Worldwide impacts of past and projected future land-use change on local species richness and the Biodiversity Intactness Index

Abstract

Although people have modified the world around us throughout human history, the 'Great Acceleration' has seen drivers such as land conversion, exploitation of natural populations, species introductions, pollution and human-induced climate change placing biodiversity under increasing pressure. In this paper we examine 1) how terrestrial species communities have been impacted over the last thousand years of human development and 2) how plausible futures defined by alternative socio-economic scenarios are expected to impact species communities in the future. We use the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) database to model impacts of land-use change and human population on local species richness, community abundance, and biodiversity intactness using a mixed-effects modelling structure. Historical impacts are inferred through projection of model results onto maps of historical land use, provided by the land-use harmonization project, and gridded human population density (HYDE 3.1). Future impacts are explored using the Shared Socio-economic Pathway (SSP) scenarios. These scenarios detail five plausible global futures based upon socio-economic factors such as wealth, population, education, technology, and reliance on fossil fuels, and can be combined with Representative Concentration Pathway (RCP) scenarios to consider climate mitigation strategies. We project model results onto the gridded outputs of six SSP/RCP scenario combinations: SSP1/RCP2.6, SSP2/RCP4.5, SSP3/RCP7.0, SSP4/RCP3.4, SSP4/RCP6.0, and SSP5/RCP8.5. Historical trend lines show that most losses in local biodiversity are relatively recent, with 75% of all loss in both abundance-based Biodiversity Intactness Index and species richness occurring post-1800. Stark regional differences emerge in all future scenarios, with biodiversity in African regions undergoing greater losses than Oceania, North America and the European regions. Although climate change is expected to have severe detrimental impacts to biodiversity -which are not quantified in these results -it is important to consider how the climate change mitigation itself may also impact biodiversity. Our results suggest that strong climate change mitigation through biofuel production will detrimentally impact biodiversity: SSP4/RCP3.4 (with high biofuel mitigation) is predicted to see two times the decrease in abundance-based biodiversity intactness and three times the decrease in local species richness between 2015-2100 as is predicted for SSP4/RCP6.0 (with lower levels of mitigation). SSP4/RCP3.4 forecasts the greatest impact to average local species richness of all the SSP/RCP combinations with an average loss of 13% of local species richness projected to have occurred by 2100. SSP3/RCP7.0 -a scenario describing a globally segregated, and economically protectionist future with low climate change mitigation -has the worst impacts on abundance-based biodiversity intactness with an average loss of 26% of intactness by 2100. However, a brighter future...

Agricultural Development and Land Use Change in India: A Scenario Analysis of Trade‐Offs Between UN Sustainable Development Goals (SDGs)

“…In consequence, according to the selected scenarios and the methodology used in our study, we can identify a clear trade‐off between the increasing food production in India and the protection of biodiversity. The calculated BII is below the values from Hill et al () and Newbold et al () who determine a BII of 0.485 for South Asia, though it is still of the same magnitude, so we feel confident it is a reasonable estimate. Also, the decrease of BII in our scenarios is larger than projected by Hill et al () under an SSP2 scenario in combination with RCP4.5 for that region.…”

“…The calculated BII is below the values from Hill et al () and Newbold et al () who determine a BII of 0.485 for South Asia, though it is still of the same magnitude, so we feel confident it is a reasonable estimate. Also, the decrease of BII in our scenarios is larger than projected by Hill et al () under an SSP2 scenario in combination with RCP4.5 for that region. The REF_HGEM scenario follows the relatively more severe RCP8.5 as represented by the HadGEM model, so these stronger climate change impacts on BII appear reasonable.…”

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

“…Initially, five aggregated land-uses were created from the GAM predictions: cropping, forest, non-forest, grazing and urban. This is the same approach reported in Kim et al (2018), following previous works (Hill et al, 2018;Newbold et al, 2016) based on the PREDICTS database (Hudson et al, 2017). Once the ecologically scaled environments and the habitat condition surface are generated (a), they are used to estimate plant persistence under scenarios of land-use change ( Once the present-day land-use classes were downscaled, we created a map of habitat condition; this was achieved by multiplying, for each grid cell, the percentage coverage of each land-use class by a coefficient representing the proportional species richness expected to be retained in that class within the forested or non-forested areas classified in LUH2 (Table S1).…”

“…Once the ecologically scaled environments and the habitat condition surface are generated (a), they are used to estimate plant persistence under scenarios of land-use change ( Once the present-day land-use classes were downscaled, we created a map of habitat condition; this was achieved by multiplying, for each grid cell, the percentage coverage of each land-use class by a coefficient representing the proportional species richness expected to be retained in that class within the forested or non-forested areas classified in LUH2 (Table S1). This is the same approach reported in Kim et al (2018), following previous works (Hill et al, 2018;Newbold et al, 2016) based on the PREDICTS database (Hudson et al, 2017).…”

“…This approach assumes that differences in condition from land-use change over time (at a single location) are of a similar magnitude to differences observed between land uses at a single point in time (across different locations), as assessed by the PREDICTS model(Hill et al, 2018;Newbold et al, 2016). The final results are past and future projections of habitat condition values at a resolution of 30 arc-seconds.…”