Conservation in a changing world needs predictive models

Wood, Kevin A.; Stillman, Richard A.; Hilton, Geoff M.

doi:10.1111/acv.12371

Cited by 35 publications

(40 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5a). Such an approach is especially useful for informing conservation efforts (Wood et al 2018), which may currently overlook delayed extinctions (Urban 2015, Chen and Peng 2017 -see Box 2 for considerations of conservation policies). Specifically, accounting for extinction debt when planning conservation and management has been shown to be especially useful when funding is limited; knowledge of the dynamics of extinctions allows more effective resource allocation (Leroux et al 2009, Leroux and Whitten 2014, Iacona et al 2017.…”

Section: Mechanistic Simulation Modeling As a Navigational Toolmentioning

confidence: 99%

Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research

et al. 2019

View full text Add to dashboard Cite

Extinction debt refers to delayed species extinctions expected as a consequence of ecosystem perturbation. Quantifying such extinctions and investigating long-term consequences of perturbations has proven challenging, because perturbations are not isolated and occur across various spatial and temporal scales, from local habitat losses to global warming. Additionally, the relative importance of eco-evolutionary processes varies across scales, because levels of ecological organization, i.e. individuals, (meta) populations and (meta)communities, respond hierarchically to perturbations. To summarize our current knowledge of the scales and mechanisms influencing extinction debts, we reviewed recent empirical, theoretical and methodological studies addressing either the spatio-temporal scales of extinction debts or the eco-evolutionary mechanisms delaying extinctions. Extinction debts were detected across a range of ecosystems and taxonomic groups, with estimates ranging from 9 to 90% of current species richness. The duration over which debts have been sustained varies from 5 to 570 yr, and projections of the total period required to settle a debt can extend to 1000 yr. Reported causes of delayed extinctions are 1) life-history traits that prolong individual survival, and 2) population and metapopulation dynamics that maintain populations under deteriorated conditions. Other potential factors that may extend survival time such as microevolutionary dynamics, or delayed extinctions of interaction partners, have rarely been analyzed. Therefore, we propose a roadmap for future research with three key avenues: 1) the microevolutionary dynamics of extinction processes, 2) the disjunctive loss of interacting species and 3) the impact of multiple regimes of perturbation on the payment of debts. For their ability to integrate processes occurring at different levels of ecological organization, we highlight mechanistic simulation models as tools to address these knowledge gaps and to deepen our understanding of extinction dynamics.The extinctions that comprise an extinction debt can be expected based on the assumption of a new equilibrium to be achieved. This new equilibrium is also a community state that depends on how much the perturbation changes environmental conditions and community properties. The changes in species richness will then emerge from the interactions of eco-evolutionary processes over time at multiple levels of ecological organization (Cabral et al. 2017(Cabral et al. , 2019. This reasoning emphasizes extinction debt as a community (or metacommunity) state. Therefore, we further refer to mechanisms of extinction debt as eco-evolutionary processes creating or prolonging this state, i.e. delaying extinctions and thus putting and maintaining the community into debt.Being a state, an extinction debt has to be first and foremost, detected. Once detected, it can be characterized (Fig. 1). The extinction debt itself is the number of extinctions expected to happen as consequence of a perturbation, therefore, the main m...

show abstract

Section: Mechanistic Simulation Modeling As a Navigational Toolmentioning

confidence: 99%

Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Environmental conditions can be diverse as a result of new extremes temperature and precipitation patterns and novel assemblages and interaction species due to the human-assisted spread of exotic species [34]. In light of this, Wood et al [34] state that such environmental changes call for conservation to become more predictive.…”

Section: Additional Technologiesmentioning

confidence: 99%

How Technology Can Transform Wildlife Conservation

Pacheco¹

2019

Green Technologies to Improve the Environment on Earth

View full text Add to dashboard Cite

Wildlife is under threat from various kinds of human activities, such as habitat destruction, illegal wildlife trade, spread of invasive species and diseases, and from the human impact on the Earth's climate, which is changing the nature of wild habitats. Advances in technology give conservationists, scientists, and the general public the advantage to better understand the animals, their habitats, and the threats they can face. In this chapter, I provide a review of the benefits of the use of technology to animal ecology and conservation. Two major approaches are being recognized to conserve threatened and endangered wildlife species. The first encompasses protecting the species within their habitat, and the second involves breeding and caring individual species ex situ. The use of technological applications in captivity, such as satellite imaging and assisted breeding technologies, is focused to enhance animal welfare and to influence zoo visitors' awareness of conservation-related behavior. Given the increasing demands on protecting wildlife, it seems a fair time for us to pause and ask what could be the best way to use technological innovations and to stimulate a closer collaboration among conservation practitioners, animal behaviorists, biologists, computer and system scientists, and engineers, to mention but a few.

show abstract

“…Predictive spatial models are currently in high demand for conservation planning and to improve understanding of species distributions (Rodríguez et al 2007;Wood et al 2017). Broad-scale climatic assessments provide an initial step towards smaller-scale projects focused on planning reintroductions, mitigating development conflicts, defining protected areas and evaluating the impact of climate change on species distributions (Peterson et al 2011).…”

Section: Predicted Distributions For the Qinghai-tibet Plateaumentioning

confidence: 99%

Climatic niche of the Saker FalconFalco cherrug: predicted new areas to direct population surveys in Central Asia

Sutton

Puschendorf

2018

Ibis

View full text Add to dashboard Cite

Accurate species distribution data across remote and extensive geographical areas are difficult to obtain. Here, we use bioclimatic envelope models to determine climatic constraints on the distribution of the migratory Saker Falcon Falco cherrug to identify areas in data‐deficient regions that may contain unidentified populations. Sakers live at low densities across large ranges in remote regions, making distribution status difficult to assess. Using presence‐background data and eight bioclimatic variables within a species distribution modelling framework, we applied MaxEnt to construct models for both breeding and wintering ranges. Occurrence data were spatially filtered and climatic variables tested for multicollinearity before selecting best fit models using the Akaike information criterion by tuning MaxEnt parameters. Model predictive performance tested using the continuous Boyce index (B) was high for both breeding (BTEST = 0.921) and wintering models (BTEST = 0.735), with low omission rates and minimal overfitting. The Saker climatic niche was defined by precipitation in the warmest quarter in the breeding range model, and mean temperature in the wettest quarter in the wintering range model. Our models accurately predicted areas of highest climate suitability and defined the climatic constraints on a wide‐ranging rare species, suggesting that climate is a key determinant of Saker distribution across macro‐scales. We recommend targeted population surveys for the Saker based on model predictions to areas of highest climatic suitability in key regions with distribution knowledge gaps, in particular the Qinghai‐Tibet plateau in western China. Further applications of our models could identify protected areas and reintroduction sites, inform development conflicts, and assess the impact of climate change on distributions.

show abstract

Conservation in a changing world needs predictive models

Cited by 35 publications

References 20 publications

Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research

Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research

How Technology Can Transform Wildlife Conservation

Climatic niche of the Saker FalconFalco cherrug: predicted new areas to direct population surveys in Central Asia

Contact Info

Product

Resources

About