Parallel individual-based modeling of Everglades deer ecology

Abstract

Q tb 1s.Individual-based models of animal ecology can take into account spatio-temporal details of a natural environment that are overly simplijied in models based on averaged variables. Parallelizing a model that simulates long-term populations of deer and panthers in the Everglades region of south Florida allows faster computation of complex scenarios and better exploration of hydrologic management alternatives.he Florida Everglades is one of the most threatened ecosystems in the United States, owing in part to decades of adverse impacts from intensive water management that has favored agricultural and urban development more than the natural ecology.' Resource experts are currently developing management alternatives for restoring natural water flows to the area. They need to predict and compare the effects of alternative hydrologic planning scenarios on the Everglades wildlife, and can realistically project these effects only by computer modeling.2 Individual-based modelsThis article describes an application of modern computational methods to a model based upon the actions of individual organisms, in which the assumptions about the growth, reproduction, mortality, and behavior of the individuals are closely linked to observable biology. Most of the mathematical models developed to analyze ecological problems contain highly aggregated variables, in which details of individuals (age, size, location, and so forth) are averaged, leading to a simplified set of differential or difference equations.Though useful for general theory development, these models are difficult to use for real applications as they generally ignore the spatio-temporal patterns of underlying environmental factors affecting the natural system. Moreover, the aggregated nature of the component variables makes the model parameters difficult to estimate from observations. The individual-based approach, on the other hand, readily allows one to take account of the detailed information available on behavioral and physiological responses of individuals to their environment, llnk this with detailed models for environmental variations in space and time, estimate the longer-term and largerspatial-scale response of populations and communities, and validate the results through comparisons with both individual behavioral data and population estimate^.^ Simpdel modelThe Simpdel model (spatially explicit individual-based simulation model of Florida panther and white-tailed deer in the Everglades and Big Cypress landscapes) was jointly developed by a group of modelers and biologists at the Institute for Environmental Modeling, University of Tennes~ee.~ Simpdel is a member of the ATLSS family of models (Across-Trophic-Level System Simulation for the Everglades and Big Cypress Swamp) under development at a variety of institutions with support from 60

Agent‐based Modeling of Animal Movement: A Review

“…Turner et al (1994) simulated the wintering behavior of large ungulates (including elk and bison) whose foraging behavior is driven by daily intake requirements and intake rates. Abbott et al (1997) investigated the response of white-tailed deer to an everglades landscape. The searching and movement behavior of deer is controlled by their maximum daily requirement and the forage level of the cells.…”

“…We highlight here two specific challenges that we believe are particularly important to the advancement of ABM for animal movement: spatial adaptation and multi-scale environmental representation. For discussion on validation, the readers are directed to Brown et al (2005a), Moss and Edmonds (2005), and Windrum et al (2007); for computation, please refer to Abbott et al (1997), Armstrong (2000), Wang et al (2006), and Tang and Wang (2009).…”

“…(1994) simulated the wintering behavior of large ungulates (including elk and bison) whose foraging behavior is driven by daily intake requirements and intake rates. Abbott et al. (1997) investigated the response of white‐tailed deer to an everglades landscape.…”

“…(2005a), Moss and Edmonds (2005), and Windrum et al. (2007); for computation, please refer to Abbott et al. (1997), Armstrong (2000), Wang et al.…”

From landscapes to waterscapes: A PSE for landuse change analysis

“…The larger problem of misclassified and unlabeled data caused by out-of-date field measurements and lack of knowledge of precise commercial and vegetation boundaries is endemic to this domain; in the future, we plan to make use of machine learning techniques [11] to aid in automatic landuse segmentation. This is related to the broader task of map analysis using GIS data, a problem that has received much attention in areas such as identifying clusters of wild life behavior in forests [7], modeling population dynamics in ecosystems [1], and socioeconomic modeling [8].…”