Limits...
The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species.

Rougier T, Lassalle G, Drouineau H, Dumoulin N, Faure T, Deffuant G, Rochard E, Lambert P - PLoS ONE (2015)

Bottom Line: Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift.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.

View Article: PubMed Central - PubMed

Affiliation: Irstea, EABX, Aquatic Ecosystems and Global Changes research unit, 50 avenue de Verdun, Gazinet Cestas, F-33612, Cestas, France.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Heat map representing the probability classes for the 73 basins at the species historical core distribution range for the two times steps, i.e. 1901–1910 and 2070–2100, and for the two modelling approaches, i.e. the correlative and mechanistic SDMs, and the two climate change scenarios, i.e. RCP 4.5 and 8.5. Five classes 0,] 0–0.25],] 0.25–0.53],] 0.53–0.75], and]0.75–1] were represented by a continuous grey gradient with black used for the highest probability class] 0.75–1]. Basins were ordered along a latitudinal gradient (i.e., latitude at the basin outlet) from South (i.e., Guadalquivir) to North (i.e., Drammenselva).
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pone.0139194.g004: Heat map representing the probability classes for the 73 basins at the species historical core distribution range for the two times steps, i.e. 1901–1910 and 2070–2100, and for the two modelling approaches, i.e. the correlative and mechanistic SDMs, and the two climate change scenarios, i.e. RCP 4.5 and 8.5. Five classes 0,] 0–0.25],] 0.25–0.53],] 0.53–0.75], and]0.75–1] were represented by a continuous grey gradient with black used for the highest probability class] 0.75–1]. Basins were ordered along a latitudinal gradient (i.e., latitude at the basin outlet) from South (i.e., Guadalquivir) to North (i.e., Drammenselva).

Mentions: Regarding trends in predicted probabilities (Table 2), one strong and common feature of SDMs predictions was probabilities of basins to be suitable for allis shad (psuit2100,j predicted by the correlative model) and to sustain stable population (psust2100,j predicted by the mechanistic model) remaining high around 2100 under both RCP scenarios (Fig 3A and 3B). The average probability of a basin to be suitable around 2100 (i.e., ) was 0.71 and 0.64 under RCP 4.5 and 8.5 respectively. The average probability of a basin to sustain a stable population (i.e., ) was 0.76 and 0.80 under RCP 4.5 and 8.5 respectively (Fig 3A and 3B). In addition, and exhibited few changes compared to the 1901–1910 period as and equaled 0.74 and 0.69 respectively (Fig 3A and 3B). More specifically, for basins at the core of the species distribution, the GR3D model showed probabilities remaining stable and close to 100% under both RCP scenarios (Fig 3B and Fig 4). Up to the Sienne basin in France, reproduction was predicted to occur every year over the 30-year period for the two RCP scenarios (Fig 3B and Fig 4).


The Combined Use of Correlative and Mechanistic Species Distribution Models Benefits Low Conservation Status Species.

Rougier T, Lassalle G, Drouineau H, Dumoulin N, Faure T, Deffuant G, Rochard E, Lambert P - PLoS ONE (2015)

Heat map representing the probability classes for the 73 basins at the species historical core distribution range for the two times steps, i.e. 1901–1910 and 2070–2100, and for the two modelling approaches, i.e. the correlative and mechanistic SDMs, and the two climate change scenarios, i.e. RCP 4.5 and 8.5. Five classes 0,] 0–0.25],] 0.25–0.53],] 0.53–0.75], and]0.75–1] were represented by a continuous grey gradient with black used for the highest probability class] 0.75–1]. Basins were ordered along a latitudinal gradient (i.e., latitude at the basin outlet) from South (i.e., Guadalquivir) to North (i.e., Drammenselva).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4591278&req=5

pone.0139194.g004: Heat map representing the probability classes for the 73 basins at the species historical core distribution range for the two times steps, i.e. 1901–1910 and 2070–2100, and for the two modelling approaches, i.e. the correlative and mechanistic SDMs, and the two climate change scenarios, i.e. RCP 4.5 and 8.5. Five classes 0,] 0–0.25],] 0.25–0.53],] 0.53–0.75], and]0.75–1] were represented by a continuous grey gradient with black used for the highest probability class] 0.75–1]. Basins were ordered along a latitudinal gradient (i.e., latitude at the basin outlet) from South (i.e., Guadalquivir) to North (i.e., Drammenselva).
Mentions: Regarding trends in predicted probabilities (Table 2), one strong and common feature of SDMs predictions was probabilities of basins to be suitable for allis shad (psuit2100,j predicted by the correlative model) and to sustain stable population (psust2100,j predicted by the mechanistic model) remaining high around 2100 under both RCP scenarios (Fig 3A and 3B). The average probability of a basin to be suitable around 2100 (i.e., ) was 0.71 and 0.64 under RCP 4.5 and 8.5 respectively. The average probability of a basin to sustain a stable population (i.e., ) was 0.76 and 0.80 under RCP 4.5 and 8.5 respectively (Fig 3A and 3B). In addition, and exhibited few changes compared to the 1901–1910 period as and equaled 0.74 and 0.69 respectively (Fig 3A and 3B). More specifically, for basins at the core of the species distribution, the GR3D model showed probabilities remaining stable and close to 100% under both RCP scenarios (Fig 3B and Fig 4). Up to the Sienne basin in France, reproduction was predicted to occur every year over the 30-year period for the two RCP scenarios (Fig 3B and Fig 4).

Bottom Line: Species can respond to climate change by tracking appropriate environmental conditions in space, resulting in a range shift.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.

View Article: PubMed Central - PubMed

Affiliation: Irstea, EABX, Aquatic Ecosystems and Global Changes research unit, 50 avenue de Verdun, Gazinet Cestas, F-33612, Cestas, France.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus