The greening of Arabia: multiple opportunities for human occupation of the Arabian peninsula during the Late Pleistocene inferred from an ensemble of climate model simulations

Climate models are potentially useful tools for addressing human dispersals and demographic change. The Arabian Peninsula is becoming increasingly significant in the story of human dispersals out of Africa during the Late Pleistocene. Although characterised largely by arid environments today, emerging climate records indicate that the peninsula was wetter many times in the past, suggesting that the region may have been inhabited considerably more than hitherto thought. Explaining the origins and spatial distribution of increased rainfall is challenging because palaeoenvironmental research in the region is in an early developmental stage. We address environmental oscillations by assembling and analysing an ensemble of five global climate models (CCSM3, COSMOS, HadCM3, KCM, and NorESM). We focus on precipitation, as the variable is key for the development of lakes, rivers and savannas. The climate models generated here were compared with published palaeoenvironmental data such as palaeolakes, speleothems and alluvial fan records as a means of validation. All five models showed, to varying degrees, that the Arabia Peninsula was significantly wetter than today during the Last Interglacial (130 ka and 126/125 ka timeslices), and that the main source of increased rainfall was from the North African summer monsoon rather than the Indian Ocean monsoon or from Mediterranean climate patterns. Where available, 104 ka (MIS 5c), 56 ka (early MIS 3) and 21 ka (LGM) timeslices showed rainfall was present but not as extensive as during the Last Interglacial. The results favour the hypothesis that humans potentially moved out of Africa and into Arabia on multiple occasions during pluvial phases of the Late Pleistocene.

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.