The role of agent-based models in wildlife ecology and management

McLane, Adam J.; Semeniuk, Christina A.D.; McDermid, Gregory J.; Marceau, Danielle J.

doi:10.1016/j.ecolmodel.2011.01.020

Cited by 190 publications

(136 citation statements)

References 88 publications

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“…In many of the existing IBMs of movement processes across complex landscapes the key questions being addressed have related to connectivity [208][209][210], emergent dispersal mortality [211,212], and home range formation [206,213,214], but in many cases these individual-based movement models have not been linked to models of population dynamics. When such links are made, it is possible to gain important new insights into the dynamics of species living on complex landscapes and into potential consequences of alternative management interventions [215]. In one recent example, [216] used RangeShifter to combine a stochastic IBM for movement with a spatially explicit population model to explore alternative plausible management scenarios for birds in the Taita Hills in Kenya, a biodiversity hotspot.…”

Section: Process-based Modelsmentioning

confidence: 99%

Emerging Opportunities for Landscape Ecological Modelling

Synes

Brown

Watts

et al. 2016

Curr Landscape Ecol Rep

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Landscape ecological modelling provides a vital means for understanding the interactions between geographical, climatic, and socio-economic drivers of land-use and the dynamics of ecological systems. This growing field is playing an increasing role in informing landscape spatial planning and management. Here, we review the key modelling approaches that are used in landscape modelling and in ecological modelling. We identify an emerging theme of increasingly detailed representation of process in both landscape and ecological modelling, with complementary suites of modelling approaches ranging from correlative, through aggregated process based approaches to models with much greater structural realism that often represent behaviours at the level of agents or individuals. We provide examples of the considerable progress that has been made at the intersection of landscape modelling and ecological modelling, while also highlighting that the majority of this work has to date exploited a relatively small number of the possible combinations of model types from each discipline. We use this review to identify key gaps in existing landscape ecological modelling effort and highlight emerging opportunities, in particular for future work to progress in novel directions by combining classes of landscape models and ecological models that have rarely been used together.

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Section: Process-based Modelsmentioning

confidence: 99%

Emerging Opportunities for Landscape Ecological Modelling

Synes

Brown

Watts

et al. 2016

Curr Landscape Ecol Rep

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“…These ABMs can be divided into categories depending on whether agents are given imposed, empirically-derived behaviors, or agents are allowed to choose the optimal strategy themselves based on decision-making tradeoffs (for a thorough review, see McLane et al 2011). The latter category is the focus of this section, as it most closely represents the tenets of behavioral ecology ( Figure 3).…”

Section: Behavioral-ecological Abms and Species Distributionsmentioning

confidence: 99%

“…In agent-based modelling, the movement trajectory or pathway of an animal can be represented as a sequence of discrete time-stamped location variables, for example, geographic coordinates. Because environment representation in ABMs can be raster-or vector-based, the location variables can be further indexed by raster cells or vector-based patches (Tang and Bennett 2010;McLane et al 2011). ABMs are not completely immune to issues of scale.…”

Section: Extent Versus Resolutionmentioning

confidence: 99%

Integrating Spatial Behavioral Ecology in Agent-Based Models for Species Conservation

Semeniuk¹,

Musiani²,

Marceau³

2011

Biodiversity

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“…The second part of the model corresponds to an agent-based system, which is a versatile modeling tool in ecology [39]. The model uses cellular automata to represent the populations that continuously respond to the simulated abiotic and biotic conditions.…”

Section: Model Structurementioning

confidence: 99%

Approaching the concepts of ecosystems resilience and stability through spatiotemporal system dynamics and agent-based modelling

Peña-Alzate

Barriga

2017

Rev. Fac. Ing. Univ. Antioquia

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Abstract:We introduce a simplified ecohydrological model to quantitatively assess the resiliency and stability of ecosystems over long periods. The model couples a hydrological soil moisture balance with a set of spatiotemporal systems dynamics and agent-based algorithms to represent the interactions among several plant populations in a gridded area under different water, soil and temperature constraints. We characterize the plant populations by allometric rules (i.e., power laws for generational and reproductive times; linear approximations for water and temperature gains, losses and optimal values; and a set of intra and interspecific interaction rules based on high, optimal and low competition responses among the populations), that represent different plant phenotypes. We define the disturbances by a clearance of populations in an area within the model's domain and calculate the resiliency and stability with simple indices to determine the ability of the ecosystem to recover from a disturbance. The indices evaluated on each population and over the structure of the entire ecosystem show how the populations respond differently to disturbances, following patterns similar to those expected in nature. The model can represent the spatial and temporal succession of the ecosystem after being disturbed, suggesting how the differences in the phenotypical characteristics of plant populations can be advantageous or disadvantageous for the ecosystem recovery.Resumen: Se presenta un modelo ecohidrológico simplificado para evaluar cuantitativamente la resiliencia y estabilidad de los ecosistemas durante largos períodos. El modelo acopla el balance hidrológico de humedad del suelo con un conjunto de dinámicas espaciotemporales de sistemas y algoritmos basados en agentes para representar las interacciones entre varias poblaciones de plantas en un área de grilla bajo diferentes restricciones de agua, suelo y temperatura. Las poblaciones de plantas se caracterizan por reglas alométricas (por ejemplo, leyes de potencia para tiempos generacionales y reproductivos; aproximaciones lineales para ganancias de agua y temperatura; pérdidas y valores óptimos; y un conjunto de reglas de interacción intra e interespecíficas basadas en respuestas de competencia altas, óptimas y bajas entre las poblaciones), que representan diferentes fenotipos de plantas. Los trastornos se determinan por una remoción de las poblaciones en un área dentro del dominio del modelo. La resistencia y la estabilidad se calculan con índices simples para determinar la capacidad del ecosistema para recuperarse de una perturbación. Los índices evaluados en cada población y sobre la estructura de todo el ecosistema muestran cómo las poblaciones responden de manera diferente a los disturbios, siguiendo patrones similares a los esperados en la naturaleza. El modelo puede representar la sucesión espacial y temporal del ecosistema después de ser perturbado, lo que sugiere que las diferencias en las características fenotípicas de las poblaciones de plantas pueden ser ventaj...

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The role of agent-based models in wildlife ecology and management

Cited by 190 publications

References 88 publications

Emerging Opportunities for Landscape Ecological Modelling

Emerging Opportunities for Landscape Ecological Modelling

Integrating Spatial Behavioral Ecology in Agent-Based Models for Species Conservation

Approaching the concepts of ecosystems resilience and stability through spatiotemporal system dynamics and agent-based modelling

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