Multistate modeling of habitat dynamics: factors affecting Florida scrub transition probabilities

Abstract

Many ecosystems are influenced by disturbances that create specific successional states and habitat structures that species need to persist. Estimating transition probabilities between habitat states and modeling the factors that influence such transitions have many applications for investigating and managing disturbance-prone ecosystems. We identify the correspondence between multistate capture-recapture models and Markov models of habitat dynamics. We exploit this correspondence by fitting and comparing competing models of different ecological covariates affecting habitat transition probabilities in Florida scrub and flatwoods, a habitat important to many unique plants and animals. We subdivided a large scrub and flatwoods ecosystem along central Florida's Atlantic coast into 10-ha grid cells, which approximated average territory size of the threatened Florida Scrub-Jay (Aphelocoma coerulescens), a management indicator species. We used 1.0-m resolution aerial imagery for 1994, 1999, and 2004 to classify grid cells into four habitat quality states that were directly related to Florida Scrub-Jay source-sink dynamics and management decision making. Results showed that static site features related to fire propagation (vegetation type, edges) and temporally varying disturbances (fires, mechanical cutting) best explained transition probabilities. Results indicated that much of the scrub and flatwoods ecosystem was resistant to moving from a degraded state to a desired state without mechanical cutting, an expensive restoration tool. We used habitat models parameterized with the estimated transition probabilities to investigate the consequences of alternative management scenarios on future habitat dynamics. We recommend this multistate modeling approach as being broadly applicable for studying ecosystem, land cover, or habitat dynamics. The approach provides maximum-likelihood estimates of transition parameters, including precision measures, and can be used to assess evidence among competing ecological models that describe system dynamics.

Effects of surrounding land use and water depth on seagrass dynamics relative to a catastrophic algal bloom

“…) and extensions to habitat modeling (Breininger et al. ) are detailed elsewhere. We estimated likelihoods, for example, by starting with the first 3 years and 2 data points for which we assumed no covariate or group effects.…”

“…An efficient alternative is to use maximum‐likelihood methods to model the entire set of site‐history data, where the data reflect the state of each site at each period of observation (Breininger et al. ).…”

Predicting the impact of fire on a vulnerable multi-species community using a dynamic vegetation model

“…Other studies have acknowledged the importance of modeling habitat dynamics and habitat management in a conservation context (Breininger et al, 2010;Johst et al, 2011;Mairota et al, 2014), but at most have linked dynamic landscape simulations to one animal species (e.g., Akç akaya et al, 2004Akç akaya et al, , 2005. Our model, however, is unusual in the literature in that it includes the direct and indirect impacts of fire on multiple vulnerable species and vegetation types under dynamic disturbance, all as functions of multiple management interventions.…”

The effects of vegetative type, edges, fire history, rainfall, and management in fire‐maintained habitat

“…Multistate models are new approaches to empirically quantify ecosystem dynamics by measuring states and using generalized linear models to estimate how environmental covariates influence transition probabilities between states (Breininger et al. , Veran et al. , Zweig and Kitchens ).…”

“…We previously used multistate models and multimodel inference to quantify scrub and flatwoods habitat transition probabilities of potential Florida scrub‐jay territories at 5‐yr intervals (1994–2004) using combinations of environmental factors that focused on vegetation type, edges, and management actions as covariates (Breininger et al. ). We used landscape units that defined potential Florida scrub‐jay territories because transition probabilities within potential territories provide the parameters to predict how alternative management actions can be used to better manage and restore the scrub and flatwoods ecosystem (Williams et al.…”