Biometrics & Modeling II

Contributed Oral Presentations

SESSION NUMBER: 91

Contributed paper sessions will be available on-demand for the duration of the conference, then again at the conclusion of the conference.

 

Spatiotemporal Covariation in Demographic Rates
Madeleine G. Lohman; Thomas Riecke; James Sedinger; Perry Williams
A fundamental tenant of wildlife ecology is that population dynamics and distributions change across time and space. Few studies have used spatiotemporal statistical models that formally incorporate capture-mark-recovery data to examine covariation in demographic rates. The population distribution of mallards (Anas platyrhyncos) in the Prairie Pothole Region (PPR) in the northern Great Plains has shifted dramatically over the past 60 years with changing agricultural patterns and climate. These spatial and temporal gradients of land-use, combined with natural heterogeneity on the landscape, create an ‘accidental experiment’ to see how changing environmental conditions affect broad-scale population dynamics. We examined covariation in survival and recruitment rates for different age and sex classes of mallards in the PPR from 1961-2015. Using release and harvest data from the USGS’s Bird Banding Lab, we modeled demographic rates with conditional autoregressive models in a Bayesian framework. Results showed a lack of spatial patterns in survival rates for all age and sex classes. However, age ratios tended to favor juveniles in the southeast PPR, corresponding to improved environmental conditions in the US associated with implementation of the Conservation Reserve Program. Following previous literature, this suggests that recruitment rather than survival drives population growth for mallards. Surprisingly, we found no temporal relationship between adult male survival rate and age ratio, reinforcing the need for an increased focus on breeding conditions in modeling population dynamics and growth, rather than harvest. Additionally, while we found a positive relationship between adult male and female survival, juvenile male and female survival did not demonstrate a detectable relationship. This may imply that different environmental factors mediate sex-specific differences in juvenile survival than those for adults and provides an area for further research.
What Is the Standard for Standardizing?
Frances Buderman
Standardizing covariates is a common tool used in statistics to facilitate comparison of effect sizes, guide specification of priors, and improve convergence and numerical stability of parameter estimates, particularly for Bayesian models. However, standardizing covariates, particularly when using hierarchical and Bayesian models, can be more complicated than expected, and details related to standardizing are frequently missing from the text of publications. Understanding and properly implementing models with standardized predictors is critical for making inference on ecological processes, from population demography to habitat selection. In this talk, I will discuss three complexities that I have encountered while fitting hierarchical Bayesian models with standardized covariates and offer recommendations and guidance for other researchers. First, I will discuss the inferential and computational benefits of group-means standardizing as an alternative to population-level standardizing. Using population-level standardizing (to a mean of zero and standard deviation of one across all observations) provides effect sizes in units relative to the entire span of the covariate; however, in space-use and movement modeling for multiple individuals, we are often more interested in an individual’s response to the range of a covariates it actually encountered. Second, one of the benefits of a Bayesian framework is that it allows one to model missing covariate data; however, standardizing of the covariate needs be done simultaneously with the model fitting, because the mean and standard deviation are unknown. Standardizing this covariate within an MCMC algorithm is easy with a custom sampler, but not all packages perform similarly well in dealing with this scenario. Finally, standardizing covariates can affect inference for model parameters in unexpected ways, and we must think carefully when combining standardized predictors with informative, ecologically motivated priors for other parameters (e.g., the intercept).
Modeling Successful Recolonization by An Apex Predator from Multiple Reintroduction Sites
Joseph M. Eisaguirre; Xinyi Lu; Mevin Hooten; William S. Beatty; Michelle L. Kissling; Perry J. Williams
Apex predator populations have declined globally, which has had cascading ecological effects. Reintroductions are often proposed and are becoming more frequently implemented to remedy these declines, maintain biological diversity, and recover ecosystem function. Many questions regarding the recolonization dynamics of predators remain, and understanding these dynamics and the factors influencing successful recolonizations will be key to implementing reintroductions. Sea otters (Enhydra lutris), an apex predator of nearshore marine ecosystems, were extirpated from much of their range by the early 20th century due to over harvest during the commercial fur trade. In southeast Alaska, 413 otters were translocated from remnant populations to seven sites across the region in the late 1960s. The population is now estimated to exceed 25,000. To help understand the dynamics influencing this successful recolonization, we implemented a spatiotemporal partial differential equation model representing ecological diffusion that integrates multiple types of data, incorporates multiple population epicenters (i.e. reintroduction sites), and accounts for density dependence. Modeling motility as a function of environmental covariates, including bathymetric variables and distance to human communities, allowed us to identify factors influencing successful colonization and persistence in certain areas. Additionally, tracking the rates of recolonization and the recolonization fronts through time allowed us to determine where connectivity exists between otter translocation sites, as well as forecast when it will occur for sites where the population is still disjunct. Further, these results show how multi-site reintroductions can drive successful recolonizations by apex predators over large geographic areas.
A Spatial Capture-Recapture Model for Group-Living Species
Robert L. Emmet; Ben Augustine; Briana Abrahms; Lindsey N. Rich; J.W. McNutt; Alan M. Wilson; Brett T. McClintock; Beth Gardner
Spatial capture-recapture (SCR) models have been applied to a large number of species, including those that are considered group-living species. Standard SCR models applied to group-living species assume that group members move independently, though this is not often the case. Recently, SCR models that include a group component have been proposed, thus allowing researchers to estimate density and home range sizes of individuals and groups. However, these models do not include explicit group movement dynamics. Group dynamics are an important part of the detection process, as individuals within groups likely have correlated movements, which may bias density estimates in group-living species. We developed a new SCR model for group-living species with an unknown number of groups. The model estimates the number of groups and group sizes using a clustering algorithm, assigning individuals to groups based on proximity to group activity centers. The model allows for the estimation of the density and home range sizes of both individuals and groups of animals. We examined the performance of this new model through a simulation study and applied the model to an empirical case study of African wild dogs in the Okavango Delta of Botswana. In this well-studied population of wild dogs, there is a known number of groups and individuals within each group. Standard SCR models were previously applied to this dataset and shown to perform well for estimating abundance. However, in our simulation study, we found that group movement dynamics can bias density estimates from standard SCR models. Thus, the new group SCR model promises to advance understanding of group movement dynamics and better estimate density of group-living species.
Fishing for Mammals: Using eDNA from Riverine Systems to Monitor Terrestrial and Semi-Aquatic Communities
Joseph Drake; Naira Sales; Maisie McKenzie; Lynsey Harper; Samuel Browett; Ilaria Coscia; Owen Wangensteen; Charles Baillie; Emma Bryce; Deborah Dawson; Erinma Ochu; Bernd Hänfling; Lori Handley; Stefano Mariani; Xavier Lambin; Chistopher Sutherland; Alla
Although environmental DNA (eDNA) has begun to revolutionize landscape level monitoring of many environments including marine and freshwater ecosystems, other applications remain relatively untested. We tested the efficiency of eDNA metabarcoding for detecting semi-aquatic and terrestrial mammals in a lotic ecosystem based on historical abundance data at a long-term ecological study site located in Northern Scotland. We also took eDNA sequence data recovered from both water and soil-based samples and compared these eDNA metabarcoding methods to other traditional methods including latrine surveys and camera trap monitoring. Using an occupancy modeling framework, we provide evidence of eDNA methods producing comparable results to the conventional methods based on a per-unit-of-effort for surveys using three focal species, including the water vole (Arvicola amphibious), a species of conservation concern. Furthermore, the eDNA methods were also able to detect a large proportion of the expected mammals in the study area, including other species often missed by traditional methods, but known to occur in the area. This method is promising, potentially allowing the construction of long-term “biodiversity libraries” from strategic sampling along multiple river courses within a watershed. However, along with potential benefits, we found several potential drawbacks to utilizing eDNA methods. Water based eDNA methods were far superior to soil based eDNA methods. As well, in order to conduct thorough surveys to capture wide-ranging meso-carnivores (such as otters and weasels), eDNA metabarcoding should be used alongside or in conjunction with other non-invasive surveying methods. This strategic sampling and tactical deployment of multiple surveying methods should maximize monitoring efforts.

 

Virtual
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