Predictive Occurrence Models for Bat Species in California

Abstract

The ability to accurately predict the potential occurrence of species of management concern is useful for wildlife managers, particularly for those whose management activities involve large areas where sampling is difficult due to logistical or financial constraints. During the summers of 2002 and 2003, we used mist nets to capture bats (Myotis yumanensis, M. californicus, M. evotis, M. thysanodes, Eptesicus fuscus, Lasionycteris noctivagans, Tadarida brasiliensis, Antrozous pallidus, Lasiurus borealis, and Lasiurus cinereus) in Whiskeytown National Recreation Area in north-central California, USA. We used landscape-scale variables, logistic regression, and Akaike's Information Criterion (AIC c ) to model species distributions and produce spatially discerning predictive occurrence maps. We developed a priori models that we used to determine which landscape-scale variables best discriminated between capture sites and non-capture sites. The odds of capturing a bat were 3.3 greater when total edge increased by 10,000 m, whereas for Yuma myotis (Myotis yumanensis), the odds of predicting presence were 0.2 greater when distance to lakes and ponds decreased by 2,000 m. Elevation was important in predicting the distribution of silver-haired bats (Lasionycteris noctivagans) and big brown bats (Eptesicus fuscus). Increasing elevation by 400 m decreased the odds of capturing a silver-haired bat by 0.1 and a big brown bat by 0.4. Classification accuracy for our models ranged from 80.9% for all bat species combined to 72.3% for Yuma myotis and silver-haired bats. Predictive occurrence models can be valuable to bat conservation efforts because they provide spatial data important for evaluating the effects of management activities on species distributions. (JOURNAL OF WILDLIFE MANAGEMENT 71 (3):693-700; 2007)

Comment on “Methods to account for spatial autocorrelation in the analysis of species distributional data: a review”

“…Interpolation to new environments (Bahn and McGill 2007) can be achieved with the autologistic model if one utilizes a Gibbs sampling estimation and prediction method (Augustin et al 1996, Wintle andBardos 2006). Such improved prediction success has been found in subsequent studies using autocovariate approaches (Hoeting et al 2000, Osborne et al 2001, He et al 2003, Knapp et al 2003, Duff and Morrell 2007, McPherson and Jetz 2007. However, because such methods tend to be computer intensive, many studies that use autocovariates do not use the Gibbs sampler making it difficult to predict independent data (Betts et al 2006).…”

Site occupancy of foraging bats on landscapes of managed pine forest

“…Results of previous studies have generally concluded that landscape characteristics, such as amount of forest cover, proximity to water, and edge, influence bat occupancy or foraging activity (Duff and Morrell, 2007;Ford et al, 2006;Grindal and Brigham, 1999;Walsh and Harris, 1996;Yates and Muzika, 2006). However, extent of influence and relevant landscape features are species-specific.…”

“…Conducting foraging studies on bats is difficult because of their small size, vagility, and nocturnal nature (Duff and Morrell, 2007). Because radiotelemetry is expensive and logistically challenging, most studies examining habitat use by foraging bats rely on acoustic methods (Morris et al, 2011b).…”

Akaike model selections of the vegetation structures and aerosphere factors in supporting lesser short nosed fruit bat (Cynopterus brachyotisMuller, 1838) populations in Asia mountainous paddy fields

“…Vegetation structures and aerosphere variables model were developed using Akaike Information Criterion (AIC) following Duff & Morrell (2007), Ingersoll (2010) and Hoffman et al (2019) to evaluate and assemble models of vegetation structure and C. brachyotis abundance. The AIC was developed using the linear regression.…”