Predicting the impact of fire on a vulnerable multi-species community using a dynamic vegetation model

Conlisk, Erin; Syphard, Alexandra D.; Franklin, Janet; Regan, Helen M.

doi:10.1016/j.ecolmodel.2015.02.004

Cited by 18 publications

(10 citation statements)

References 54 publications

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“…This difficulty in accurately capturing the population dynamics and generating projections for species with early age at first reproduction and high variability in population estimates has been reported elsewhere (Conlisk et al. ).…”

Section: Discussionmentioning

confidence: 95%

Effects of uncertainty and variability on population declines and IUCN Red List classifications

Rueda‐Cediel

Anderson

Regan

et al. 2018

Conservation Biology

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The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.

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Section: Discussionmentioning

confidence: 95%

Effects of uncertainty and variability on population declines and IUCN Red List classifications

Rueda‐Cediel

Anderson

Regan

et al. 2018

Conservation Biology

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“…While the inclusion of vegetation (dynamic global vegetation model: DGVM [Figure a]; for a review of the spectrum of climate–forest models, see: Scheller & Mladenoff, ) has improved avian distribution models (Conlisk, Syphard, Franklin, & Regan, ; Matthews et al, ), plant functional types (outputs of DGVMs) still do not adequately account for future habitat distributions of woodpeckers (i.e., the type of climate–forest model (Figure a) is important). This is because plant functional groupings may be of a scale too course to model woodpecker responses to forest characteristics.…”

Section: Framework Integrationmentioning

confidence: 99%

Climate change, woodpeckers, and forests: Current trends and future modeling needs

Walsh

Vierling

Strand

et al. 2019

Ecology and Evolution

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The structure and composition of forest ecosystems are expected to shift with climate‐induced changes in precipitation, temperature, fire, carbon mitigation strategies, and biological disturbance. These factors are likely to have biodiversity implications. However, climate‐driven forest ecosystem models used to predict changes to forest structure and composition are not coupled to models used to predict changes to biodiversity. We proposed integrating woodpecker response (biodiversity indicator) with forest ecosystem models. Woodpeckers are a good indicator species of forest ecosystem dynamics, because they are ecologically constrained by landscape‐scale forest components, such as composition, structure, disturbance regimes, and management activities. In addition, they are correlated with forest avifauna community diversity. In this study, we explore integrating woodpecker and forest ecosystem climate models. We review climate–woodpecker models and compare the predicted responses to observed climate‐induced changes. We identify inconsistencies between observed and predicted responses, explore the modeling causes, and identify the models pertinent to integration that address the inconsistencies. We found that predictions in the short term are not in agreement with observed trends for 7 of 15 evaluated species. Because niche constraints associated with woodpeckers are a result of complex interactions between climate, vegetation, and disturbance, we hypothesize that the lack of adequate representation of these processes in the current broad‐scale climate–woodpecker models results in model–data mismatch. As a first step toward improvement, we suggest a conceptual model of climate–woodpecker–forest modeling for integration. The integration model provides climate‐driven forest ecosystem modeling with a measure of biodiversity while retaining the feedback between climate and vegetation in woodpecker climate change modeling.

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“…They require 5-15 year old heathland (Southwell et al, 2008). Similarly, in California, frequent fires can destroy the mature coastal sage scrub habitat required for the coastal cactus wren and the California gnatcatcher on which these species rely (Conlisk et al, 2015). If we want to conserve these species, it is important to maintain the availability and connectivity of their habitats.…”

Section: Introductionmentioning

confidence: 99%

Fuel treatment planning: Fragmenting high fuel load areas while maintaining availability and connectivity of faunal habitat

Rachmawati

Özlen

Hearne

et al. 2018

Applied Mathematical Modelling

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Reducing the fuel load in fire-prone landscapes is aimed at mitigating the risk of catastrophic wildfires but there are ecological consequences. Maintaining habitat for fauna of both sufficient extent and connectivity while fragmenting areas of high fuel loads presents land managers with seemingly contrasting objectives. Faced with this dichotomy, we propose a Mixed Integer Programming (MIP) model that can optimally schedule fuel treatments to reduce fuel hazards by fragmenting high fuel load regions while considering critical ecological requirements over time and space. The model takes into account both the frequency of fire that vegetation can tolerate and the frequency of fire necessary for fire-dependent species. Our approach also ensures that suitable alternate habitat is available and accessible to fauna affected by a treated area. More importantly, to conserve fauna the model sets a minimum acceptable target for the connectivity of habitat at any time . These factors are all included in the formulation of a model that yields a multi-period spatially-explicit schedule for treatment planning. Our approach is then demonstrated in a series of computational experiments with hypothetical landscapes, a single vegetation type and a group of faunal species with the same habitat requirements. Our experiments show that it is possible to reduce the risk of wildfires while ensuring sufficient connectivity of habitat over both space and time. Furthermore, it is demonstrated that the habitat connectivity constraint is more effective than neighbourhood habitat constraints. This is critical for the conservation of fauna and of special concern for vulnerable or endangered species.

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Predicting the impact of fire on a vulnerable multi-species community using a dynamic vegetation model

Cited by 18 publications

References 54 publications

Effects of uncertainty and variability on population declines and IUCN Red List classifications

Effects of uncertainty and variability on population declines and IUCN Red List classifications

Climate change, woodpeckers, and forests: Current trends and future modeling needs

Fuel treatment planning: Fragmenting high fuel load areas while maintaining availability and connectivity of faunal habitat

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