MInOSSE: A new method to reconstruct geographic ranges of fossil species

The earliest Ethiopian wolf: implications for the species evolution and its future survival

In 2017, a hemimandible (MW5-B208), corresponding to the Ethiopian wolf (Canis simensis), was found in a stratigraphically-controlled and radio-isotopically-dated sequence of the Melka Wakena paleoanthropological site-complex, on the Southeastern Ethiopian Highlands, ~ 2300 m above sea level. The specimen is the first and unique Pleistocene fossil of this species. Our data provide an unambiguous minimum age of 1.6-1.4 Ma for the species' presence in Africa and constitutes the first empirical evidence that supports molecular interpretations. Currently, C. simensis is one of the most endangered carnivore species of Africa. Bioclimate niche modeling applied to the time frame indicated by the fossil suggests that the lineage of the Ethiopian wolf faced severe survival challenges in the past, with consecutive drastic geographic range contractions during warmer periods. These models help to describe future scenarios for the survival of the species. Projections ranging from most pessimistic to most optimistic future climatic scenarios indicate significant reduction of the already-deteriorating territories suitable for the Ethiopian Wolf, increasing the threat to the specie's future survival. Additionally, the recovery of the Melka Wakena fossil underscores the importance of work outside the East African Rift System in research of early human origins and associated biodiversity on the African continent.

Taxon Cycles in Neotropical Mangroves

The concept of the taxon cycle involves successive range expansions and contractions over time, through which a species can indefinitely maintain its core distribution. Otherwise, it becomes extinct. Taxon cycles have been defined mostly for tropical island faunas; examples from continental areas are scarce, and similar case studies for plants remain unknown. Most taxon cycles have been identified on the basis of phylogeographic studies, and straightforward empirical evidence from fossils is lacking. Here, empirical fossil evidence is provided for the recurrent Eocene to the present expansion/contraction cycles in a mangrove taxon (Pelliciera) after a Neotropical-wide study of the available pollen records. This recurrent behavior is compatible with the concept of the taxon cycle from biogeographical, chronological and ecological perspectives. The biotic and abiotic drivers potentially involved in the initiation and maintenance of the Pelliciera expansion/contraction cycles are analyzed, and the ecological and evolutionary implications are discussed. Whether this could be a trend toward extinction is considered under the predictions of the taxon cycle theory. The recurrent expansion and contraction cycles identified for Pelliciera have strong potential for being the first empirically and unequivocally documented taxon cycles and likely the only taxon cycles documented to date for plants.

Integrating geographic ranges across temporal scales

Geographic ranges are a fundamental unit of biogeography and macroecology. Increasingly, paleontologists and ecologists alike are reconstructing geographic ranges of species from fossils, in order to understand the long-term processes governing biogeographic and macroevolutionary patterns. As these reconstructions have become increasingly common, uncertainty has arisen over the equivalency of paleo-ranges and modern ranges. Here, we argue geographic ranges are time-averaged at all temporal scales, and reflect the biotic and abiotic processes operating across the equivalent range of time and space scales. This conceptual framework integrates the study of geographic ranges reconstructed using modern and ancient data, and highlights the potential for ranges to illuminate processes responsible for diversity patterns over intervals spanning days to tens of millions of years of Earth history.

A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer

Many areas around the world are affected by Groundwater Level rising (GWLr). One of the most severe consequences of this phenomenon is Groundwater Flooding (GF), with serious impacts for the human and natural environment. In Europe, GF has recently received specific attention with Directive 2007/60/EC, which requires Member States to map GF hazard and propose measures for risk mitigation. In this paper a methodology has been developed for Groundwater Flooding Susceptibility (GFS) assessment, using for the first time Spatial Distribution Models. These Machine Learning techniques connect occurrence data to predisposing factors (PFs) to estimate their distributions. The implemented methodology employs aquifer type, depth of piezometric level, thickness and hydraulic conductivity of unsaturated zone, drainage density and land-use as PFs, and a GF observations inventory as occurrences. The algorithms adopted to perform the analysis are Generalized Boosting Model, Artificial Neural Network and Maximum Entropy. Ensemble Models are carried out to reduce the uncertainty associated with each algorithm and increase its reliability. GFS is mapped by choosing the ensemble model with the best predictivity performance and dividing occurrence probability values into five classes, from very low to very high susceptibility, using Natural Breaks classification. The methodology has been tested and statistically validated in an area of 14,3 km² located in the Metropolitan City of Naples (Italy), affected by GWLr since 1990 and GF in buildings and agricultural soils since 2007. The results of modeling show that about 93% of the inventoried points fall in the high and very high GFS classes, and piezometric level depth, thickness of unsaturated zone and drainage density are the most influencing PFs, in accordance with field observations and the triggering mechanism of GF. The outcomes provide a first step in the assessment of GF hazard and a decision support tool to local authorities for GF risk management.