Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift. Species Distribution Models (SDMs) can help forecast such range shift responses. For few species, both correlative and mechanistic SDMs were built, but allis shad (Alosa alosa), an endangered anadromous fish species, is one of them. The main purpose of this study was to provide a framework for joint analyses of correlative and mechanistic SDMs projections in order to strengthen conservation measures for species of conservation concern. Guidelines for joint representation and subsequent interpretation of models outputs were defined and applied. The present joint analysis was based on the novel mechanistic model GR3D (Global Repositioning Dynamics of Diadromous fish Distribution) which was parameterized on allis shad and then used to predict its future distribution along the European Atlantic coast under different climate change scenarios (RCP 4.5 and RCP 8.5). We then used a correlative SDM for this species to forecast its distribution across the same geographic area and under the same climate change scenarios. First, projections from correlative and mechanistic models provided congruent trends in probability of habitat suitability and population dynamics. This agreement was preferentially interpreted as referring to the species vulnerability to climate change. Climate change could not be accordingly listed as a major threat for allis shad. The congruence in predicted range limits between SDMs projections was the next point of interest. The difference, when noticed, required to deepen our understanding of the niche modelled by each approach. In this respect, the relative position of the northern range limit between the two methods strongly suggested here that a key biological process related to intraspecific variability was potentially lacking in the mechanistic SDM. Based on our knowledge, we hypothesized that local adaptations to cold temperatures deserved more attention in terms of modelling, but further in conservation planning as well.
Irstea, EABX, Aquatic Ecosystems and Global Changes research unit, 50 avenue de Verdun, Gazinet Cestas, F-33612, Cestas, France;Irstea, EABX, Aquatic Ecosystems and Global Changes research unit, 50 avenue de Verdun, Gazinet Cestas, F-33612, Cestas, France;Irstea, EABX, Aquatic Ecosystems and Global Changes research unit, 50 avenue de Verdun, Gazinet Cestas, F-33612, Cestas, France;Irstea, LISC, Complex Systems Engineering Laboratory, 9 avenue Blaise Pascal–CS 20085, 63178, Aubière, France;Irstea, LISC, Complex Systems Engineering Laboratory, 9 avenue Blaise Pascal–CS 20085, 63178, Aubière, France;Irstea, LISC, Complex Systems Engineering Laboratory, 9 avenue Blaise Pascal–CS 20085, 63178, Aubière, France;Irstea, EABX, Aquatic Ecosystems and Global Changes research unit, 50 avenue de Verdun, Gazinet Cestas, F-33612, Cestas, France;Irstea, EABX, Aquatic Ecosystems and Global Changes research unit, 50 avenue de Verdun, Gazinet Cestas, F-33612, Cestas, France
Recommended Citation:
Thibaud Rougier,Géraldine Lassalle,Hilaire Drouineau,et al. The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species[J]. PLOS ONE,2015-01-01,10(10)