A comparison of individual-based and matrix projection models for simulating yellow perch population dynamics in Oneida Lake, New York, USA

Sable, Shaye E.; Rose, Kenneth A.

doi:10.1016/j.ecolmodel.2008.02.013

Cited by 17 publications

(26 citation statements)

References 22 publications

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“…Individual‐based models have been used to explore emerging population‐level behaviour accounting for individual variability (see a review in Tyler and Rose 1994; Clark and Rose 1997; Shin et al. 2004, p158, 2004; Brodeur 2001; Sable and Rose 2008). K. Rose and Shaye Sable (Louisiana State University) are currently exploring this option with a 6‐species model of shrimp and fish species in salt marshes.…”

Section: Assessment Of Vulnerability Exchange Rates For Ecosystem Manmentioning

confidence: 99%

“…A potential fourth approach for estimating vulnerability exchange rates may be to predict them as emergent properties of multispecies, individualbased models that represent foraging interactions on very short time scales (minutes to hours) over very detailed spatial maps (1 m 2 or smaller grid cells). Individual-based models have been used to explore emerging population-level behaviour accounting for individual variability (see a review in Tyler and Rose 1994;Clark and Rose 1997;Shin et al 2004Shin et al , p158, 2004Brodeur 2001;Sable and Rose 2008). K. Rose and Shaye Sable (Louisiana State University) are currently exploring this option with a 6-species model of shrimp and fish species in salt marshes.…”

Section: Using Complex Individual-based Spatial Modelsmentioning

confidence: 99%

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Foraging arena theory

Ahrens¹,

Walters²,

Christensen³

2011

Fish and Fisheries

283

198

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Introduction 42Basic models of foraging arena theory 43Mechanisms that cause prey population partitioning and vulnerability exchange processes 44 Arena structure caused by restricted spatial distribution of predators relative to prey 45Restricted predator distribution in response to predation risk caused by its predators 45Restricted predator distribution caused by limited predator mobility or habitat requirements 45Restricted prey distribution and/or activity 47Time allocation to safe/resting sites 47 AbstractThere is a critical need for quantitative models that can help evaluate trade-off decisions related to the impacts of harvesting and protection of aquatic ecosystems within an ecosystem context. Ecosystem models used to evaluate such trade-offs need to have the capability of capturing the dynamic stability that can arise when predator-prey interactions are restricted to spatial and temporal arenas. Foraging arenas appear common in aquatic systems and are created by a wide range of mechanisms, ranging from restrictions of predator distributions in response to predation risk caused by their own predators, to risk-sensitive foraging behaviour by their prey. Foraging arenas partition the prey in each predator-prey interaction in a food web into vulnerable and invulnerable states, with exchange between these states potentially limiting overall trophic flow. Inclusion of vulnerability exchange processes in models for recruitment processes and food web responses to disturbances like harvesting leads to very different predictions about dynamic stability, trophic cascades and maintenance of ecological diversity than do models based on large-scale mass action (random mixing) interactions between prey and predators. Although a number of methods to estimate these critical exchange rates are presented, none are considered fully satisfactory. The most important challenge for the practical application of models that incorporate foraging arena theory today is not only developing new or improved methods for measuring exchange rates but also evaluating how such rates vary in responses to major fishery-induced changes in abundances of predators.

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Section: Assessment Of Vulnerability Exchange Rates For Ecosystem Manmentioning

confidence: 99%

Section: Using Complex Individual-based Spatial Modelsmentioning

confidence: 99%

Foraging arena theory

Ahrens¹,

Walters²,

Christensen³

2011

Fish and Fisheries

283

198

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“…The advantages of population matrix models, compared to individual-based models (interchangeably empirical-statistical individual tree models, see [16]) are abundant, but dependent on the application; the best approach for a particular case should be the one that is the most consistent with modelling purposes while making the fewest assumptions according to the law of parsimony, i.e., Occam's razor [1]. Stated differently, when the two approaches (individual-based models and population matrix models) make predictions of similar quality, Occam's razor favours parsimonious population matrix models (see, e.g., [17]). Given the complexity of individual-based models, the large amount of information (and field measurements) they require and the long processing time still make them difficult and laborious to apply for forest management, thus simpler and more compact models dealing with e.g., size classes are more efficient and practical for the majority of purposes [1,18].…”

Section: Introductionmentioning

confidence: 99%

Introducing a Non-Stationary Matrix Model for Stand-Level Optimization, an Even-Aged Pine (Pinus Sylvestris L.) Stand in Finland

Pyy

Ahtikoski

Laitinen

et al. 2017

Forests

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Abstract:In general, matrix models are commonly applied to predict tree growth for size-structured tree populations, whereas empirical-statistical models are designed to predict tree growth based on a vast amount of field observations. From the theoretical point of view, matrix models can be considered to be more generic since their dependency on ad hoc growth conditions is far less prevalent than that of empirical-statistical models. On the other hand, the main pitfall of matrix models is their inability to include variation among the individuals within a size class, occasionally resulting in less accurate predictions of tree growth compared to empirical-statistical models. Thus, the relevant question is whether a matrix model can capture essential tree-growth dynamics/characteristics so that the model produces accurate stand projections which can further be applied in practical decision-making. Such a dynamic characteristic in our model is the basal area of trees, which causes nonlinearity in time. Therefore, our matrix model is a nonlinear model. The empirical data for models was based on 20 sample plots representing 8360 tree records. Further, according to the model, stand projections were produced for three Scots pine (Pinus sylvestris L.) sapling stands (age of 25 years, stand density fluctuating from 850 to 1400 stems ha −1 ). Then, (even-aged) stand management was optimized by applying sequential quadratic programming (SQP) among those growth predictions. The objective function of the optimization task was to maximize the net present value (NPV) of the ongoing rotation. The stands were located in Northern Ostrobothnia, Finland, on nutrient-poor soil type. The results indicated that initial stand density had an effect on optimal solutions-optimal stand management varied with respect to thinnings (timing and intensity) as well as to optimal rotation. Further, an increasing discount rate shortened considerably the optimal rotation period, and relaxing the minimum thinning removal to 30 m 3 ha −1 resulted in an increase both in number of thinnings and in the maximum net present value.

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“…These models can be used to simulate harvesting regimes and provide insight into how population dynamics and parameters are likely to change (e.g., Kendall & Quinn, ; McLoughlin et al, ). Both structured population‐based models (e.g., matrix models; Caswell, ) and individual‐based models are commonly used to simulate population dynamics (for a comparison, see Sable & Rose, ). Model choice depends on the complexity of research question and study system, the available data, and computing capacity.…”

Section: Introductionmentioning

confidence: 99%

“…individual-based models are commonly used to simulate population dynamics (for a comparison, see Sable & Rose, 2008). Model choice depends on the complexity of research question and study system, the available data, and computing capacity.…”

Section: Introductionmentioning

confidence: 99%

Effects of size‐ and sex‐selective harvesting: An integral projection model approach

Stubberud

Vindenes

Vøllestad

et al. 2019

Ecology and Evolution

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Harvesting is often size‐selective, and in species with sexual size dimorphism, it may also be sex‐selective. A powerful approach to investigate potential consequences of size‐ and/or sex‐selective harvesting is to simulate it in a demographic population model. We developed a population‐based integral projection model for a size‐ and sex‐structured species, the commonly exploited pike (Esox lucius). The model allows reproductive success to be proportional to body size and potentially limited by both sexes. We ran all harvest simulations with both lower size limits and slot limits, and to quantify the effects of selective harvesting, we calculated sex ratios and the long‐term population growth rate (λ). In addition, we quantified to what degree purely size‐selective harvesting was sex‐selective, and determined when λ shifted from being female to male limited under size‐ and sex‐selective harvesting. We found that purely size‐selective harvest can be sex‐selective, and that it depends on the harvest limits and the size distributions of the sexes. For the size‐ and sex‐selective harvest simulations, λ increased with harvest intensity up to a threshold as females limited reproduction. Beyond this threshold, males became the limiting sex, and λ decreased as more males were harvested. The peak in λ, and the corresponding sex ratio in harvest, varied with both the selectivity and the intensity of the harvest simulation. Our model represents a useful extension of size‐structured population models as it includes both sexes, relaxes the assumption of female dominance, and accounts for size‐dependent fecundity. The consequences of selective harvesting presented here are especially relevant for size‐ and sex‐structured exploited species, such as commercial fisheries. Thus, our model provides a useful contribution toward the development of more sustainable harvesting regimes.

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A comparison of individual-based and matrix projection models for simulating yellow perch population dynamics in Oneida Lake, New York, USA

Cited by 17 publications

References 22 publications

Foraging arena theory

Foraging arena theory

Introducing a Non-Stationary Matrix Model for Stand-Level Optimization, an Even-Aged Pine (Pinus Sylvestris L.) Stand in Finland

Effects of size‐ and sex‐selective harvesting: An integral projection model approach

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