The Role of Assumptions in Predictions of Habitat Availability and Quality

Laurent, Edward J.; Drew, C. Ashton; Thogmartin, Wayne E.

doi:10.1007/978-1-4419-7390-0_5

Cited by 6 publications

(3 citation statements)

References 77 publications

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“…Species habitat models are a simplification of a complex biological system (Laurent et al 2010). This simplification means species habitat models cannot be perfectly explanatory or predictive (Van Horne 2002).…”

Section: Discussionmentioning

confidence: 99%

Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

Jones-Farrand

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Thogmartin³

et al. 2011

Ecological Applications

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Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and regression tree (CRT), habitat suitability index (HSI), forest structure database (FS), and habitat association database (HA). We focused our comparison on models for five priority forest-breeding species in the Central Hardwoods Bird Conservation Region: Acadian Flycatcher, Cerulean Warbler, Prairie Warbler, Red-headed Woodpecker, and Worm-eating Warbler. Lacking complete knowledge on the distribution and abundance of each species with which we could illuminate differences between approaches and provide strong grounds for recommending one approach over another, we used two approaches to compare models: rank correlations among model outputs and comparison of spatial correspondence. In general, rank correlations were significantly positive among models for each species, indicating general agreement among the models. Worm-eating Warblers had the highest pairwise correlations, all of which were significant (P < 0.05). Red-headed Woodpeckers had the lowest agreement among models, suggesting greater uncertainty in the relative conservation value of areas within the region. We assessed model uncertainty by mapping the spatial congruence in priorities (i.e., top ranks) resulting from each model for each species and calculating the coefficient of variation across model ranks for each location. This allowed identification of areas more likely to be good targets of conservation effort for a species, those areas that were least likely, and those in between where uncertainty is higher and thus conservation action incorporates more risk. Based on our results, models developed independently for the same purpose (conservation planning for a particular species in a particular geography) yield different answers and thus different conservation strategies. We assert that using only one habitat model (even if validated) as the foundation of a conservation plan is risky. Using multiple models (i.e., ensemble prediction) can reduce uncertainty and increase efficacy of conservation action when models corroborate one another and increase understanding of the system when they do not.

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Section: Discussionmentioning

confidence: 99%

Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

Jones-Farrand

Fearer

Thogmartin³

et al. 2011

Ecological Applications

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show abstract

“…In environmental modelling, common assumptions include the choice of temporal and spatial scales (to exclude processes occurring at other scales), setting the system boundaries (to exclude or limit interactions with external entities), the choice of variables and processes (to exclude those irrelevant to the modelling exercise and to simplify others, e.g. treating a dynamic process as constant), and grouping states or variables into similar classes (Laurent et al, 2011). Cases of limited data typically constrain the modelling exercise to simpler model structures, which inherently can involve more, or at least different, assumptions.…”

Section: Model Uncertainty In Bayesian Networkmentioning

confidence: 99%

Habitat suitability modelling of rare species using Bayesian networks: Model evaluation under limited data

Hamilton

Pollino

Jakeman

2015

Ecological Modelling

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“…Our modeling also does not account for the potential effects of the spatial context of forest stands (e.g., change in aspen cover across the landscape in which our hardwood stands are located) or disturbances other than timber harvest (such as wind, disease or insects). Hence, our projections, and approaches like this, should be used as one facet of management decision-making and used with awareness of its assumptions and limitations (Laurent et al, 2010). With these caveats understood, and along with continuing improvements (e.g.…”

Section: Management Implicationsmentioning

confidence: 96%

Combined long-term effects of variable tree regeneration and timber management on forest songbirds and timber production

Millington

Walters

Matonis

et al. 2011

Forest Ecology and Management

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The Role of Assumptions in Predictions of Habitat Availability and Quality

Cited by 6 publications

References 77 publications

Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

Habitat suitability modelling of rare species using Bayesian networks: Model evaluation under limited data

Combined long-term effects of variable tree regeneration and timber management on forest songbirds and timber production

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