Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity

Abstract

SignificanceUnderstanding how land-use and climate change interact is of major importance to project the future of biodiversity. We assessed how the global species richness of vertebrates may become affected by these two threats, especially under a scenario following the Paris Agreement, which aims to limit global warming to 2 °C or even 1.5 °C. We found that combined effects of climate and land-use change will be most severe under such a scenario, due to the massive expansion of bioenergy cropland for climate change mitigation. While our findings suggest that the Paris goals will reduce direct climate change impacts on biodiversity, biodiversity will suffer as severely as under a high-level emission scenario if bioenergy remains a major component of climate change mitigation strategies.

A comparison of macroecological and stacked species distribution models to predict future global terrestrial vertebrate richness

“…The methods for species data, climate data and S-SDMs were performed following the same procedure as Hof et al (2018) and thus are only described briefly here.…”

“…In order to avoid the violation of key statistical assumptions (Dormann, 2007), spatial autocorrelation was again reduced by applying the ecoregion-blocking approach described above (Bagchi et al, 2013;Hof et al, 2018). The resulting species richness values per grid cell were then used as response variable for the MEMs using the same explanatory variables as for the SDMs to allow for a direct comparison.…”

“…see Barbosa, Estrada, Márquez, Purvis, & Orme, 2012;Fourcade, 2016;Herkt et al, 2017;Meyer et al, 2015), coming to mixed conclusions; however, EOO range maps are still widely used in macroecological research (Belmaker & Jetz, 2015;Hof et al, 2018;Slavenko & Meiri, 2015;Thuiller et al, 2019;Torres-Romero & Olalla-Tárraga, 2015;Zurell et al, 2018). see Barbosa, Estrada, Márquez, Purvis, & Orme, 2012;Fourcade, 2016;Herkt et al, 2017;Meyer et al, 2015), coming to mixed conclusions; however, EOO range maps are still widely used in macroecological research (Belmaker & Jetz, 2015;Hof et al, 2018;Slavenko & Meiri, 2015;Thuiller et al, 2019;Torres-Romero & Olalla-Tárraga, 2015;Zurell et al, 2018).…”

“…SDMs are a widely used tool for predicting species distributions in response to changing climate (Engler et al, 2011;Hof, Araújo, Jetz, & Rahbek, 2011;Hof et al, 2018; Morán-Ordóñez, Lahoz-Monfort, Elith, Wintle, & Guisan, 2017;Peterson et al, 2002;Thuiller, Guéguen, Renaud, Karger, & Zimmermann, 2019;Zurell, Graham, Gallien, Thuiller, & Zimmermann, 2018). SDMs are based on statistical correlations between species occurrences and environmental predictor variables (Elith & Leathwick, 2009;Guisan & Thuiller, 2005;Guisan & Zimmermann, 2000), which can then be transferred into future time periods to predict future species distributions under climate change.…”

Macroecology in the age of Big Data – Where to go from here?

“…The last decade has seen an increasing use of such integrative methods. Finally,Hof et al (2018) outlined opportunities and pitfalls in approaches that combine data on land-use and climate change to forecast biodiversity dynamics. Also, joint species distribution models (JSDMs) are increasingly used to model species jointly, rather than using data only from single species, which should support models on species distributions by using information from cooccurrence patterns(Clark, Gelfand, Woodall, & Zhu, 2014; e.g.…”