Linking the influence and dependence of people on biodiversity across scales

Isbell, Forest; Gonzalez, Andrew; Loreau, Michael; Cowles, Jane; Dı́az, Sandra; Héctor, Andy; Mace, Georgina M.; Wardle, David A.; O’Connor, Mary I.; Duffy, J. Emmett; Turnbull, Lindsay A.; Thompson, Patrick L.; Larigauderie, Anne

doi:10.1038/nature22899

Cited by 568 publications

(512 citation statements)

References 99 publications

(175 reference statements)

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“…Although an impact of urbanization on biodiversity is well known for other areas of ecology [32–35], this is the first time, to our knowledge, that it has been described for microorganisms. Surprisingly, our results suggest that urbanization has a positive effect on Prokaryote biodiversity, as, e.g., the area of the environment covered by streets correlates positively with all biodiversity indices used in this study (Table 3).…”

Section: Discussionmentioning

confidence: 99%

SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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BackgroundMicrobes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available.ResultsIn this work, we present SEDE-GPS, a tool that gathers data that are relevant to the environment of an user-provided GPS coordinate. To this end, it accesses a list of public and corporate databases and aggregates the information in a single file, which can be used for further analysis. To showcase the use of SEDE-GPS, we enriched a lake microbial ecology sequencing dataset with around 18,000 socio-economic, climate, and geographic features. The sources of SEDE-GPS are public databases such as Eurostat, the Climate Data Center, and OpenStreetMap, as well as corporate sources such as Twitter. Using machine learning and feature selection methods, we were able to identify features in the data provided by SEDE-GPS that can be used to predict lake microbiome alpha diversity.ConclusionThe results presented in this study show that SEDE-GPS is a handy and easy-to-use tool for comprehensive data enrichment for studies of ecology and other processes that are affected by environmental features. Furthermore, we present lists of environmental, socio-economic, and climate features that are predictive for microbial biodiversity in lake ecosystems. These lists indicate that human action has a major impact on lake microbiomes. SEDE-GPS and its source code is available for download at http://SEDE-GPS.heiderlab.deElectronic supplementary materialThe online version of this article (10.1186/s12859-018-2419-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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“…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. 4, Halley et al 2016Halley et al , 2017, further investigation into their consequences, and the extension of those consequences to ecosystem service debts (Isbell et al 2015, Valiente-Banuet et al 2015, Lafuite and Loreau 2017) is essential if we are to understand extinction debt implications for human life (Isbell et al 2017). 4, Halley et al 2016Halley et al , 2017, further investigation into their consequences, and the extension of those consequences to ecosystem service debts (Isbell et al 2015, Valiente-Banuet et al 2015, Lafuite and Loreau 2017) is essential if we are to understand extinction debt implications for human life (Isbell 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

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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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“…Much of this understanding has been generated through small-scale experimental manipulations of plant species richness (e.g., Cardinale et al 2012). Four key research considerations are necessary to advance our understanding of diversity-ecosystem function relationships (Gamfeldt et al 2013, Tilman et al 2013, Isbell et al 2017: (1) considering multifunctionality rather than individual functions, (2) evaluating the effect of multiple metrics of biodiversity on multifunctionality, (3) studying diversity-function relationships within natural systems, and (4) assessing how ecosystem functions trade off. Four key research considerations are necessary to advance our understanding of diversity-ecosystem function relationships (Gamfeldt et al 2013, Tilman et al 2013, Isbell et al 2017: (1) considering multifunctionality rather than individual functions, (2) evaluating the effect of multiple metrics of biodiversity on multifunctionality, (3) studying diversity-function relationships within natural systems, and (4) assessing how ecosystem functions trade off.…”

Section: Introductionmentioning

confidence: 99%

Landscape context explains ecosystem multifunctionality in restored grasslands better than plant diversity

Zirbel

Grman

Bassett

et al. 2019

Ecology

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There is strong evidence for a positive relationship between biodiversity and ecosystem functioning at local spatial scales. However, how different aspects of biodiversity relate to multiple ecosystem functions (multifunctionality) across heterogeneous landscapes, and how the magnitude of biodiversity, dominant species, and environmental effects on functioning compare, remain poorly understood. We compared relationships between plant phylogenetic, functional, and taxonomic diversity and ecosystem multifunctionality across 29 restored grasslands. Functional diversity was positively associated with multifunctionality, more strongly than other diversity measures; however, landscape composition explained nearly four times more variation in multifunctionality than did functional diversity, with plots within human-modified landscapes supporting lower multifunctionality. Individual functions were typically more strongly correlated with environmental variables than with diversity. We also found that abundance of the dominant species, Andropogon gerardii, was positively correlated with multifunctionality. Plant diversity, dominant species, and underlying environmental conditions underpin ecosystem multifunctionality in grasslands, but how biodiversity is measured matters for the strength and direction of biodiversity-ecosystem function relationships. Finally, in natural systems environmental variation unrelated to local biodiversity is important for determining ecosystem functioning.

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Linking the influence and dependence of people on biodiversity across scales

Cited by 568 publications

References 99 publications

SEDE-GPS: socio-economic data enrichment based on GPS information

SEDE-GPS: socio-economic data enrichment based on GPS information

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

Landscape context explains ecosystem multifunctionality in restored grasslands better than plant diversity

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