Region-Wide Ecological Responses of Arid Wyoming Big Sagebrush Communities to Fuel Treatments

If arid sagebrush ecosystems lack resilience to disturbances or resistance to annual invasives, then alternative successional states dominated by annual invasives, especially cheatgrass (Bromus tectorum L.), are likely after fuel treatments. We identified six Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis Beetle & Young) locations (152–381 mm precipitation) that we believed had sufficient resilience and resistance for recovery. We examined impacts of woody fuel reduction (fire, mowing, the herbicide tebuthiuron, and untreated controls, all with and without the herbicide imazapic) on short-term dominance of plant groups and on important land health parameters with the use of analysis of variance (ANOVA). Fire and mowing reduced woody biomass at least 85% for 3 yr, but herbaceous fuels were reduced only by fire (72%) and only in the first year. Herbaceous fuels produced at least 36% more biomass with mowing than untreated areas during posttreatment years. Imazapic only reduced herbaceous biomass after fires (34%). Tebuthiuron never affected herbaceous biomass. Perennial tall grass cover was reduced by 59% relative to untreated controls in the first year after fire, but it recovered by the second year. Cover of all remaining herbaceous groups was not changed by woody fuel treatments. Only imazapic reduced significantly herbaceous cover. Cheatgrass cover was reduced at least 63% with imazapic for 3 yr. Imazapic reduced annual forb cover by at least 45%, and unexpectedly, perennial grass cover by 49% (combination of tall grasses and Sandberg bluegrass [Poa secunda J. Presl.]). Fire reduced density of Sandberg bluegrass between 40% and 58%, decreased lichen and moss cover between 69% and 80%, and consequently increased bare ground between 21% and 34% and proportion of gaps among perennial plants &spigt; 2 m (at least 28% during the 3 yr). Fire, mowing, and imazapic may be effective in reducing fuels for 3 yr, but each has potentially undesirable consequences on plant communities

Appendix A. A summary of detections of birds during the pre-treatment year (1994), post-treatment years (1995–2000), and all years combined, as well as a complete species list.

A summary of detections of birds during the pre-treatment year (1994), post-treatment years (1995–2000), and all years combined, as well as a complete species list

Appendix B. Calculations of overall probability of detection g at Foote Creek Rim (Young et al. 2003).

Calculations of overall probability of detection g at Foote Creek Rim (Young et al. 2003)

Appendix A. Expectation and variance of overall probability of detection g across multiple independent areas or time periods.

Expectation and variance of overall probability of detection g across multiple independent areas or time periods

Supplement 1. R code to calculate posterior distribution of M, total fatality at a wind facility when zero carcasses are observed.

<h2>File List</h2><div> <p><a href="Xeq0_Rcode.R">Xeq0_Rcode.R</a> (MD5: 0189df9bc7f877f78943a34f377c950c) </p> </div><h2>Description</h2><div> <p>The Xeq0_Rcode.R file is an R script file that calculates the posterior distribution of M, the total fatality at a wind facility, assuming observed count of carcasses=0. The posterior distribution is a function of the prior, the overall probability of detecting a carcass killed at the facility (g), and the number of carcasses counted during the search process (x). Since we are assuming x = 0, we only need posterior distributions for each combination of g and prior. For a given prior, the code below calculates an array with a posterior for each value of g. There are three possibilities for g: fixed, uncertain (1.732x), highly uncertain (3x). The degree of uncertainty about g is given in terms of the width of the CI in terms of odds ratios: if Upr and Lwr are upper and lower bounds on CI for g, then odds(Upr)/odds(Lwr) is a measure of uncertainty about g.</p> </div

A review of supervised learning methods for classifying animal behavioural states from environmental features

Abstract Accurately predicting behavioural modes of animals in response to environmental features is important for ecology and conservation. Supervised learning (SL) methods are increasingly common in animal movement ecology for classifying behavioural modes. However, few examples exist of applying SL to classify polytomous animal behaviour from environmental features especially in the context of millions of animal observations. We review SL methods (weighted k‐nearest neighbours; neural nets; random forests; and boosted classification trees with XGBoost) for classifying polytomous animal behaviour from environmental predictors. We also describe tuning parameter selection and assessment strategies, approaches for visualizing relationships between predictors and class outputs, and computational considerations. We demonstrate these methods by predicting three categories of risk to bald eagles from colliding with wind turbines using, as predictors, 12 environmental state features associated with 1.7 million GPS telemetry data points from 57 eagles. Of the SL methods we considered, XGBoost yielded the most accurate model with 86.2% classification accuracy and pairwise‐averaged area under the ROC curve of 90.6. Computational time of XGBoost scaled better to large data than any other SL method. We also show how SHAP values integrated in the R package (xgboost) facilitate investigation of variable relationships and importance. For big data applications, XGBoost appears to provide superior classification accuracy and computational efficiency. Our results suggest XGBoost should be considered as an early modelling option in situations where the intent is to classify millions of animal behaviour observations from environmental predictors and to understand relationships between those predictors and movement behaviours. We also offer a tutorial to assist researchers in implementing this method

Mortality estimation from carcass searches using the R-package carcass — a tutorial

This article is a tutorial for the R-package carcass. It starts with a short overview of common methods used to estimate mortality based on carcass searches. Then, it guides step by step through a simple example. First, the proportion of animals that fall into the search area is estimated. Second, carcass persistence time is estimated based on experimental data. Third, searcher efficiency is estimated. Fourth, these three estimated parameters are combined to obtain the probability that an animal killed is found by an observer. Finally, this probability is used together with the observed number of carcasses found to obtain an estimate for the total number of killed animals together with a credible interval