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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]

Landscape ecology studies have demonstrated that past modifications of the landscape frequently influence its structure, highlighting the utility of integrating historical perspectives from the fields of historical ecology and environmental history. Yet questions remain for historically-informed landscape ecology, especially the relative influence of social factors, compared to biophysical factors, on long-term land-cover change. Moreover, methods are needed to more effectively link history to ecology, specifically to illuminate the underlying political, economic, and cultural forces that influence heterogeneous human drivers of land-cover change. In northern Wisconsin, USA, we assess the magnitude of human historical forces, relative to biophysical factors, on land-cover change of a landscape dominated by eastern white pine ( Pinus strobus L.) forest before Euro-American settlement. First, we characterize land-cover transitions of pine-dominant sites over three intervals (1860-1931; 1931-1951; 1951-1987). Transition analysis shows that white pine was replaced by secondary successional forest communities and agricultural land-covers. Second, we assess the relative influence of a socio-historical variable ('on-/off-Indian reservation'), soil texture (clay and sand), and elevation on land-cover transition. On the Lake Superior clay plain, models that combine socio-historical and biophysical variables best explain long-term land-cover change. The socio-historical variable dominates: the magnitude and rate of land-cover change differs among regions exposed to contrasting human histories. Third, we developed an integrative environmental history-landscape ecology approach, thereby facilitating linkage of observed land-cover transitions to broader political, economic, and cultural forces. These results are relevant to other landscape investigations that integrate history and ecology. [ABSTRACT FROM AUTHOR]

While spatial data analysis has received increasing attention in demographic studies, it remains a difficult subject to learn for practitioners due to its complexity and various unresolved issues. Here we give a practical guide to spatial demographic analysis, with a focus on the use of spatial regression models. We first summarize spatially explicit and implicit theories of population dynamics. We then describe basic concepts in exploratory spatial data analysis and spatial regression modeling through an illustration of population change in the 1990s at the minor civil division level in the state of Wisconsin. We also review spatial regression models including spatial lag models, spatial error models, and spatial autoregressive moving average models and use these models for analyzing the data example. We finally suggest opportunities and directions for future research on spatial demographic theories and practice. [ABSTRACT FROM AUTHOR]