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

Abstract

Estimating fossil species' geographic range is a major goal for paleobiologists. In the deep time, this is most commonly performed by using polygon‐based methods such as the minimum convex polygon (MCP) or the Alpha‐Hull. Unfortunately, such methods provide a poor representation of the fossil species' actual range, because they are unable to take control of the severe stochastic and taphonomic biases. Here, we introduce MInOSSE (massively interpolated occurrences for species spatial estimation), a model‐based method that combines a machine learning algorithm and geostatistical approaches to reconstruct a target fossil species' geographic ranges by relying on the distribution of other coeval species and without using environmental predictors. We tested MInOSSE by using many simulated fossil species' distributions, comparing its performance with MCP and Alpha‐Hull outcomes and applying it to real case studies. In all simulations, MInOSSE outperformed the competing methods. Interestingly, the superior performance of MInOSSE becomes more apparent when the fossil record of the target species is scarce, that is, when appropriate range reconstruction is most problematic with polygon‐based methods. MInOSSE is a powerful tool for researchers interested in studying geographic range evolution, effects of range size on extinction risk, as well as biodiversity dynamics and macroecological patterns in the deep time.

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

“…The discovery of the ~1.6–1.4 Ma specimen MW5-B208, coupled with the genetic data that suggest its speciation at an even earlier date, suggests that populations of the Ethiopian wolf likely survived unfavorable environments throughout a number of climatic cycles since their arrival to the Ethiopian highlands. Additional information, with the application of new methods to reconstruct the geographic distributions of past species 35 , is needed to understand how C. simensis overcame past periods of habitat loss, which might help in building conservation programs for this iconic animal.…”

“…The reconstructions of past and future spatial distribution of the Ethiopian wolf and related analyses require mathematical modeling of the species’ bioclimatic niche followed by the projection of this niche onto the geographic space. This procedure is known as Species Distribution Modeling (SDM) 50 and is widely employed for both living 51 and fossil species 35 , 52 . The output of this projection is a map of the species’ Habitat Suitability Index (HSI map), which is the reconstruction of the territories suitable to the species’ habitat in the geographic space and that can be used to derive the species’ geographic range and metrics of the landscape connectivity structure.…”

Taxon Cycles in Neotropical Mangroves

“…In some cases, there was not enough chronological precision to resolve between them and longer ranges were used (e.g., Oligo–Miocene, Mio–Pliocene). The lack of site coordinates in many original references prevented precise estimations of range sizes using statistical methods [ 55 , 56 , 57 ].…”

Megafrugivores as fading shadows of the past: extant frugivores and the abiotic environment as the most important determinants of the distribution of palms in Madagascar

“…species found in the same fossil site) and without using environmental predictors. This approach is implemented in the R package ‘Ecopast' (Carotenuto et al 2020b). More details on this method are provided in the Supporting information.…”

“…Fossil records of elephant birds were collected from Hansford and Turvey (2018). We used MInOSSE (Carotenuto et al 2020a), a model-based method that reconstructs the past ranges of extinct fauna, by relying on the distribution of other coeval species (i.e. species found in the same fossil site) and without using environmental predictors.…”