A nonlinear mixed-effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example

Increasingly, often ecologist collects data with nonlinear trends, heterogeneous variances, temporal correlation, and hierarchical structure. Nonlinear mixed-effects models offer a flexible approach to such data, but the estimation and interpretation of these models present challenges, partly associated with the lack of worked examples in the ecological literature.We illustrate the nonlinear mixed-effects modeling approach using temporal dynamics of vegetation moisture with field data from northwestern Patagonia. This is a Mediterranean-type climate region where modeling temporal changes in live fuel moisture content are conceptually relevant (ecological theory) and have practical implications (fire management). We used this approach to answer whether moisture dynamics varies among functional groups and aridity conditions, and compared it with other simpler statistical models. The modeling process is set out "step-by-step": We start translating the ideas about the system dynamics to a statistical model, which is made increasingly complex in order to include different sources of variability and correlation structures. We provide guidelines and R scripts (including a new self-starting function) that make data analyses reproducible. We also explain how to extract the parameter estimates from the R output.Our modeling approach suggests moisture dynamic to vary between grasses and shrubs, and between grasses facing different aridity conditions. Compared to more classical models, the nonlinear mixed-effects model showed greater goodness of fit and met statistical assumptions. While the mixed-effects approach accounts for spatial nesting, temporal dependence, and variance heterogeneity; the nonlinear function allowed to model the seasonal pattern.Parameters of the nonlinear mixed-effects model reflected relevant ecological processes. From an applied perspective, the model could forecast the time when fuel moisture becomes critical to fire occurrence. Due to the lack of worked examples for nonlinear mixed-effects models in the literature, our modeling approach could be useful to diverse ecologists dealing with complex data.

From leaves to landscape: A multiscale approach to assess fire hazard in wildland-urban interface areas

The overlapping zone between urbanization and wildland vegetation, known as the wildland urban interface (WUI), is often at high risk of wildfire. Human activities increase the likelihood of wildfires, which can have disastrous consequences for property and land use, and can pose a serious threat to lives. Fire hazard assessments depend strongly on the spatial scale of analysis. We assessed the fire hazard in a WUI area of a Patagonian city by working at three scales: landscape, community and species. Fire is a complex phenomenon, so we used a large number of variables that correlate a priori with the fire hazard. Consequently, we analyzed environmental variables together with fuel load and leaf flammability variables and integrated all the information in a fire hazard map with four fire hazard categories. The Nothofagus dombeyi forest had the highest fire hazard while grasslands had the lowest. Our work highlights the vulnerability of the wildland-urban interface to fire in this region and our suggested methodology could be applied in other wildland-urban interface areas. Particularly in high hazard areas, our work could help in spatial delimitation policies, urban planning and development of plans for the protection of human lives and assets.