Abstract
BackgroundThe influence of vegetative changes due to livestock grazing on grassland birds is well-recognized because these birds are heavily influenced by vegetative structure. Traditionally, species distribution models (SDMs) use direct variables, resources that the animal consumes or requires to persist in an area (e.g., water) to define and project a species’ niche and distribution. Indirect variables, which are features the animal does not consume or require for persistence but with which it may still interact, are often excluded. Our objective was to improve the traditional SDMs projecting the distribution of three summer resident South Texas grassland birds (Northern Bobwhite Colinus virginianus, Eastern Meadowlark Sturnella magna, and Cassin’s Sparrow Peucaea cassinii) by incorporating livestock grazing pressure, an indirect variable, into five SDM algorithms: BioClim, generalized linear model, MaxEnt, boosted regression tree, and random forest. We collected data from the Coloraditas Grazing Research and Demonstration Area (CGRDA), a 7684-ha area located on the San Antonio Viejo Ranch (SAV) in South Texas. We used several relevant environmental characteristics to build SDMs and compared model performance (AUC and TSS) with and without grazing pressure as an indirect variable.ResultsMachine learning models (MaxEnt and random forest) had the highest predictive performance for all species, with random forest being the most consistent for each analysis. BioClim and generalized linear model remained constant or only marginally improved with the addition of the grazing pressure.ConclusionsOur findings suggest that model selection for SDM should include consideration of species prevalence, and machine-learning algorithms should be preferred when the target species is of low or unknown prevalence. Further, livestock grazing has measurable influence on grassland bird species’ distributions and should be included in SDMs as an indirect variable in addition to associated vegetative changes.
Highlights
The influence of vegetative changes due to livestock grazing on grassland birds is well-recognized because these birds are heavily influenced by vegetative structure
Our objective was to (1) improve traditional species distribution models (SDMs) projecting the distribution of three summer resident South Texas grassland birds (Northern Bobwhite Colinus virginianus, Eastern Meadowlark Sturnella magna, and Cassin’s Sparrow Peucaea cassinii) by incorporating livestock grazing pressure, an indirect variable, and (2) interpret the possible effect of grazing pressure on bird distribution per each SDM approach
The predictive power of both machine learning models and the boosted regression tree (BRT) improved with the addition of the grazing pressure raster for all species, with the exception of MaxEnt and Eastern Meadowlark [Maxent: Northern Bobwhite [ΔAUC = +0.06], Cassin’s Sparrow [ΔAUC = +0.02]; random forest: Northern Bobwhite [ΔAUC = +0.01], Eastern Meadowlark [ΔAUC = +0.05], Cassin’s Sparrow [ΔAUC = +0.02]; random forest: Northern Bobwhite [ΔAUC = +0.03], Eastern Meadowlark [ΔAUC = +0.04], Cassin’s Sparrow [ΔAUC = +0.03]
Summary
The influence of vegetative changes due to livestock grazing on grassland birds is well-recognized because these birds are heavily influenced by vegetative structure. Landowners can anticipate an average gross profit of $4.69 per hectare for a deer or exotic ungulate hunting lease and can expect an average gross profit of $20.99 per hectare for a quail (e.g., Northern Bobwhite Colinus virginianus and Scaled Quail Callipepla squamata) hunting lease (TPWD 2017) This area provides crucial resources for other migratory and resident grassland birds (e.g., Cassin’s Sparrow Aimophila cassinii, Grasshopper Sparrow Ammodramus savannarum, and Dicksissel Spiza americana) that have declined throughout their ranges due to land use and climate change since 1966 (Brennan and Kuvlesky Jr 2005; Knopf 1994). It is essential we advance our understanding of how grassland birds are affected by their environment, inclusive of both their requirements to persist (i.e., resources), and how they interact with environmental features or biotic influences
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