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

The geographical extent of the correlative and mechanistic modelling approaches with the allis shad historical distribution.Light grey and dark grey polygons corresponded to the 197 basins of EuroDiad 3.2 considered in the correlative SDM. Light grey and dark grey polygons represented also the allis shad former absences and presences around 1900, respectively. The area delineated by a solid black line denoted the 73 basins taken into account in the GR3D model application.
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pone.0139194.g002: The geographical extent of the correlative and mechanistic modelling approaches with the allis shad historical distribution.Light grey and dark grey polygons corresponded to the 197 basins of EuroDiad 3.2 considered in the correlative SDM. Light grey and dark grey polygons represented also the allis shad former absences and presences around 1900, respectively. The area delineated by a solid black line denoted the 73 basins taken into account in the GR3D model application.

Mentions: Allis shad (Alosa alosa) is an anadromous clupeid that spawns in the main stem of rivers. Fish migrate to sea during their first year where they grow and then return to fresh waters to spawn between 3 and 6 years old [49]. The species distribution (originally along the Atlantic coast from Norway to Morocco; Fig 2) has decreased considerably since the middle of the 20th century, mainly because of overfishing, dam constructions, water quality degradation and deterioration of spawning habitats [22]. Currently, populations of allis shad exist along the northeastern Atlantic coast in some large rivers of France (i.e., Loire, Gironde-Garonne-Dordogne, and Adour) and Portugal (i.e., Minho and Lima) [38]. Despite the implementation of protective measures, this species appears to have been in serious decline for a number of years [22, 31]. Allis shad has lost nearly half of its populations in Europe since the mid-20th century [50] and, for the Gironde population, long considered as the reference, a total fishing moratorium was implemented since 2008 due to a dramatic drop in landings [31]. Biology and ecology of allis shad have therefore received a great deal of attention in the last 30 years [30, 37, 51–56] and several studies also dealt with its population dynamics [31, 57]. This species is the focus of an ongoing stocking program (started in 2008) in the Rhine River (Germany) with juveniles coming from assisted reproduction of wild spawners from the Gironde-Garonne-Dordogne basin (France) [33]; http://www.lanuv.nrw.de/alosa-alosa/en/.


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)

The geographical extent of the correlative and mechanistic modelling approaches with the allis shad historical distribution.Light grey and dark grey polygons corresponded to the 197 basins of EuroDiad 3.2 considered in the correlative SDM. Light grey and dark grey polygons represented also the allis shad former absences and presences around 1900, respectively. The area delineated by a solid black line denoted the 73 basins taken into account in the GR3D model application.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139194.g002: The geographical extent of the correlative and mechanistic modelling approaches with the allis shad historical distribution.Light grey and dark grey polygons corresponded to the 197 basins of EuroDiad 3.2 considered in the correlative SDM. Light grey and dark grey polygons represented also the allis shad former absences and presences around 1900, respectively. The area delineated by a solid black line denoted the 73 basins taken into account in the GR3D model application.
Mentions: Allis shad (Alosa alosa) is an anadromous clupeid that spawns in the main stem of rivers. Fish migrate to sea during their first year where they grow and then return to fresh waters to spawn between 3 and 6 years old [49]. The species distribution (originally along the Atlantic coast from Norway to Morocco; Fig 2) has decreased considerably since the middle of the 20th century, mainly because of overfishing, dam constructions, water quality degradation and deterioration of spawning habitats [22]. Currently, populations of allis shad exist along the northeastern Atlantic coast in some large rivers of France (i.e., Loire, Gironde-Garonne-Dordogne, and Adour) and Portugal (i.e., Minho and Lima) [38]. Despite the implementation of protective measures, this species appears to have been in serious decline for a number of years [22, 31]. Allis shad has lost nearly half of its populations in Europe since the mid-20th century [50] and, for the Gironde population, long considered as the reference, a total fishing moratorium was implemented since 2008 due to a dramatic drop in landings [31]. Biology and ecology of allis shad have therefore received a great deal of attention in the last 30 years [30, 37, 51–56] and several studies also dealt with its population dynamics [31, 57]. This species is the focus of an ongoing stocking program (started in 2008) in the Rhine River (Germany) with juveniles coming from assisted reproduction of wild spawners from the Gironde-Garonne-Dordogne basin (France) [33]; http://www.lanuv.nrw.de/alosa-alosa/en/.

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