Robert Beyer’s research while affiliated with University of Cambridge and other places

Understanding and forecasting human mobility in response to climatic and environmental changes has become a subject of substantial political, societal, and academic interest. Quantitative models exploring the relationship between climatic factors and migration patterns have been developed since the early 2000s; however, different models have produced results that are not always consistent with one another or robust enough to provide actionable insights into future dynamics. Here we examine weaknesses of classical methods and identify next-generation approaches with the potential to close existing knowledge gaps. We propose six priorities for the future of climate mobility modeling: (i) the use of non-linear machine-learning rather than linear methods, (ii) the prioritization of explaining the observed data rather than testing statistical significance of predictors, (iii) the consideration of relevant climate impacts rather than temperature- and precipitation-based metrics, (iv) the examination of heterogeneities, including across space and demographic groups rather than aggregated measures, (v) the investigation of temporal migration dynamics rather than essentially spatial patterns, (vi) the use of better calibration data, including disaggregated and within-country flows. Improving both methods and data to accommodate the high complexity and context-specificity of climate mobility will be crucial for establishing the scientific consensus on historical trends and future projections that has eluded the discipline thus far.

The recent development of continuous paleoclimatic reconstructions covering hundreds of thousands of years paved the way for a large number of studies from disciplines ranging from paleoecology to conservation, archaeology to population genetics, macroevolution to anthropology, and human evolution to linguistics. The downside to this process is that (paleo)climatic data are stored in large and complex file formats, and they can be challenging to handle for scholars unfamiliar with them. Here we present pastclim, an R package facilitating the access and use of paleoclimatic reconstructions (https://evolecolgroup.github.io/pastclim/index.html). It currently gives direct access to two such datasets, covering respectively the last 120,000 and 800,000 years, and a vignette provides instructions on how to include additional ones. We are expecting the number of datasets available to increase in the following months. The package contains a set of R functions to quickly and easily recover the climate for time periods of interest either for the whole world or specific areas, extract data from locations scattered in space and/or time, retrieve time series from individual sites, and manage the ice or land coverage, offering a handy platform to include the climate of the past into existing or new analyses and pipelines.

The recent development of continuous paleoclimatic reconstructions covering hundreds of thousands of years paved the way for a large number of studies from disciplines ranging from paleoecology to archaeology, conservation to population genetics, macroevolution to anthropology and human evolution to linguistics. Unfortunately, (paleo)climatic data can be challenging to extract and analyze for scholars unfamiliar with such specific file formats. Here we present pastclim, an R package facilitating the access and use of paleoclimatic reconstructions. It currently includes two of such datasets, covering respectively the last 120 000 and 800 000 years, and a vignette provides instructions on how to include additional datasets. The package contains a set of functions to quickly and easily recover the climate for time periods of interest either for the whole world or specific areas, extract data from locations scattered in space and/or time, retrieve time series from individual sites, and manage the ice or land coverage, offering a handy platform to include the climate of the past into existing or new analyses and pipelines.

Many companies have made zero-deforestation commitments (ZDCs) to reduce carbon emissions and biodiversity losses linked to tropical commodities. However, ZDCs conserve areas primarily based on tree cover and aboveground carbon, potentially leading to the unintended consequence that agricultural expansion could be encouraged in biomes outside tropical rainforest, which also support important biodiversity. We examine locations suitable for zero-deforestation expansion of commercial oil palm, which is increasingly expanding outside the tropical rainforest biome, by generating empirical models of global suitability for rainfed and irrigated oil palm. We find that tropical grassy and dry forest biomes contain >50% of the total area of land climatically suitable for rainfed oil palm expansion in compliance with ZDCs (following the High Carbon Stock Approach; in locations outside urban areas and cropland), and that irrigation could double the area suitable for expansion in these biomes. Within these biomes, ZDCs fail to protect areas of high vertebrate richness from oil palm expansion. To prevent unintended consequences of ZDCs and minimize the environmental impacts of oil palm expansion, policies and governance for sustainable development and conservation must expand focus from rainforests to all tropical biomes.

The recent development of continuous paleoclimatic reconstructions covering hundreds of thousands of years paved the way to a large number of studies from disciplines ranging from paleoecology to linguistics, from archaeology to conservation and from population genetics to human evolution. Unfortunately, such climatic data can be challenging to extract and analyze for scholars unfamiliar with such specific climatic file formats. Here we present pastclim , an R package facilitating the access and use of two sets of paleoclimatic reconstructions covering respectively the last 120,000 and 800,000 years. The package contains a set of functions allowing to quickly and easily recover the climate for the whole world or specific areas for time periods of interest, extract data from locations scattered in space and/or time, retrieve time series from individual sites, and easily manage the ice or land coverage. The package can easily be adapted to paleoclimatic reconstructions different from the ones already included, offering a handy platform to include the climate of the past into existing analyses and pipelines.

African paleoanthropological studies typically focus on regions of the continent such as Eastern, Southern and Northern Africa, which hold the highest density of Pleistocene archaeological sites. Nevertheless, lesser known areas such as West Africa also feature a high number of sites. Here, we present a high-resolution map synthesising all well contextualised Pleistocene archaeological sites present in Sub-Saharan West Africa. A detailed elevation and ecoregional map was developed and correlated with palaeoanthropological sites. This map is supplemented with 1,000- and 2000-year interval climate reconstructions over the last 120,000 years for three subregions of high archaeological interest. The presented archaeological sites were compiled by reviewing published literature, and selected based on: (1) documented archaeological stratification or >10 characteristic artefacts, (2) published coordinates, and (3) published chronometric ages or relative dating. The data presented here elucidates the current state of knowledge of Pleistocene West Africa, highlighting the regional potential for human evolutionary studies.

Agricultural production has replaced natural ecosystems across the planet, becoming a major driver of carbon emissions, biodiversity loss, and freshwater consumption. Here we combined global crop yield and environmental data in a ~1-million-dimensional mathematical optimisation framework to determine how optimising the spatial distribution of global croplands could reduce environmental impacts whilst maintaining current crop production levels. We estimate that relocating current croplands to optimal locations, whilst allowing ecosystems in then-abandoned areas to regenerate, could simultaneously decrease the current carbon, biodiversity, and irrigation water footprint of global crop production by 71%, 87%, and 100%, respectively, assuming high-input farming on newly established sites. The optimal global distribution of crops is largely similar for current and end-of-century climatic conditions across emission scenarios. Substantial impact reductions could already be achieved by relocating only a small proportion of worldwide crop production, relocating croplands only within national borders, and assuming less intensive farming systems. Optimising the spatial distribution of global croplands could substantially reduce carbon emissions and biodiversity loss associated with rain-fed crop production, according to a mathematical framework applied to environmental impact and crop yield data

The major social and economic impacts of international migration have led to a strong interest in better understanding the drivers of cross-border movement. Quantitative models have sought to explain global migration patterns in terms of economic, social, climatic, and other variables, and future projections of these variables are increasingly being used to forecast international migration flows. An important implicit assumption in the most widely used class of these approaches, so-called gravity models, is that their parameterisation based on panel data enables them to describe the effects of predictor variables on migration flows across both space and time, i.e., that they explain flow variation both across country pairs at a given time and across time for a given country pair. Here we show that this assumption does not hold. Whilst gravity models describe spatial patterns of international migration very well, they fail to capture even basic temporal dynamics, indeed, often worse than even the time-invariant average of the historical flows. We show that standard validation techniques have been unable to detect this important limitation of gravity models due to the different orders of magnitude of migration flows across spatial corridors, on the one hand, and over time, on the other hand. Our analysis suggests that gravity-model-based inferences about the effects that certain variables have had, or will have, on international migration over time may in reality represent statistical artefacts rather than true mechanisms. We argue that future predictions based on gravity models lack statistical support and that, in its current form, this class of models is not suited for informing policy makers about migration trajectories in the coming years and decades.

Significance We combine tracks of a long-distance migratory bird with high–temporal resolution climate data to reconstruct habitat availability month by month for the past 120,000 y. The seasonal changes of suitable habitat in the past imply that continued seasonal migration was necessary during the glacial maxima. Genomic-based estimates of effective population size indicate that more generally migratory lifestyles can be beneficially adapted to various climatic conditions. Our results provide a major step forward in understanding how migratory species will fare in the future and have important implications for how we understand the role of migration in the distribution of species and potentially speciation.