Research about Baker County, Georgia

USGS2025

Interspecific interactions moderate direct effects of vegetation change resulting from prescribed fires

Savannas depend on frequent, low-intensity fires that shape animal and plant communities. These fires alter animal populations, movement, and habitat use. Here, we report on how fires in a longleaf pine (Pinus palustris) savanna affected small mammal microhabitat use via changes in competition and predation. We monitored small mammal populations and vegetation subjected to biennial prescribed fires and compared microhabitat use of three small mammal populations [hispid cotton rats (Sigmodon hispidus), cotton mice (Peromyscus gossypinus) and oldfield mice (Peromyscus polionotus)] in the presence and absence of mesocarnivores while accounting for changes in density and movement of each small mammal species. Densities of cotton rats varied greatly across years but were similar between predator exclosures and controls. However, frequency of use was greater in exclosures than in controls irrespective of vegetation characteristics, suggesting predation risk altered cotton rat microhabitat use. Conversely, higher relative abundance of cotton rats was associated with lower cotton mouse and oldfield mouse use, suggesting spatial separation in niche and indicating that cotton mice expand their realized niche following predation-induced declines of cotton rats associated with prescribed burn events. Our results contribute to a better understanding of pyrodiversity and how interspecific interactions can moderate effects of vegetation changes following prescribed fires.

Georgia↗

USGS2022

Accounting for spatial heterogeneity in visual obstruction in line-transect distance sampling of gopher tortoises

Line-transect distance sampling (LTDS) surveys are commonly used to estimate abundance of animals or objects. In terrestrial LTDS surveys of gopher tortoise ( Gopherus polyphemus ) burrows, the presence of ground-level vegetation substantially decreases detection of burrows of all sizes, but no field or analytical methods exist to control for spatially heterogeneous vegetation obstruction as a source of variation in detection. We propose the addition of a simple measurement of ground-level vegetation that serves as a covariate for the detection function. We present a Bayesian hierarchical model in which covariates burrow width and nearby vegetation height help to account for detection bias and improve precision of estimated density. We investigate the performance of this covariate by simulation and by using real LTDS data collected before and after application of prescribed fire. We collected data in 2018 at the Jones Center at Ichauway in Newton, Georgia, USA. Across all simulations, our model including both covariates produced the most accurate density point estimates of any of the models tested. For our case study, our Bayesian model with vegetation covariates tended to produce similar estimates of density before and after burns. Our study indicates that any level of spatial variation in vegetation obstruction decreases detection of burrows and may lead to underestimation in population size (≤68%) and proportion of individuals with small burrow sizes (≤32%) when not considered during analysis. Our work is extensible to other terrestrial sampling efforts where systematic measurement of a spatially distributed obstructing feature is feasible during the LTDS survey.

Georgia↗