Species–area relationships of the Aegean, a comparative approach between six taxa

Leonidas Maroulis,Moisis Mylonas,Katerina Vardinoyannis

doi:10.21425/f5fbg52929

Abstract

Islands occupy a proportionately small area on Earth, however they play a crucial role in Ecology and Biogeography, as they constitute “natural laboratories”. The increased number of species, with increasing island area, is such a commonly observed pattern that it has been labelled as one of the few laws of ecology. The Aegean archipelago is of broad biogeographical interest, as it has a considerable number of islands in addition to a rich paleogeographical and geological history, while being divided among three continents (Europe, Asia, Africa). As a result, the composition of life in the Aegean is dominated by species of European, Asian, African origin as well as species endemic in the archipelago. In this framework, we approached the species–area relationship (SAR) of the Aegean islands for six different organismic groups (birds, herptiles, snails, isopods, tenebrionids and chilopods) and 20 different models. The aim was to determine which model(s) perform better for each taxon and also to compare the z and C parameters of the power model between animal groups, which are the only model parameters to date that have been linked with biological processes. We compared the relationship across different taxa for the entire archipelago and for the exact same islands, in two subgroups with similar paleogeographic history and environmental conditions in the central and eastern Aegean. For the taxonomic groups that were examined a strong correlation between the number of species and area was found, except for chilopods and herptiles. Although there is no universal best model for the SAR of the Aegean, the power model performed better for invertebrates, whereas concerning vertebrates there was more ambiguity in the shape of the relationship.

Highlights

IntroductionMost ubiquitous patterns that has been recognised in ecology is the increase in species richness (S) with increasing sampling area (A): the species–area relationship (SAR)
Biological research on islands has been playing a crucial role in the deeper understanding of fundamental characteristics in ecology, evolution and biogeography.Islands are considered to be extremely important in biogeography as they have delineated geographical borders, and are isolated and simple systems, making them ideal “natural laboratories”e-ISSN: 1948-6596 https://escholarship.org/uc/fb doi:10.21425/F5FBG52929One of the oldest, most ubiquitous patterns that has been recognised in ecology is the increase in species richness (S) with increasing sampling area (A): the species–area relationship (SAR)
Conservation biologists frequently rely on the species–area relationship to predict changes in species diversity resulting from habitat loss, and to develop strategies for conserving biological diversity in spatially limited reserves and fragmented ecosystems (Brooks et al 1997, Smith 2010, Gerstner et al 2014, Halley et al 2014, de la Sancha and Boyle 2019)

Summary

Introduction

Most ubiquitous patterns that has been recognised in ecology is the increase in species richness (S) with increasing sampling area (A): the species–area relationship (SAR). The SAR has been troubling scientists for more than 150 years (de Candolle 1855, MacArthur and Wilson 1967, Connor and McCoy 1979, He and Legendre 1996), it has broad applications in ecology and conservation and has been used as the basis for protected area design, prediction of species extinctions resulting from the loss of native habitat and estimating regional diversity from smaller-scale sample data (Guilhaumon et al 2010). For biogeography and in species–area relationship studies models are fitted to species–area plots for descriptive, explicative and predictive purposes. Explicative purposes -which are predominately the primary goals of this study- refer to the understanding of how observed or expected patterns in nature affect curve shape and the biological interpretation of parameters and their values. Predictive purposes include extrapolation of total species numbers, prediction of extinction caused by fragmentation and habitat loss, and the identification of species hotspots (Tjørve 2009)

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