Used-habitat calibration plots: a new procedure for validating species distribution, resource selection, and step-selection models

“Species distribution modeling” was recently ranked as one of the top five “research fronts” in ecology and the environmental sciences by ISI's Essential Science Indicators (Renner and Warton 2013), reflecting the importance of predicting how species distributions will respond to anthropogenic change. Unfortunately, species distribution models (SDMs) often perform poorly when applied to novel environments. Compounding on this problem is the shortage of methods for evaluating SDMs (hence, we may be getting our predictions wrong and not even know it). Traditional methods for validating SDMs quantify a model's ability to classify locations as used or unused. Instead, we propose to focus on how well SDMs can predict the characteristics of used locations. This subtle shift in viewpoint leads to a more natural and informative evaluation and validation of models across the entire spectrum of SDMs. Through a series of examples, we show how simple graphical methods can help with three fundamental challenges of habitat modeling: identifying missing covariates, non-linearity, and multicollinearity. Identifying habitat characteristics that are not well-predicted by the model can provide insights into variables affecting the distribution of species, suggest appropriate model modifications, and ultimately improve the reliability and generality of conservation and management recommendations

Partitioning Longleaf Pine Soil Respiration into Its Heterotrophic and Autotrophic Components through Root Exclusion

Rapid and accurate estimations of the heterotrophic and autotrophic components of total soil respiration (Rs) are important for calculating forest carbon budgets and for understanding carbon dynamics associated with natural and management-related disturbances. The objective of this study was to use deep (60 cm) root exclusion tubes and paired control (i.e., no root exclusion) collars to estimate heterotrophic respiration (Rh) and Rs, respectively, in three 26-year-old longleaf pine (Pinus palustris Mill.) stands in western Georgia. Root biomass was measured in root exclusion tubes and control collars after 102–104 days of incubation and fine root biomass loss from root exclusion was used to quantify root decay. Mean Rs from control collars was 3.3 micromol•CO2•m−2•s−1. Root exclusion tubes decreased Rs, providing an estimate of Rh. Mean Rh was 2.7 micromol•CO2•m−2•s−1 when uncorrected by pretreatment variation, root decay, or soil moisture compared to 2.1 micromol•CO2•m−2•s−1 when Rh was corrected for root decay. The corresponding ratio of Rh to Rs ranged from 66% to 82%, depending on the estimation method. This study provides an estimate of Rh in longleaf pine forests, and demonstrates the potential for deep root exclusion tubes to provide relatively rapid assessments (i.e., ~40 days post-treatment) of Rh in similar forests. The range in Rh to Rs is comparable to other reports for similar temperate coniferous ecosystems

Spatial Variability Of Soil Respiration In A 64-Year-Old Longleaf Pine Forest

Aims: The objectives of this study were to determine the spatial structure of soil respiration (Rs) in a naturally-regenerated longleaf pine forest and to assess the ecological factors affecting the spatial variability in Rs. Methods: Soil respiration, soil temperature (Ts), and soil moisture were repeatedly measured over 6 days in summer 2012 in 3 semi-independent plots. Edaphic, forest floor, and root variables were measured. Diameters of 338 trees were mapped. Spatial analysis and regression were applied. Results: Soil respiration was spatially autocorrelated across plots (66—92 m), but not within plots (6—34 m). Spatial distributions of Rs were relatively stable from morning through early evening and were decoupled from temporal variation of Ts. Ecological covariates (e.g., soil moisture, bulk density and carbon, litter mass, understory cover, roots, nearby trees) related to the spatial variability in Rs; however, models varied between plots. Conclusions: This study shows the importance of stationary plant and soil factors in determining the spatial, temperature-independent distribution of Rs in a heterogeneous forest. We suggest the need for a better understanding of the complex interactions between the heterotrophic, autotrophic, and physical processes driving Rs in order to better model forest carbon budgets

Time series sightability modeling of animal populations.

Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years

Population estimates by year.

<p>A: Population estimates () by year <i>t</i> and B: the log rate of change of between years from the modified Horvitz-Thompson (mHT) approach (grey bands and lines), the fixed-effect, model-based approach (FE model), and the temporal model-based approach (TS model). Error bars are 90% confidence intervals for mHT and 90% credible intervals for the TS and FE models.</p

Sensitivity to the amount of detection-only data.

<p>A: Sightability model curves demonstrating the relationship between predicted detection probability () and visual obstruction (<i>x</i>_{<i>h</i>,<i>i</i>,<i>j</i>,<i>t</i>}) for each moose group <i>j</i> in plot <i>i</i> in stratum <i>h</i> in year <i>t</i>, B: abundance estimates () by year with 90% credible intervals, and C: the posterior distribution of mean voc by strata <i>h</i> in 2006 () for the fixed-effect, joint model-based approach following Fieberg <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190706#pone.0190706.ref008" target="_blank">8</a>] with varying numbers of years of operational survey data included in the model. Gray bands and lines in (A) and (B) represent estimates and 90% confidence intervals for the sightability model curve and abundance estimates using the modified Horvitz-Thompson approach, respectively.</p

R Code and Output Supporting: Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models

Each example.html file (“example_”) uses various uhcplot functions (html files with description “code for function”). The example with the MN moose data (example_4_moose.html) also uses data for one Minnesota moose from 2013 and 2014 (in moose12687.csv). The tables (tables.html) are created with regression output files created and saved with the codes provided in the example.html files. The zipped folder (UHCPlotsPaper_R.zip) contains all of the Program R files (.R extension) for each of the html files. MNmoose_Arrowhead.pdf details the location of the moose from dataset. See the readme.txt for more information.Species distribution models (SDMs) are one of a variety of statistical methods that link individuals, populations, and species to the habitats they occupy. In Fieberg et al. "Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models", we introduce a new method for model calibration, which we call Used-Habitat Calibration plots (UHC plots) that can be applied across the entire spectrum of SDMs. Here, we share the Program R code and data necessary to replicate all three of the examples from the manuscript that together demonstrate how UHC plots can help with three fundamental challenges of habitat modeling: identifying missing covariates, non-linearity, and multicollinearity.This work was funded in part by the University of Minnesota-Twin Cities and the Minnesota Environment and Natural Resources Trust Fund. The Minnesota Department of Natural Resources assisted with collaring and monitoring of the moose

R Code and Output Supporting: Computational reproducibility in The Wildlife Society's flagship journals

A full description of the data and code files is provided in the attached README.txt. Briefly, each html file uses the raw data (or processed data) to create results and figures for the manuscript. The script in 01_processing_data.html should be run first before any other subsequent scripts can be run. The zipped folder (item K) contains all of the Program R files (.R extension) for each of the html files.The goal of this study was to gauge the level of computational reproducibility, which is the ability to reach the same results using the same data and analysis methods, in the field of wildlife sciences. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those for which we could obtain data, we attempted to reproduce their quantitative results using the original methods and data. The dataset shared in this repository is the de-identified results of our review, and the code provided here produces the results and figures in our published manuscript.Concordia College, Moorhead, Minnesota (Centennial Scholars Program