Geology topics

Richard Chandler

Publications and source records attributed to Richard Chandler.

8 recordsLinked to original sources

USGS2025

Apparent annual survival of adult Golden-winged Warblers (Vermivora chrysoptera) may not differ by sex or region

Understanding range-wide demographic, spatial, and temporal variation in annual survival is essential for managing species of conservation concern. Multi-population models are useful tools for integrating diverse datasets, reducing biases, and deriving survival estimates across differing spatial scales. We conducted a range-wide, multi-population apparent annual survival analysis for a declining songbird, Vermivora chrysoptera (Golden-winged Warbler), using data from 18 sites across its breeding and nonbreeding grounds. This Nearctic-Neotropical migrant breeds in 2 disjunct regional populations, the Great Lakes and Appalachian Mountains, which are experiencing different rates of decline. We aimed to quantify regional-, site-, and sex-specific apparent annual survival estimates to identify geographic patterns or demographic factors influencing population declines. We used simulations to assess the precision of our estimates. Our models did not reveal a substantial difference in apparent annual survival between the Great Lakes (0.41, 95% credible interval (CrI):0.31–0.50) and the Appalachian regions (0.49, 95% CrI: 0.36–0.60), as CrIs overlapped. Site-specific estimates also showed no clear differences in apparent annual survival among sites representing both regional populations. Male apparent annual survival tended to be greater than female apparent annual survival in both regions, though CrI’s overlapped. Our study suggests demographic factors other than adult annual survival likely play a larger role in recent regional and range-wide population declines, such as productivity, juvenile/immature survival, or recruitment. Simulations indicate that improving recapture probability and study duration of datasets could lead to more precise apparent annual survival estimates. However, our model produced CrI ranges comparable to the most ideal data collection scenario, suggesting the lack of trends we found was not due to variability in our estimates. We stress the importance of addressing inherent biases in survival datasets and the need for standardized collaborative efforts to inform species conservation on a range-wide scale.

Ornithology↗

USGS2021

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.

Ecology↗

USGS2021

Wetland selection by female Ring-Necked Ducks (Aythya collaris) in the Southern Atlantic Flyway

On the wintering grounds, wetland selection by waterfowl is influenced by spatiotemporal resource distribution. The ring-necked duck (Aythya collaris) winters in the southeastern United States where a disproportionate amount of Atlantic Flyway ring-necked duck harvest occurs. We quantified female ring-necked duck selection for wetland characteristics during and after the 2017-2018 and 2018-2019 waterfowl hunting seasons using discrete choice modeling under a Bayesian framework. Relative probability of selection was primarily influenced by characteristics at the local wetland scale. Relative probability of selection was higher for flooded agriculture and vegetated wetlands than open water and was positively influenced by wetland area during the winter. After the hunting season, the relative probability of selection decreased for flooded agriculture but increased for vegetated wetlands, and the effect of wetland area decreased in magnitude. We attribute changes in selection during and after the hunting season to dietary shifts related to migratory preparation, resource depletion, and reproductive pairing. Understanding the wetland characteristics that wintering waterfowl select, and the spatial scale at which selection occurs, is important for informing effective wetland management and waterfowl harvest practices.

Florida, Georgia↗

USGS2018

Increasing connectivity between metapopulation ecology and landscape ecology

Metapopulation ecology and landscape ecology aim to understand how spatial structure influences ecological processes, yet these disciplines address the problem using fundamentally different modeling approaches. Metapopulation models describe how the spatial distribution of patches affects colonization and extinction, but often do not account for the heterogeneity in the landscape between patches. Models in landscape ecology use detailed descriptions of landscape structure, but often without considering colonization and extinction dynamics. We present a novel spatially explicit modeling framework for narrowing the divide between these disciplines to advance understanding of the effects of landscape structure on metapopulation dynamics. Unlike previous efforts, this framework allows for statistical inference on landscape resistance to colonization using empirical data. We demonstrate the approach using 11 yr of data on a threatened amphibian in a desert ecosystem. Occupancy data for Lithobates chiricahuensis (Chiricahua leopard frog) were collected on the Buenos Aires National Wildlife Refuge (BANWR), Arizona, USA from 2007 to 2017 following a reintroduction in 2003. Results indicated that colonization dynamics were influenced by both patch characteristics and landscape structure. Landscape resistance increased with increasing elevation and distance to the nearest streambed. Colonization rate was also influenced by patch quality, with semi‐permanent and permanent ponds contributing substantially more to the colonization of neighboring ponds relative to intermittent ponds. Ponds that only hold water intermittently also had the highest extinction rate. Our modeling framework can be widely applied to understand metapopulation dynamics in complex landscapes, particularly in systems in which the environment between habitat patches influences the colonization process

Ecology↗

USGS2014

Inferences about population dynamics from count data using multi-state models: A comparison to capture-recapture approaches

Wildlife populations consist of individuals that contribute disproportionately to growth and viability. Understanding a population's spatial and temporal dynamics requires estimates of abundance and demographic rates that account for this heterogeneity. Estimating these quantities can be difficult, requiring years of intensive data collection. Often, this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, N-mixture models allow for the estimation of abundance, and spatial variation in abundance, from count data alone. We extend recently developed multistate, open population N-mixture models, which can additionally estimate demographic rates based on an organism's life history characteristics. In our extension, we develop an approach to account for the case where not all individuals can be assigned to a state during sampling. Using only state-specific count data, we show how our model can be used to estimate local population abundance, as well as density-dependent recruitment rates and state-specific survival. We apply our model to a population of black-throated blue warblers ( Setophaga caerulescens ) that have been surveyed for 25 years on their breeding grounds at the Hubbard Brook Experimental Forest in New Hampshire, USA. The intensive data collection efforts allow us to compare our estimates to estimates derived from capture–recapture data. Our model performed well in estimating population abundance and density-dependent rates of annual recruitment/immigration. Estimates of local carrying capacity and per capita recruitment of yearlings were consistent with those published in other studies. However, our model moderately underestimated annual survival probability of yearling and adult females and severely underestimates survival probabilities for both of these male stages. The most accurate and precise estimates will necessarily require some amount of intensive data collection efforts (such as capture–recapture). Integrated population models that combine data from both intensive and extensive sources are likely to be the most efficient approach for estimating demographic rates at large spatial and temporal scales.

New Hampshire↗

USGS2014

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.

Ecology↗

USGS2014

Reply to Efford on ‘Integrating resource selection information with spatial capture-recapture’

1. We proposed ( Methods in Ecology and Evolution , 2013, 4) a model for combining telemetry data with spatial capture–recapture (SCR) data that was vigorously criticized by Efford ( Methods in Ecology and Evolution , 2014, 000, 000). Efford's main claim was that our encounter probability model was incorrect, and therefore our R code and simulation results were wrong. 2. In fact, our encounter probability model is correct under the Poisson point process model that we used as a basis for our integrated model. On the other hand, the basis for Efford's claims clearly rest on the assumption of an alternative model which, while possibly useful, is distinct from that analysed in Royle et al. (Methods in Ecology and Evolution, 2013, 4). 3. A key point of Royle et al. ( Methods in Ecology and Evolution , 2013, 4) was that active resource selection induces heterogeneity in encounter probability which, if unaccounted for, should bias estimates of population size or density. The models of Royle et al. ( Methods in Ecology and Evolution , 2013, 4) and Efford ( Methods in Ecology and Evolution , 2014, 000, 000) merely amount to alternative models of resource selection, and hence varying amounts of heterogeneity in encounter probability.

Methods in Ecology and Evolution↗

USGS2012

Presence-only modeling using MAXENT: when can we trust the inferences?

1. Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence-only data and the deployment of these methods in user-friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence of random or representative sampling and that detectability during sampling does not vary with the covariates that determine occurrence probability. 2. Based on our interactions with researchers using these tools, we hypothesized that many presence-only studies were ignoring important assumptions of presence-only modelling. We tested this hypothesis by reviewing 108 articles published between 2008 and 2012 that used the MAXENT algorithm to analyse empirical (i.e. not simulated) data. We chose to focus on these articles because MAXENT has been the most popular algorithm in recent years for analysing presence-only data. 3. Many articles (87%) were based on data that were likely to suffer from sample selection bias; however, methods to control for sample selection bias were rarely used. In addition, many analyses (36%) discarded absence information by analysing presence–absence data in a presence-only framework, and few articles (14%) mentioned detection probability. We conclude that there are many misconceptions concerning the use of presence-only models, including the misunderstanding that MAXENT, and other presence-only methods, relieve users from the constraints of survey design. 4. In the process of our literature review, we became aware of other factors that raised concerns about the validity of study conclusions. In particular, we observed that 83% of articles studies focused exclusively on model output (i.e. maps) without providing readers with any means to critically examine modelled relationships and that MAXENT's logistic output was frequently (54% of articles) and incorrectly interpreted as occurrence probability. 5. We conclude with a series of recommendations foremost that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences can be made about clearly defined parameters such as occurrence probability.

Methods in Ecology and Evolution↗