Geology topics
Paul B. Conn
Publications and source records attributed to Paul B. Conn.
An integrated path for spatial capture–recapture and animal movement modeling
Ecologists and conservation biologists increasingly rely on spatial capture–recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.
A guide to Bayesian model checking for ecologists
Checking that models adequately represent data is an essential component of applied statistical inference. Ecologists increasingly use hierarchical Bayesian statistical models in their research. The appeal of this modeling paradigm is undeniable, as researchers can build and fit models that embody complex ecological processes while simultaneously accounting for observation error. However, ecologists tend to be less focused on checking model assumptions and assessing potential lack of fit when applying Bayesian methods than when applying more traditional modes of inference such as maximum likelihood. There are also multiple ways of assessing the fit of Bayesian models, each of which has strengths and weaknesses. For instance, Bayesian P values are relatively easy to compute, but are well known to be conservative, producing P values biased toward 0.5. Alternatively, lesser known approaches to model checking, such as prior predictive checks, cross‐validation probability integral transforms, and pivot discrepancy measures may produce more accurate characterizations of goodness‐of‐fit but are not as well known to ecologists. In addition, a suite of visual and targeted diagnostics can be used to examine violations of different model assumptions and lack of fit at different levels of the modeling hierarchy, and to check for residual temporal or spatial autocorrelation. In this review, we synthesize existing literature to guide ecologists through the many available options for Bayesian model checking. We illustrate methods and procedures with several ecological case studies including (1) analysis of simulated spatiotemporal count data, (2) N‐mixture models for estimating abundance of sea otters from an aircraft, and (3) hidden Markov modeling to describe attendance patterns of California sea lion mothers on a rookery. We find that commonly used procedures based on posterior predictive P values detect extreme model inadequacy, but often do not detect more subtle cases of lack of fit. Tests based on cross‐validation and pivot discrepancy measures (including the “sampled predictive P value”) appear to be better suited to model checking and to have better overall statistical performance. We conclude that model checking is necessary to ensure that scientific inference is well founded. As an essential component of scientific discovery, it should accompany most Bayesian analyses presented in the literature.
Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts
Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatiotemporal variation in density. We review several concepts from the burgeoning literature on spatiotemporal statistical models, including the nature of the temporal structure (i.e., descriptive or dynamical) and strategies for dimension reduction to promote computational tractability. We also review several features as they specifically relate to abundance estimation, including boundary conditions, population closure, choice of link function, and extrapolation of predicted relationships to unsampled areas. We then compare a suite of novel and existing spatiotemporal hierarchical models for animal count data that permit animal density to vary over space and time, including formulations motivated by resource selection and allowing for closed populations. We gauge the relative performance (bias, precision, computational demands) of alternative spatiotemporal models when confronted with simulated and real data sets from dynamic animal populations. For the latter, we analyze spotted seal ( Phoca largha ) counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted. Simulation analyses suggested that multiple types of spatiotemporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision. Analysis of spotted seal data indicated that several model formulations, including those based on a log-Gaussian Cox process, had a tendency to overestimate abundance. By contrast, a model that included a population closure assumption and a scale prior on total abundance produced estimates that largely conformed to our a priori expectation. Although care must be taken to tailor models to match the study population and survey data available, we argue that hierarchical spatiotemporal statistical models represent a powerful way forward for estimating abundance and explaining variation in the distribution of dynamical populations.
Age-specific survival of tundra swans on the lower Alaska Peninsula
The population of Tundra Swans (Cygnus columbianus columbianus) breeding on the lower Alaska Peninsula represents the southern extremity of the species' range and is uniquely nonmigratory. We used data on recaptures, resightings, and recoveries of neck-collared Tundra Swans on the lower Alaska Peninsula to estimate collar loss, annual apparent survival, and other demographic parameters for the years 1978–1989. Annual collar loss was greater for adult males fitted with either the thinner collar type (0.34) or the thicker collar type (0.15) than for other age/sex classes (thinner: 0.10, thicker: 0.04). The apparent mean probability of survival of adults (0.61) was higher than that of immatures (0.41) and for both age classes varied considerably by year (adult range: 0.44–0.95, immature range: 0.25–0.90). To assess effects of permanent emigration by age and breeding class, we analyzed post hoc the encounter histories of swans known to breed in our study area. The apparent mean survival of known breeders (0.65) was generally higher than that of the entire marked sample but still varied considerably by year (range 0.26–1.00) and indicated that permanent emigration of breeding swans was likely. We suggest that reductions in apparent survival probability were influenced primarily by high and variable rates of permanent emigration and that immigration by swans from elsewhere may be important in sustaining a breeding population at and near Izembek National Wildlife Refuge.
Spatial occupancy models for large data sets
Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km 2 ) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.
Comparative demography of new world populations of thrushes (Turdus spp.): Comment
Survival and fecundity are fundamental to the study of evolutionary ecology, as they are two of the key variables influencing the constrained optimization process we call natural selection. Likewise, population managers require accurate estimates of these parameters; along with dispersal, they govern population dynamics ( Lack 1954 ) and thus are essential for predicting population change and the effects of management actions. It can be frustrating, then, when survival is difficult to estimate for certain populations, as in some avian species that exhibit a substantial degree of dispersal.
A general model for the analysis of mark-resight, mark-recapture, and band-recovery data under tag loss
Estimates of waterfowl demographic parameters often come from resighting studies where birds fit with individually identifiable neck collars are resighted at a distance. Concerns have been raised about the effects of collar loss on parameter estimates, and the reliability of extrapolating from collared individuals to the population. Models previously proposed to account for collar loss do not allow survival or harvest parameters to depend on neck collar presence or absence. Also, few models have incorporated recent advances in mark‐recapture theory that allow for multiple states or auxiliary encounters such as band recoveries. We propose a multistate model for tag loss in which the presence or absence of a collar is considered as a state variable. In this framework, demographic parameters are corrected for tag loss and questions related to collar effects on survival and recovery rates can be addressed. Encounters of individuals between closed sampling periods also can be incorporated in the analysis. We discuss data requirements for answering questions related to tag loss and sampling designs that lend themselves to this purpose. We illustrate the application of our model using a study of lesser snow geese ( Chen caerulescens caerulescens ).