Early Successional Forest

Conversion of tropical forests to agriculture affects vertebrate assemblages, but we do not know how fast or to what extent these
assemblages recover after field abandonment. We addressed this question by examining amphibians and reptiles in secondary forests in
southeastern Mexico. We used chronosequence data (12 secondary forests fallow for 1–23 yr and 3 old-growth forest sites) to analyze
successional trajectories and estimate recovery times of assemblage attributes for amphibians and reptiles. We conducted 6 surveys at
each site over 14 mo (1200 person-hours) and recorded 1552 individuals, including 25 species of amphibians and 36 of reptiles, representing
96 and 74 percent of the expected regional number of species, respectively. Abundance, species richness, and species diversity of
amphibians increased rapidly with successional age, approaching old-growth forest values in < 30 yr. Species richness and species diversity
of reptiles reached old-growth forest values in < 20 yr. By contrast, the abundance of reptiles and the assemblage composition of
amphibians and reptiles recovered more slowly. Along the chronosequence, we observed more species replacement in reptile assemblages
than in amphibian assemblages. Several species in the old-growth forest were absent from secondary forests. Dispersal limitation
and harsh conditions prevailing in open sites and early successional environments appear to preclude colonization by old-growth forest
species. Furthermore, short fallow periods and isolation of forest remnants lead to the formation of new assemblages dominated by species
favored by human disturbances.

Wildlife organizations often engage landowners in habitat management. Landowner
typology research can provide suggestions for how to work with diverse types of
landowners. We explored how typologies can inform selection of tools to engage
landowners in early successional habitat (ESH) management. Using a survey, effectiveness
of three kinds of tools were assessed: (a) basic needs, (b) learning, and (c) social.
Across all types and typologies, learning tools were most likely to influence landowner
behavior, whereas social tools (e.g., recognition) were least likely. Continuing adopters
were the only type of landowners that reported basic needs tools would influence them at
the same level as learning tools. In each typology, landowner types with higher behavioral
intention were more likely to be influenced by all of the tools. Thus, tools may
reinforce behaviors, rather than engage the unengaged. We suggest learning tools be
initially prioritized to encourage ESH management.

Understanding forest complexity and self-organization across multiple scales is essential for both ecology and natural resource management. In this paper, we develop a Markov chain approach for the modelling of forest stand dynamics. The aim of this work is to generalize the recently developed Perfect Plasticity Approximation (PPA) model for scaling of vegetation dynamics from individual level to the landscape level through the ecosystem hierarchical structure. Our basic assumption is that the forested ecosystem and disturbance regimes can be modelled on 3 hierarchical scales (levels): individual trees, forest stand (or patch, defined as a spatial unit about 0.5e1 ha of the same forest at one successional stage.) and landscape (collection of forest patches of different forest/soil types at different successional stages) levels. In our modelling approach the PPA model is an intermediate step for scaling from the individual level to the forest stand level (or patch level). In this paper we develop a Markov chain model for stage-structured dynamics of forest stands (patches). In order to determine the structure of the Markov chain model and estimate parameters, we analyze the patch-mosaic patterns of forest stands of the Lake States (MI, WI, and MN) recorded in the USDA FIA database as well as data for other US states and Canada. The distribution of macroscopic characteristics of a large collection of forest patches is considered as an estimate of the stationary distribution of the underlying Markov chain. The data demonstrates that this distribution is unimodal and skewed to the right. We identify the simplest Markov chain that produces such a distribution and estimate the upper bound of the probability of disaster for this Markov chain.