Impact of cattle grazing on the occupancy of a cryptic, threatened rail.

Impacts of livestock grazing in arid and semiarid environments are often concentrated in and around wetlands where animals congregate for water, cooler temperatures, and green forage. We assessed the impacts of winter-spring (November-May) cattle grazing on marsh vegetation cover and occupancy of a highly secretive marsh bird that relies on dense vegetation cover, the California Black Rail (Laterallus jamaicensis coturniculus), in the northern Sierra Nevada foothills of California, U.S.A. Using detection-nondetection data collected during repeated call playback surveys at grazed vs. ungrazed marshes and a "random changes in occupancy" parameterization of a multi-season occupancy model, we examined relationships between occupancy and habitat covariates, while accounting for imperfect detection. Marsh vegetation cover was significantly lower at grazed marshes than at ungrazed marshes during the grazing season in 2007 but not in 2008. Winter-spring grazing had little effect on Black Rail occupancy at irrigated marshes. However, at nonirrigated marshes fed by natural springs and streams, grazed sites had lower occupancy than ungrazed sites. Black Rail occupancy was positively associated with marsh area, irrigation as a water source, and summer vegetation cover, and negatively associated with marsh isolation. Residual dry matter (RDM), a commonly used metric of grazing intensity, was significantly associated with summer marsh vegetation cover at grazed sites but not spring cover. Direct monitoring of marsh vegetation cover, particularly at natural spring- or stream-fed marshes, is recommended to prevent negative impacts to rails from overgrazing.

Two-species occupancy models: a new parameterization applied to co-occurrence of secretive rails.

Two-species occupancy models that account for false absences provide a robust method for testing for evidence of competitive exclusion, but previous model parameterizations were inadequate for incorporating covariates. We present a new parameterization that is stable when covariates are included: the conditional two-species occupancy model, which can be used to examine alternative hypotheses for species' distribution patterns. This new model estimates the probability of occupancy for a subordinate species conditional upon the presence of a dominant species. It can also be used to test if the detection of either species differs when one or both species are present, and if detection of the subordinate species depends on the detection of the dominant species when both are present. We apply the model to test if the presence of the larger Virginia Rail (Rallus limicola) affects probabilities of detection or occupancy of the smaller California Black Rail (Laterallus jamaicensis coturniculus) in small freshwater marshes that range in size from 0.013 to 13.99 ha. We hypothesized that Black Rail occupancy should be lower in small marshes when Virginia Rails are present than when they are absent, because resources are presumably more limited and interference competition should increase. We found that Black Rail detection probability was unaffected by the detection of Virginia Rails, while, surprisingly, Black and Virginia Rail occupancy were positively associated even in small marshes. The average probability of Black Rail occupancy was higher when Virginia Rails were present (0.74 +/- 0.053, mean +/- SE) than when they were absent (0.36 +/- 0.069), and for both species occupancy increased with marsh size. Our results contrast with recent findings from patchy forest systems, where small birds were presumed to be excluded from small habitat patches by larger competitors.

Is the climate right for pleistocene rewilding? Using species distribution models to extrapolate climatic suitability for mammals across continents.

Species distribution models (SDMs) are increasingly used for extrapolation, or predicting suitable regions for species under new geographic or temporal scenarios. However, SDM predictions may be prone to errors if species are not at equilibrium with climatic conditions in the current range and if training samples are not representative. Here the controversial "Pleistocene rewilding" proposal was used as a novel example to address some of the challenges of extrapolating modeled species-climate relationships outside of current ranges. Climatic suitability for three proposed proxy species (Asian elephant, African cheetah and African lion) was extrapolated to the American southwest and Great Plains using Maxent, a machine-learning species distribution model. Similar models were fit for Oryx gazella, a species native to Africa that has naturalized in North America, to test model predictions. To overcome biases introduced by contracted modern ranges and limited occurrence data, random pseudo-presence points generated from modern and historical ranges were used for model training. For all species except the oryx, models of climatic suitability fit to training data from historical ranges produced larger areas of predicted suitability in North America than models fit to training data from modern ranges. Four naturalized oryx populations in the American southwest were correctly predicted with a generous model threshold, but none of these locations were predicted with a more stringent threshold. In general, the northern Great Plains had low climatic suitability for all focal species and scenarios considered, while portions of the southern Great Plains and American southwest had low to intermediate suitability for some species in some scenarios. The results suggest that the use of historical, in addition to modern, range information and randomly sampled pseudo-presence points may improve model accuracy. This has implications for modeling range shifts of organisms in response to climate change.