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Accurate population estimates are essential for monitoring and managing wildlife populations. Mark–recapture sampling methods have regularly been used to estimate population parameters for rare and cryptic species, including the federally listed Mojave desert tortoise (Gopherus agassizii); however, the methods employed are often plagued by violations of statistical assumptions, which have the potential to bias density estimates. By incorporating spatial information into conventional density estimation models, spatial capture–recapture (SCR) models can account for common assumption violations such as spatially heterogeneous detection probabilities and temporary emigration when animals leave plots during a survey. We conducted mark–recapture surveys at 10 1‐km2 plots in and adjacent to the Ivanpah Valley of California and Nevada from 2015 to 2019. Locality data were collected concurrently using radio‐telemetry and GPS data loggers. GPS data demonstrated that desert tortoises frequently exhibited temporary emigration outside a plot during the survey periods, thereby complicating standard approaches for closed‐model density estimation. We integrated mark–recapture survey data for subadults and adults at each plot with corresponding spatial capture locations and supplementary spatial data using a modified SCR model fitted in a Bayesian framework. We compared density estimates modeled with conventional non‐spatial methods, as well as three SCR models based on symmetrical usage areas described by various levels and types of supplementary spatial data. The conventional model consistently resulted in inflated estimates of density while the SCR models allowed us to generate spatially corrected estimates for a species where detectability and densities are low. However, we found that if not properly specified, the temporal scale of supplementary data may result in an unintended source of bias in parameter estimates. Integrating spatial data over a larger temporal scale than mark–recapture surveys were conducted resulted in higher detection probabilities and lower density estimates, due to an overestimation of space use. Our results not only demonstrate the importance of accounting for spatial information but also the value of understanding the potential for bias when integrating multiple data sets at different temporal resolutions. The methods presented can be used to enhance monitoring efforts for the Mojave desert tortoise and other species where mark–recapture methods are used.

A key assumption in species distribution modeling (SDM) with presence‐background (PB) methods is that sampling of occurrence localities is unbiased and that any sampling bias is proportional to the background distribution of environmental covariates. This assumption is rarely met when SDM practitioners rely on federated museum records from natural history collections for geo‐located occurrences due to inherent sampling bias found in these collections. We use a simulation approach to explore the effectiveness of three methods developed to account for sampling bias in SDM with PB frameworks. Two of the methods rely on careful filtering of observation data—geographic thinning (G‐Filter) and environmental thinning (E‐Filter)—while a third, FactorBiasOut, creates selection weights for background data to bias locations toward areas where the observation dataset was sampled. While these methods have been assessed previously, evaluation has emphasized spatial predictions of habitat potential. Here, we dig deeper into the effectiveness of these methods by exploring how sampling bias not only affects predictions of habitat potential, but also our understanding of niche characteristics such as which explanatory variables and response curves best represent species–environment relationships. We simulate 100 virtual species ranging from generalist to specialist in their habitat preferences and introduce geographic and environmental bias at three intensity levels to measure the effectiveness of each correction method to (1) predict true probability of occurrence across a study area, (2) recover true species–environment relationships, and (3) identify true explanatory variables. We find that the FactorBiasOut most often showed the greatest improvement in recreating known distributions but did no better at correctly identifying environmental covariates or recreating species–environment relationships than G‐Filter or E‐Filter methods. Narrow niche species are most problematic for biased calibration datasets, such that correction methods can, in some cases, make predictions worse.

Habitat disturbance from development, resource extraction, off-road vehicle use, and energy development ranks highly among threats to desert systems worldwide. In the Mojave Desert, United States, these disturbances have promoted the establishment of nonnative plants, so that native grasses and forbs are now intermixed with, or have been replaced by invasive, nonnative Mediterranean grasses. This shift in plant composition has altered food availability for Mojave Desert tortoises (Gopherus agassizii), a federally listed species. We hypothesized that this change in forage would negatively influence the physiological ecology, immune competence, and health of neonatal and yearling tortoises. To test this, we monitored the effects of diet on growth, body condition, immunological responses (measured by gene transcription), and survival for 100 captive Mojave tortoises. Tortoises were assigned to one of five diets: native forbs, native grass, invasive grass, and native forbs combined with either the native or invasive grass. Tortoises eating native forbs had better body condition and immune functions, grew more, and had higher survival rates (>95%) than tortoises consuming any other diet. At the end of the experiment, 32% of individuals fed only native grass and 37% fed only invasive grass were found dead or removed from the experiment due to poor body conditions. In contrast, all tortoises fed either the native forb or combined native forb and native grass diets survived and were in good condition. Health and body condition quickly declined for tortoises fed only the native grass (Festuca octoflora) or invasive grass (Bromus rubens) with notable loss of fat and muscle mass and increased muscular atrophy. Bromus rubens seeds were found embedded in the oral mucosa and tongue in most individuals eating that diet, which led to mucosal inflammation. Genes indicative of physiological, immune, and metabolic functions were transcribed at lower levels for individuals fed B. rubens, indicating potential greater susceptibility to disease or other health-related problems. This study highlights the negative indirect effects of invasive grasses, such as red brome, in desert ecosystems, and provides definitive evidence of a larger negative consequence to health, survival, and ultimately population recruitment for Mojave Desert tortoises than previously understood.