to protect their feature of interest, and investigated how the distribution of features in the landscape influenced organizations’ attitudes toward cooperation. Features with highly correlated spatial distributions, although typically considered an opportunity for mutually beneficial conservation planning, can lead to organizational interactions that result in lower levels of protection. These detrimental outcomes can be avoided by organizations that cooperate when acquiring land. Nevertheless, for cooperative purchases to benefit both organizations’ objectives, each must forgo the protection of land parcels that they would consider to be of high conservation value. Transaction costs incurred during cooperation and the sources of conservation funding could facilitate or hinder cooperative behavior.     

Analyzing animal movements using Brownian bridges

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.     

Ontogenetic and sex-specific differences in density-dependent habitat selection of a marine fish population

The spatial dynamics of species are the result of complex interactions between density-independent and density-dependent sources of variability. Disentangling these two sources of variability has challenged ecologists working in both terrestrial and aquatic ecosystems. Using a novel spatially explicit statistical model, we tested for the presence of density-independent and density-dependent habitat selection in yellowfin sole (Limanda aspera) in the eastern Bering Sea. We found specificities in the density-dependent processes operating across ontogeny and particularly with gender. Density-dependent habitat expansion occurred primarily in females, and to a lesser degree in males. These patterns were especially evident in adult stages, while juvenile stages of both sexes exhibited a mix of different dynamics. Association of yellowfin sole with substrate type also varied by sex and to a lesser degree with size, with large females distributed over a wider range of substrates than males. Moreover, yellowfin sole expanded northward as cold subsurface waters retracted in summer, suggesting high sensitivity to arctic warming. Our findings illustrate how marginal habitats can play an important role in buffering density-dependent habitat expansion, with direct implications for resource management. Our spatially explicit modeling approach is effective in evaluating density-dependent spatial dynamics, and can easily be used to test similar hypotheses from a variety of aquatic and terrestrial ecosystems.     

Animal dispersal modelling: Handling landscape features and related animal choices

Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled.We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path.     

Dispersal in Spatially Explicit Population Models

Abstract: Ruckelshaus et al. (1997) outlined a simulation model of dispersal between patches in a fragmented landscape. They showed that dispersal success—the proportion of dispersers successfully locating a patch—was particularly sensitive to errors in dispersal mortality and concluded that this limits the utility of spatially explicit population models in conservation biology. I contend that, although they explored error propagation in a simple dispersal model, they did not explore how errors are propagated in spatially explicit population models, as no consideration of population processes was included. I developed a simple simulation model to investigate the effect of varying dispersal success on predictions of patch occupancy and population viability, the conventional outputs of spatially explicit population models. The model simulates births and deaths within habitat patches and dispersal as the transfer of individuals between them. Model predictions were sensitive to changes in dispersal success across a restricted range of within-patch growth rates, which depended on the dispersal initiation mechanism, patch carrying capacities, and number of generations simulated. Predictions of persistence and patch occupancy were generally more sensitive to changes in dispersal success (1) under presaturation rather than saturation dispersal; (2) at lower patch carrying capacities; and (3) over longer time periods. The framework I present provides a means of assessing, quantitatively, the regions of parameter space for which differences in dispersal success are likely to have a large effect on population model outputs. Investigating the effect of the representation of dispersal behavior within the demographic and landscape context provides a more useful assessment of whether our lack of knowledge is likely to cause unacceptable uncertainty in the predictions of spatially explicit population models.     

Estimation of population density by spatially explicit capture-recapture analysis of data from area searches

The recent development of capture-recapture methods for estimating animal population density has focused on passive detection using devices such as traps or automatic cameras. Some species lend themselves more to active searching: a polygonal plot may be searched repeatedly and the locations of detected individuals recorded, or a plot may be searched just once and multiple cues (feces or other sign) identified as belonging to particular individuals. This report presents new likelihood-based spatially explicit capture-recapture (SECR) methods for such data. The methods are shown to be at least as robust in simulations as an equivalent Bayesian analysis, and to have negligible bias and near-nominal confidence interval coverage with parameter values from a lizard data set. It is recommended on the basis of simulation that plots for SECR should be at least as large as the home range of the target species. The R package "secr" may be used to fit the models. The likelihood-based implementation extends the spatially explicit analyses available for search data to include binary data (animal detected or not detected on each occasion) or count data (multiple detections per occasion) from multiple irregular polygons, with or without dependence among polygons. It is also shown how the method may be adapted for detections along a linear transect.     

Building statistical models to analyze species distributions.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.     

A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove

The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.     

A hierarchical model for spatial capture-recapture data

Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture-recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.     

Measuring terrestrial subsidies to aquatic food webs using stable isotopes of hydrogen

Understanding river food webs requires distinguishing energy derived from primary production in the river itself (autochthonous) from that produced externally (allochthonous), yet there are no universally applicable and reliable techniques for doing so. We compared the natural abundance stable isotope ratios of hydrogen (deltaD) of allochthonous and autochthonous energy sources in four different aquatic ecosystems. We found that autochthonous organic matter is uniformly far more depleted in deuterium (lower deltaD values) than allochthonous: an average difference of approximately 100% per hundred. We also found that organisms at higher trophic levels, including both aquatic invertebrates and fish, have deltaD values intermediate between aquatic algae and terrestrial plants. The consistent differences between leaves and algae in deltaD among these four watersheds, along with the intermediate values in higher trophic levels, indicate that natural abundance hydrogen isotope signatures are a powerful tool for partitioning energy flow in aquatic ecosystems.