Supporting global biodiversity assessment through high-resolution macroecological modelling: Methodological underpinnings of the BILBI framework

Hoskins, Andrew J.; Harwood, Thomas D.; Ware, Chris; Williams, Kristen; Perry, Justin; Ota, Noboru; Croft, Jim R; Jetz, Walter; Golebiewski, Maciej; Purvis, Andy; Ferrier, Simon

doi:10.1101/309377

Cited by 17 publications

(36 citation statements)

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“…We combined models of the spatial turnover in species composition of vascular plant communities generated by Hoskins et al (2019) with estimates of habitats condition, to derive projections of plant biodiversity persistence under scenarios of land-use and climate change ( Figure 1). …”

Section: Modelling Compositional Turnover In Vascular Plant Communimentioning

confidence: 99%

“…In the current study, we used a specially modified form of GDM, as implemented in the Biogeographic Infrastructure for Large-scaled Biodiversity Indicators (BILBI; Hoskins et al, 2019). This approach corrects for biases introduced into predictions when models are fitted to incomplete survey inventories.…”

Section: Modelling Compositional Turnover In Vascular Plant Communimentioning

confidence: 99%

“…However, forecasting biodiversity trends is a complex challenge which requires considering how a representative set of biodiversity indicators respond to a predefined set of scenarios. As part of this exercise, we performed a high-resolution (1 km), multi-extent (local to global) analysis of the expected changes in plant biodiversity persistence through space and time, using an innovative approach based on compositional turnover modelling (Hoskins et al, 2019). In this model intercomparison, a standardized set of land-use and climate change projections were used to project outcomes for a variety of biodiversity and ecosystem-service indicators.…”

Section: Introductionmentioning

confidence: 99%

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Projecting impacts of global climate and land‐use scenarios on plant biodiversity using compositional‐turnover modelling

Marco

Harwood

Hoskins

et al. 2019

Global Change Biology

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103

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Nations have committed to ambitious conservation targets in response to accelerating rates of global biodiversity loss. Anticipating future impacts is essential to inform policy decisions for achieving these targets, but predictions need to be of sufficiently high spatial resolution to forecast the local effects of global change. As part of the intercomparison of biodiversity and ecosystem services models of the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services, we present a fine‐resolution assessment of trends in the persistence of global plant biodiversity. We coupled generalized dissimilarity models, fitted to >52 million records of >254 thousand plant species, with the species–area relationship, to estimate the effect of land‐use and climate change on global biodiversity persistence. We estimated that the number of plant species committed to extinction over the long term has increased by 60% globally between 1900 and 2015 (from ~10,000 to ~16,000). This number is projected to decrease slightly by 2050 under the most optimistic scenario of land‐use change and to substantially increase (to ~18,000) under the most pessimistic scenario. This means that, in the absence of climate change, scenarios of sustainable socio‐economic development can potentially bring extinction risk back to pre‐2000 levels. Alarmingly, under all scenarios, the additional impact from climate change might largely surpass that of land‐use change. In this case, the estimated number of species committed to extinction increases by 3.7–4.5 times compared to land‐use‐only projections. African regions (especially central and southern) are expected to suffer some of the highest impacts into the future, while biodiversity decline in Southeast Asia (which has previously been among the highest globally) is projected to slow down. Our results suggest that environmentally sustainable land‐use planning alone might not be sufficient to prevent potentially dramatic biodiversity loss, unless a stabilization of climate to pre‐industrial times is observed.

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Section: Modelling Compositional Turnover In Vascular Plant Communimentioning

confidence: 99%

Section: Modelling Compositional Turnover In Vascular Plant Communimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Projecting impacts of global climate and land‐use scenarios on plant biodiversity using compositional‐turnover modelling

Marco

Harwood

Hoskins

et al. 2019

Global Change Biology

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103

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“…Abiotic and biotic factors, such as perturbation intensity and species life-history traits, respectively, as well as stochasticity have been shown to influence how many extinctions happen and how long they will take (Kuussaari et al 2009). Understanding this extinction dynamics and the underlying processes is paramount, considering that current extinction debts represent a sizable portion of the predicted 1 million species threatened with extinction (hundreds of thousands of terrestrial species alone -Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) 2019, based on Hoskins et al 2019). At the community (and metacommunity) levels, biotic interactions add further feedbacks between these processes (Jackson andSax 2010, Essl et al 2015a).…”

Section: Introductionmentioning

confidence: 99%

“…The variety of processes, the ecological level at which they act, and interactions among them complicate the ability to predict which, when and why species go extinct. Understanding this extinction dynamics and the underlying processes is paramount, considering that current extinction debts represent a sizable portion of the predicted 1 million species threatened with extinction (hundreds of thousands of terrestrial species alone -Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) 2019, based on Hoskins et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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...

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Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling

Fernández

Ferrier

Navarro

et al. 2020

Remote Sensing of Plant Biodiversity

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Essential biodiversity variables (EBVs) are designed to support the detection and quantification of biodiversity change and to define priorities in biodiversity monitoring. Unlike most primary observations of biodiversity phenomena, EBV products should provide information readily available to produce policy-relevant biodiversity indicators, ideally at multiple spatial scales, from global to subnational. This information is typically complex to produce from a single set of data or type of observation, thus requiring approaches that integrate multiple sources of in situ and remote sensing (RS) data. Here we present an up-to-date EBV concept for biodiversity data integration and discuss the critical components of workflows for EBV production. We argue that open and reproducible workflows for data integration are critical to ensure traceability and reproducibility so that each EBV endures and can be updated as novel biodiversity models are adopted, new observation systems become available, and new data sets are incorporated. Fulfilling the EBV vision requires strengthening efforts to mobilize massive amounts of in situ biodiversity data that are not yet publicly available and taking full advantage of emerging RS technologies, novel biodiversity models, and informatics infrastructures, in alignment with the development of a globally coordinated system for biodiversity monitoring.

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Supporting global biodiversity assessment through high-resolution macroecological modelling: Methodological underpinnings of the BILBI framework

Cited by 17 publications

References 50 publications

Projecting impacts of global climate and land‐use scenarios on plant biodiversity using compositional‐turnover modelling

Projecting impacts of global climate and land‐use scenarios on plant biodiversity using compositional‐turnover modelling

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

Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling

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