Geology topics

Erica F. Stuber

Publications and source records attributed to Erica F. Stuber.

5 recordsLinked to original sources

USGS2019

How characteristic is the species characteristic selection scale?

Aim The importance of framing investigations of organism–environment relationships to interpret patterns at relevant spatial scales is increasingly recognized. However, most research related to environmental relationships is single-scaled, implicitly or explicitly assuming that a “species characteristic selection scale” exists. We tested the premise that a single characteristic scale exists to understand species–environment relationships within species by asking (a) what are the characteristic scales of species’ relationships with environmental predictors, and (b) is within-species, cross-predictor consistency in characteristic scales a general phenomenon. Location Nebraska, USA. Time period 2016. Major taxa studied Birds. Methods We used data from 86 species at > 500 locations to build hierarchical N-mixture models relating species abundance to land cover variables. By incorporating Bayesian latent indicator scale selection, we identified the spatial scales that best explain species–environment relationships with each land cover predictor. We quantified the extent of cross-predictor consistency in characteristic scales, and contrasted this to the expectation given a single species’ characteristic scale. Results We found no evidence for a characteristic spatial scale explaining all abundance–environment relationships within species, rather we found substantial variation in scale-dependence across multiple environmental attributes. Furthermore, 33% of species displayed evidence of multiple important spatial scales within environmental attributes. Major conclusions Within species there is little evidence for a single characteristic scale of environmental relationships and considerable variation in species’ scale dependencies. Because species may respond to multiple environmental attributes at different spatial scales, or single environmental attributes at multiple scales, we caution against any unoptimized single-scale studies. Our results demonstrate that until a framework is developed to predict the scales at which species respond to environmental characteristics, multi-scale investigations must be performed to identify and account for multi-scale dependencies. Natural selection acting on species’ response to distinct environmental attributes, rather than natural selection acting on species’ perception of spatial scales per se, may have shaped patterns of scale dependency and is an area ripe for investigation.

Nebraska↗

USGS2018

Ecological neighborhoods as a framework for umbrella species selection

Umbrella species are typically chosen because they are expected to confer protection for other species assumed to have similar ecological requirements. Despite its popularity and substantial history, the value of the umbrella species concept has come into question because umbrella species chosen using heuristic methods, such as body or home range size, are not acting as adequate proxies for the metrics of interest: species richness or population abundance in a multi-species community for which protection is sought. How species associate with habitat across ecological scales has important implications for understanding population size and species richness, and therefore may be a better proxy for choosing an umbrella species. We determined the spatial scales of ecological neighborhoods important for predicting abundance of 8 potential umbrella species breeding in Nebraska using Bayesian latent indicator scale selection in N-mixture models accounting for imperfect detection. We compare the conservation value measured as collective avian abundance under different umbrella species selected following commonly used criteria and selected based on identifying spatial land cover characteristics within ecological neighborhoods that maximize collective abundance. Using traditional criteria to select an umbrella species resulted in sub-maximal expected collective abundance in 86% of cases compared to selecting an umbrella species based on land cover characteristics that maximized collective abundance directly. We conclude that directly assessing the expected quantitative outcomes, rather than ecological proxies, is likely the most efficient method to maximize the potential for conservation success under the umbrella species concept.

Biological Conservation↗

USGS2017

Translating statistical species-habitat models to interactive decision support tools

Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habitat model, a regression analysis relating ring-necked pheasant abundance to landcover, into an interactive online tool. The Pheasant Habitat Simulator combines the analytical power of the R programming environment with the user-friendly Shiny web interface to create an online platform in which wildlife professionals can explore the effects of variation in local landcover on relative pheasant habitat suitability within spatial scales relevant to individual wildlife managers. Our tool allows users to virtually manipulate the landcover composition of a simulated space to explore how changes in landcover may affect pheasant relative habitat suitability, and guides users through the economic tradeoffs of landscape changes. We offer suggestions for development of similar interactive applications and demonstrate their potential as innovative science delivery tools for diverse professional and public audiences.

PLoS ONE↗

USGS2017

A Bayesian method for assessing multiscalespecies-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

Nebraska↗

USGS2017

Species distributions models in wildlife planning: agricultural policy and wildlife management in the great plains

We know economic and social policy has implications for ecosystems at large, but the consequences for a given geographic area or specific wildlife population are more difficult to conceptualize and communicate. Species distribution models, which extrapolate species-habitat relationships across ecological scales, are capable of predicting population changes in distribution and abundance in response to management and policy, and thus, are an ideal means for facilitating proactive management within a larger policy framework. To illustrate the capabilities of species distribution modeling in scenario planning for wildlife populations, we projected an existing distribution model for ring-necked pheasants ( Phasianus colchicus ) onto a series of alternative future landscape scenarios for Nebraska, USA. Based on our scenarios, we qualitatively and quantitatively estimated the effects of agricultural policy decisions on pheasant populations across Nebraska, in specific management regions, and at wildlife management areas.

Nebraska↗