Dataset Information

Towards a global understanding of the drivers of marine and terrestrial biodiversity.

ABSTRACT: Understanding the distribution of life's variety has driven naturalists and scientists for centuries, yet this has been constrained both by the available data and the models needed for their analysis. Here we compiled data for over 67,000 marine and terrestrial species and used artificial neural networks to model species richness with the state and variability of climate, productivity, and multiple other environmental variables. We find terrestrial diversity is better predicted by the available environmental drivers than is marine diversity, and that marine diversity can be predicted with a smaller set of variables. Ecological mechanisms such as geographic isolation and structural complexity appear to explain model residuals and also identify regions and processes that deserve further attention at the global scale. Improving estimates of the relationships between the patterns of global biodiversity, and the environmental mechanisms that support them, should help in efforts to mitigate the impacts of climate change and provide guidance for adapting to life in the Anthropocene.

SUBMITTER: Gagne TO

PROVIDER: S-EPMC7001915 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Towards a global understanding of the drivers of marine and terrestrial biodiversity.

Gagné Tyler O TO Reygondeau Gabriel G Jenkins Clinton N CN Sexton Joseph O JO Bograd Steven J SJ Hazen Elliott L EL Van Houtan Kyle S KS

PloS one 20200205 2

Understanding the distribution of life's variety has driven naturalists and scientists for centuries, yet this has been constrained both by the available data and the models needed for their analysis. Here we compiled data for over 67,000 marine and terrestrial species and used artificial neural networks to model species richness with the state and variability of climate, productivity, and multiple other environmental variables. We find terrestrial diversity is better predicted by the available ...[more]

PMID: 32023269

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Project description:Risks associated with exposure of individual plant species to ozone (O3) are well documented, but implications for terrestrial biodiversity and ecosystem processes have received insufficient attention. This is an important gap because feedbacks to the atmosphere may change as future O3 levels increase or decrease, depending on air quality and climate policies. Global simulation of O3 using the Community Earth System Model (CESM) revealed that in 2000, about 40% of the Global 200 terrestrial ecoregions (ER) were exposed to O3 above thresholds for ecological risks, with highest exposures in North America and Southern Europe, where there is field evidence of adverse effects of O3, and in central Asia. Experimental studies show that O3 can adversely affect the growth and flowering of plants and alter species composition and richness, although some communities can be resilient. Additional effects include changes in water flux regulation, pollination efficiency, and plant pathogen development. Recent research is unraveling a range of effects belowground, including changes in soil invertebrates, plant litter quantity and quality, decomposition, and nutrient cycling and carbon pools. Changes are likely slow and may take decades to become detectable. CESM simulations for 2050 show that O3 exposure under emission scenario RCP8.5 increases in all major biomes and that policies represented in scenario RCP4.5 do not lead to a general reduction in O3 risks; rather, 50% of ERs still show an increase in exposure. Although a conceptual model is lacking to extrapolate documented effects to ERs with limited or no local information, and there is uncertainty about interactions with nitrogen input and climate change, the analysis suggests that in many ERs, O3 risks will persist for biodiversity at different trophic levels, and for a range of ecosystem processes and feedbacks, which deserves more attention when assessing ecological implications of future atmospheric pollution and climate change.

Dataset Information

Towards a global understanding of the drivers of marine and terrestrial biodiversity.

Publications

Towards a global understanding of the drivers of marine and terrestrial biodiversity.

Similar Datasets

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