Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site‐level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. We demonstrate our framework using six synthetic occupancy data sets and two real data sets. Our results demonstrate how to model both traditional and nontraditional spatial dependence in occupancy data, which enables a broader class of spatial occupancy models that can be used to improve predictive accuracy and model adequacy.

Many exogenous factors may influence demographic rates (i.e., births, deaths, immigration, emigration), particularly for migratory birds that must cope with variable weather and habitat throughout their range and annual cycle. In midcontinental grasslands, disturbance (e.g., fire and grazing) and precipitation influence variation in grassland structure and function, but we know little about when and why precipitation is associated with grassland species' vital rates. We related estimates of detection, survival, and emigration to a priori sets of precipitation metrics to test the putative alternative factors influencing movement and mortality in grasshopper sparrows (Ammodramus savannarum). This species is a migratory songbird that exhibits exceptionally high rates of within-season and between-season dispersal. Between 2013 and 2020, we captured and resighted grasshopper sparrows in northeastern Kansas, USA, compiling capture histories for 1,332 adult males. We tested predictions of climatic hypotheses explaining variation in survival and emigration throughout a grasshopper sparrow's annual cycle; both survival and emigration were associated with the El Niño-Southern Oscillation precipitation index (ESPI). Survival was positively related with ESPI during winter, and temporary emigration was curvilinearly related to breeding season ESPI lagged 2 years, with the highest site fidelity associated with intermediate rainfall values. The relationship between rainfall and temporary emigration likely reflects the influence of weather over multiple years on

The ability of mesoscale environmental monitoring networks to collect spatially unbiased observations and to detect mesoscale environmental phenomena is radically determined by the spatial configuration of the network. However, there is lack of an objective, practical, and amenable method for guiding the spatial configuration of multifunctional, long-term mesoscale networks. The objective of this study is to present and demonstrate the application of a new method based on computational geometry that identifies the optimal location of future monitoring stations by finding the largest unmonitored area of the network. The computation of the method is first illustrated using the spatial distribution of the Kansas Mesonet as a case-study scenario and is then applied to several statewide and nationwide mesoscale networks in the United States. The proposed geometric method was effective to generate a long-term road map with the location of future monitoring stations. The geometric method seamlessly integrated with georeferenced data to identify the largest unmonitored areas with frequent occurrence of wildland fires and severe drought and to identify underrepresented soil types. Spatially dense statewide mesoscale networks with >120 stations across the studied U.S. states resulted in largest unmonitored areas of about 602 km2, whereas nationwide networks had largest unmonitored areas of 5002–6002 km2. The proposed method based on the geometric arrangement of network stations can be used by scientists, network managers, and state climatologists to improve the spatial representability of existing networks, better plan the allocation of limited resources, and increase the preparedness potential of mesoscale networks.

Species distribution models (SDMs) are increasingly used in ecology, biogeography, and wildlife management to learn about the species-habitat relationships and abundance across space and time. Distance sampling (DS) and capture-recapture (CR) are two widely collected data types to learn about species-habitat relationships and abundance; still, they are seldomly used in SDMs due to the lack of spatial coverage. However, data fusion of the two data sources can increase spatial coverage, which can reduce parameter uncertainty and make predictions more accurate, and therefore, can be used for species distribution modeling. We developed a model-based approach for data fusion of DS and CR data. Our modeling approach accounts for two common missing data issues: 1) missing individuals that are missing not at random (MNAR) and 2) partially missing location information. Using a simulation experiment, we evaluated the performance of our modeling approach and compared it to existing approaches that use ad-hoc methods to account for missing data issues.Our results show that our approach provides unbiased parameter estimates with increased efficiency compared to the existing approaches. We demonstrated our approach using data collected for Grasshopper Sparrows (Ammodramus savannarum) in north-eastern Kansas, USA.

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