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We consider a continuous-time proportional hazards model for the analysis of ecological monitoring data where subjects are monitored at discrete times and fixed sites across space. Since the exact time of event occurrence is not directly observed, we rely on dichotomous event indicators observed at monitoring times to make inference about the model parameters. We use autoregression on the response at neighboring sites from a previous time point to take into account spatial dependence. The interesting fact is utilized that the probability of observing an event at a monitoring time when the underlying hazards is proportional falls under the class of generalized linear models with binary responses and complementary log-log link functions. Thus, a maximum likelihood approach can be taken for inference and the computation can be carried out using standard statistical software packages. This approach has significant computational advantages over some of the existing methods that rely on Monte Carlo simulations. Simulation experiments are conducted and demonstrate that our method has sound finite-sample properties. A real dataset from an ecological study that monitored bark beetle colonization of red pines in Wisconsin is analyzed using the proposed models and inference. Supplementary materials that contain technical details are available online. [ABSTRACT FROM AUTHOR]

Identifying how habitat use is influenced by environmental heterogeneity at different scales is central to understanding ungulate population dynamics on complex landscapes. We used resource selection functions (RSF) to study summer habitat use in a reintroduced and expanding elk (Cervus elaphus nelsoni) population in the Chequamegon National Forest, Wisconsin, USA. Factors were examined that influenced where elk established home ranges and that influenced habitat use within established home ranges. We also determined grain sizes over which elk responded to environmental heterogeneity and the number of categories of habitat selection from low to high that the elk distinguished. At a large spatial extent, elk home-range establishment was largely explained by the spatial distribution of wolf (Canis lupus) territories. Forage abundance was also influential but was relatively more important at a small spatial extent when elk moved within established home ranges. Areas near roads were avoided when establishing a home-range, but areas near roads were selected for use within the established home range. Elk distinguished among 4 different categories of habitat selection when establishing and moving within home ranges. Spatial and temporal cross validation demonstrated that to improve the predictive strength of habitat models in areas of low inter-annual variability in the environment, it is better to follow more individuals across diverse environmental conditions than to follow the same individuals over a longer time period. Last, our results show that the effects of environmental variables on habitat use were scale-dependent and reemphasize the necessity of analyzing habitat use at multiple scales that are fit to address specific research questions. [ABSTRACT FROM AUTHOR]

• PXRF spectra predict key soil properties within the profiles at depths. • Combination of EMI, Sentinel-2, and DEM maps soil texture, N, and soil depth. • Proximal and remote sensing delineate management zones for decision-making. • Proximal sensing data improve delineation of subsoil characteristics. The potential of a stepwise fusion of proximally sensed portable X-ray fluorescence (pXRF) spectra and electromagnetic induction (EMI) with remote Sentinel-2 bands and a digital elevation model (DEM) was investigated for predicting soil physicochemical properties in pedons and across a heterogeneous 80-ha crop field in Wisconsin, USA. We found that pXRF spectra with partial least squares regression (PLSR) models can predict sand, total nitrogen (TN), organic carbon (OC), silt contents, and clay with validation R2 of 0.81, 0.74, 0.73, 0.68, and 0.64 at the pedon scale but performed less well for soil pH (R2 = 0.51). A combination of EMI, Sentinel-2, and DEM data showed promise in mapping sand, silt contents, and TN at two depths and Ap horizon thickness and soil depth across the field. A clustering analysis using combinations of mapped soil properties or proximal and remote sensing data suggested that data fusion improved the characterization of field-scale variability of soil properties. The cost-benefit analysis showed that the most accurate management zones (MZs) for topsoil can be generated only using estimated soil property maps while it was the most costly as compared to other data sources. For an intermediate-high (for topsoil) and high (subsoil) accuracy and a moderate economic budget, the combination of sensors (proximal + remote sensing + DEM) might be a better approach for effective MZs generation than collecting soil samples for laboratory analysis while the latter produced the most accurate maps for topsoil. It can be concluded that pXRF spectra can be useful for predicting key soil properties (e.g., sand, TN, OC, silt, clay) at different soil depths, and a combination of proximal and remote sensing provides an effective way to delineate soil MZs that are useful for decision-making. [ABSTRACT FROM AUTHOR]