Limits...
Understanding complex biogeographic responses to climate change.

Seabra R, Wethey DS, Santos AM, Lima FP - Sci Rep (2015)

Bottom Line: Understanding whether those shifts are indeed contrary to climate change predictions involves understanding the most basic mechanisms determining the distribution of species.Temperature metrics have contrasting geographical patterns and latitude or the grand mean are poor predictors for many of them.Our data suggest that unless the appropriate metrics are analysed, the impact of climate change in even a single metric of a single stressor may lead to range shifts in directions that would otherwise be classified as "contrary to prediction".

View Article: PubMed Central - PubMed

Affiliation: 1] CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, 4485-661, Vairão, Portugal [2] Departamento de Biologia, Faculdade de Ciências da Universidade do Porto, R. Campo Alegre, s/n, 4169-007 Porto, Portugal.

ABSTRACT
Predicting the extent and direction of species' range shifts is a major priority for scientists and resource managers. Seminal studies have fostered the notion that biological systems responding to climate change-impacted variables (e.g., temperature, precipitation) should exhibit poleward range shifts but shifts contrary to that expectation have been frequently reported. Understanding whether those shifts are indeed contrary to climate change predictions involves understanding the most basic mechanisms determining the distribution of species. We assessed the patterns of ecologically relevant temperature metrics (e.g., daily range, min, max) along the European Atlantic coast. Temperature metrics have contrasting geographical patterns and latitude or the grand mean are poor predictors for many of them. Our data suggest that unless the appropriate metrics are analysed, the impact of climate change in even a single metric of a single stressor may lead to range shifts in directions that would otherwise be classified as "contrary to prediction".

No MeSH data available.


Climate change can generate complex biogeographic responses.Conceptual framework (a–c) and example built using real temperature data (d–f) illustrating the mechanism through which climate change may induce complex biogeographic responses. Black dots show the abundance of a hypothetical species in each location (A–O, see Fig. 1a), which results from the interplay of ‘winter minimum’ (blue areas) and ‘summer 5th percentile’ (dark orange areas). Light orange results from the overlap between blue and orange areas and shows the outcome of the Liebig’s law of the minimum. (a,d) show the initial conditions, (b,e) result from the monotonic increase of both winter minimum and summer 5th percentile (scenario of increased mean), and (c,f) from increase of one aspect of temperature and decrease of the other (scenario of increased variability but stable mean).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4526865&req=5

f2: Climate change can generate complex biogeographic responses.Conceptual framework (a–c) and example built using real temperature data (d–f) illustrating the mechanism through which climate change may induce complex biogeographic responses. Black dots show the abundance of a hypothetical species in each location (A–O, see Fig. 1a), which results from the interplay of ‘winter minimum’ (blue areas) and ‘summer 5th percentile’ (dark orange areas). Light orange results from the overlap between blue and orange areas and shows the outcome of the Liebig’s law of the minimum. (a,d) show the initial conditions, (b,e) result from the monotonic increase of both winter minimum and summer 5th percentile (scenario of increased mean), and (c,f) from increase of one aspect of temperature and decrease of the other (scenario of increased variability but stable mean).

Mentions: Furthermore, using a theoretical example we show that complex biogeographic responses to climate change can be interpreted by using the appropriate metrics (Fig. 2). We assume that the distribution of a theoretical species is determined by a group of relevant metrics (in this case the thermal extremes measured as ‘winter minimum’ and ‘summer 5th percentile’) and follows Liebig’s law of the minimum (i.e., at each location density is dependent on the least favourable relevant metric). In the simplest form, the distribution pattern will be determined by the least favourable of a number of relevant metrics (Fig. 2a, light orange area). If climate change results in a favourable monotonic change of all metrics (Fig. 2b), the extent of suitable locations increases and a range expansion can be expected — the “general perception” poleward scenario. However, studies have highlighted that climate change not only can result in increased mean, minimum and maximum temperatures but also in increased variability — and that the exact signature of climate change varies regionally18252627. In this case, if at least one metric becomes less favourable due to the increased variability, the whole distribution can be adversely affected, and an equatorward range contraction may occur (Fig. 2c). Using the metrics computed in this study it is possible to further expand the example. If the distribution of a species was found to be dependent on the interplay between extremes like ‘winter minimum’ and ‘summer 5th percentile’ the initial distribution pattern should include a gap at shore H, and a polar range limit at shore B (Fig. 2d). If both ‘winter minimum’ and ‘summer 5th percentile’ become warmer, a poleward range expansion can be expected (Fig. 2e), but if ‘summer 5th percentile’ becomes warmer while ‘winter minimum’ becomes colder, the harshness of winter conditions prevail over the favourable summers and a equatorward range contraction should occur (Fig. 2f). Interestingly, in a few locations suitability would actually increase because the limiting factor was ‘summer 5th percentile’ and not ‘winter minimum’ (shores L and N, Fig. 2h), highlighting the consequences of different mechanisms limiting species’ densities across different locations28. The crucial point is that if field surveys were to reveal an equatorward range contraction for this species, this range shift would not be contrary to predictions, as general perception would suggest. Instead, it would be consistent with the predicted direction of change for this biological system’s response to climate change, thus representing positive evidence towards the establishment of a link to climate change.


Understanding complex biogeographic responses to climate change.

Seabra R, Wethey DS, Santos AM, Lima FP - Sci Rep (2015)

Climate change can generate complex biogeographic responses.Conceptual framework (a–c) and example built using real temperature data (d–f) illustrating the mechanism through which climate change may induce complex biogeographic responses. Black dots show the abundance of a hypothetical species in each location (A–O, see Fig. 1a), which results from the interplay of ‘winter minimum’ (blue areas) and ‘summer 5th percentile’ (dark orange areas). Light orange results from the overlap between blue and orange areas and shows the outcome of the Liebig’s law of the minimum. (a,d) show the initial conditions, (b,e) result from the monotonic increase of both winter minimum and summer 5th percentile (scenario of increased mean), and (c,f) from increase of one aspect of temperature and decrease of the other (scenario of increased variability but stable mean).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526865&req=5

f2: Climate change can generate complex biogeographic responses.Conceptual framework (a–c) and example built using real temperature data (d–f) illustrating the mechanism through which climate change may induce complex biogeographic responses. Black dots show the abundance of a hypothetical species in each location (A–O, see Fig. 1a), which results from the interplay of ‘winter minimum’ (blue areas) and ‘summer 5th percentile’ (dark orange areas). Light orange results from the overlap between blue and orange areas and shows the outcome of the Liebig’s law of the minimum. (a,d) show the initial conditions, (b,e) result from the monotonic increase of both winter minimum and summer 5th percentile (scenario of increased mean), and (c,f) from increase of one aspect of temperature and decrease of the other (scenario of increased variability but stable mean).
Mentions: Furthermore, using a theoretical example we show that complex biogeographic responses to climate change can be interpreted by using the appropriate metrics (Fig. 2). We assume that the distribution of a theoretical species is determined by a group of relevant metrics (in this case the thermal extremes measured as ‘winter minimum’ and ‘summer 5th percentile’) and follows Liebig’s law of the minimum (i.e., at each location density is dependent on the least favourable relevant metric). In the simplest form, the distribution pattern will be determined by the least favourable of a number of relevant metrics (Fig. 2a, light orange area). If climate change results in a favourable monotonic change of all metrics (Fig. 2b), the extent of suitable locations increases and a range expansion can be expected — the “general perception” poleward scenario. However, studies have highlighted that climate change not only can result in increased mean, minimum and maximum temperatures but also in increased variability — and that the exact signature of climate change varies regionally18252627. In this case, if at least one metric becomes less favourable due to the increased variability, the whole distribution can be adversely affected, and an equatorward range contraction may occur (Fig. 2c). Using the metrics computed in this study it is possible to further expand the example. If the distribution of a species was found to be dependent on the interplay between extremes like ‘winter minimum’ and ‘summer 5th percentile’ the initial distribution pattern should include a gap at shore H, and a polar range limit at shore B (Fig. 2d). If both ‘winter minimum’ and ‘summer 5th percentile’ become warmer, a poleward range expansion can be expected (Fig. 2e), but if ‘summer 5th percentile’ becomes warmer while ‘winter minimum’ becomes colder, the harshness of winter conditions prevail over the favourable summers and a equatorward range contraction should occur (Fig. 2f). Interestingly, in a few locations suitability would actually increase because the limiting factor was ‘summer 5th percentile’ and not ‘winter minimum’ (shores L and N, Fig. 2h), highlighting the consequences of different mechanisms limiting species’ densities across different locations28. The crucial point is that if field surveys were to reveal an equatorward range contraction for this species, this range shift would not be contrary to predictions, as general perception would suggest. Instead, it would be consistent with the predicted direction of change for this biological system’s response to climate change, thus representing positive evidence towards the establishment of a link to climate change.

Bottom Line: Understanding whether those shifts are indeed contrary to climate change predictions involves understanding the most basic mechanisms determining the distribution of species.Temperature metrics have contrasting geographical patterns and latitude or the grand mean are poor predictors for many of them.Our data suggest that unless the appropriate metrics are analysed, the impact of climate change in even a single metric of a single stressor may lead to range shifts in directions that would otherwise be classified as "contrary to prediction".

View Article: PubMed Central - PubMed

Affiliation: 1] CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, 4485-661, Vairão, Portugal [2] Departamento de Biologia, Faculdade de Ciências da Universidade do Porto, R. Campo Alegre, s/n, 4169-007 Porto, Portugal.

ABSTRACT
Predicting the extent and direction of species' range shifts is a major priority for scientists and resource managers. Seminal studies have fostered the notion that biological systems responding to climate change-impacted variables (e.g., temperature, precipitation) should exhibit poleward range shifts but shifts contrary to that expectation have been frequently reported. Understanding whether those shifts are indeed contrary to climate change predictions involves understanding the most basic mechanisms determining the distribution of species. We assessed the patterns of ecologically relevant temperature metrics (e.g., daily range, min, max) along the European Atlantic coast. Temperature metrics have contrasting geographical patterns and latitude or the grand mean are poor predictors for many of them. Our data suggest that unless the appropriate metrics are analysed, the impact of climate change in even a single metric of a single stressor may lead to range shifts in directions that would otherwise be classified as "contrary to prediction".

No MeSH data available.