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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.


Patterns of temperature metrics across the European Atlantic intertidal ecosystem.(a) Locations surveyed. Geographic pattern of metrics: (b) grand mean, (c) 7 day mean, (d) daily range, (e) microhabitat range, (f) minimum, (g) 5th percentile, (h) mean, (i) 95th percentile, (j) maximum. Black line (b) is grand mean, calculated using all data from each shore. Red and blue lines (c–j) calculated using the warmest and coldest 30 days of each year (7 days for (c)), per shore. The shaded area is the pattern expected if each metric was perfectly correlated with latitude. Points in shaded area are “cooler than expected given latitude”, and points outside shaded area are “hotter than expected”. Correlation coefficients between each metric and latitude are depicted in the top right corner of each panel (blue for cold and red for warm periods). Map created in R35 using Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) coastline data.
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f1: Patterns of temperature metrics across the European Atlantic intertidal ecosystem.(a) Locations surveyed. Geographic pattern of metrics: (b) grand mean, (c) 7 day mean, (d) daily range, (e) microhabitat range, (f) minimum, (g) 5th percentile, (h) mean, (i) 95th percentile, (j) maximum. Black line (b) is grand mean, calculated using all data from each shore. Red and blue lines (c–j) calculated using the warmest and coldest 30 days of each year (7 days for (c)), per shore. The shaded area is the pattern expected if each metric was perfectly correlated with latitude. Points in shaded area are “cooler than expected given latitude”, and points outside shaded area are “hotter than expected”. Correlation coefficients between each metric and latitude are depicted in the top right corner of each panel (blue for cold and red for warm periods). Map created in R35 using Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) coastline data.

Mentions: We used a dataset comprised of 90 individual 4-year-long temperature time series (six microhabitats on 15 shores from 37 °N to 55 °N latitude, Fig. 1a) to evaluate to what extent the patterns of ecologically relevant metrics of a stressor are indeed captured by that stressor’s mean. Extreme temperature is a major stressor in most ecosystems, and especially in the rocky intertidal16, where animals and plants a few centimetres apart can be experiencing dramatic differences in body temperature2122. The overall mean temperature, which is often used as a reference for estimating species distributions17, was shown to match latitude surprisingly well (R = 0.98, Fig. 1b), thus reinforcing the idea of a relatively smooth, continuous gradient from warmer to colder temperatures with increasing latitudes along the European Atlantic coastline. However, with the exception of ‘winter mean’ and ‘winter 95th percentile’ (Fig. 1h,i, blue lines), all other metrics (‘summer mean’, ‘summer 95th percentile’, and winter and summer ‘7 day mean’, ‘daily range’, ‘microhabitat range’, ‘minimum’, ‘5th percentile’ and ‘maximum’) exhibited patterns deviating substantially from that of the grand mean. These differences highlight the key role played by climatic, geomorphologic and oceanographic factors at the local level, and more importantly, show that such factors can change skewness or kurtosis of the distribution of temperatures (or even causing multimodality) without affecting the mean. For example, seasonality, which is strongest within the Bay of Biscay — shores H and I — appears not to drive the grand mean for these shores too far away from the expected value given their latitude, but results in remarkably high summer temperatures (in fact the highest recorded in the study area; Fig. 1i,j, red lines) and equally remarkably low temperatures during winter (again, the lowest within the study area; Fig. 1f,g, blue lines). In another example, upwelling, which is typically stronger around shores F and L during summer23, can be seen driving ‘summer daily range’ and ‘summer microhabitat range’ (Fig. 1d,e, red lines), likely due to the co-occurrence of low water temperatures and high air temperatures. Again, this effect does not result in any important deviation of the grand mean from the latitudinal pattern for these shores, and would be largely missed if data at the appropriate spatial scale had not been collected. Additionally, the combination of regional factors and local context can result in surprising temperature distributions, such as seen at shore N. At this shore, all metrics were found to be lower or equal to the expected value based on latitude. However, the grand mean does not reflect the magnitude of this difference, especially considering that shore N is the coldest in the study area for some of the metrics calculated. The many patterns encapsulated within the distribution of values of a single stressor clearly indicates that the grand mean may largely misrepresent many other ecologically relevant aspects of that stressor. This is in accordance with previous studies2124 and reinforces the notion that a-priori knowledge of the physiological requirements of a species and a detailed characterisation of the thermal extremes at the study area are fundamental to ascertain the real stress landscape imposed on organisms.


Understanding complex biogeographic responses to climate change.

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

Patterns of temperature metrics across the European Atlantic intertidal ecosystem.(a) Locations surveyed. Geographic pattern of metrics: (b) grand mean, (c) 7 day mean, (d) daily range, (e) microhabitat range, (f) minimum, (g) 5th percentile, (h) mean, (i) 95th percentile, (j) maximum. Black line (b) is grand mean, calculated using all data from each shore. Red and blue lines (c–j) calculated using the warmest and coldest 30 days of each year (7 days for (c)), per shore. The shaded area is the pattern expected if each metric was perfectly correlated with latitude. Points in shaded area are “cooler than expected given latitude”, and points outside shaded area are “hotter than expected”. Correlation coefficients between each metric and latitude are depicted in the top right corner of each panel (blue for cold and red for warm periods). Map created in R35 using Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) coastline data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: Patterns of temperature metrics across the European Atlantic intertidal ecosystem.(a) Locations surveyed. Geographic pattern of metrics: (b) grand mean, (c) 7 day mean, (d) daily range, (e) microhabitat range, (f) minimum, (g) 5th percentile, (h) mean, (i) 95th percentile, (j) maximum. Black line (b) is grand mean, calculated using all data from each shore. Red and blue lines (c–j) calculated using the warmest and coldest 30 days of each year (7 days for (c)), per shore. The shaded area is the pattern expected if each metric was perfectly correlated with latitude. Points in shaded area are “cooler than expected given latitude”, and points outside shaded area are “hotter than expected”. Correlation coefficients between each metric and latitude are depicted in the top right corner of each panel (blue for cold and red for warm periods). Map created in R35 using Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) coastline data.
Mentions: We used a dataset comprised of 90 individual 4-year-long temperature time series (six microhabitats on 15 shores from 37 °N to 55 °N latitude, Fig. 1a) to evaluate to what extent the patterns of ecologically relevant metrics of a stressor are indeed captured by that stressor’s mean. Extreme temperature is a major stressor in most ecosystems, and especially in the rocky intertidal16, where animals and plants a few centimetres apart can be experiencing dramatic differences in body temperature2122. The overall mean temperature, which is often used as a reference for estimating species distributions17, was shown to match latitude surprisingly well (R = 0.98, Fig. 1b), thus reinforcing the idea of a relatively smooth, continuous gradient from warmer to colder temperatures with increasing latitudes along the European Atlantic coastline. However, with the exception of ‘winter mean’ and ‘winter 95th percentile’ (Fig. 1h,i, blue lines), all other metrics (‘summer mean’, ‘summer 95th percentile’, and winter and summer ‘7 day mean’, ‘daily range’, ‘microhabitat range’, ‘minimum’, ‘5th percentile’ and ‘maximum’) exhibited patterns deviating substantially from that of the grand mean. These differences highlight the key role played by climatic, geomorphologic and oceanographic factors at the local level, and more importantly, show that such factors can change skewness or kurtosis of the distribution of temperatures (or even causing multimodality) without affecting the mean. For example, seasonality, which is strongest within the Bay of Biscay — shores H and I — appears not to drive the grand mean for these shores too far away from the expected value given their latitude, but results in remarkably high summer temperatures (in fact the highest recorded in the study area; Fig. 1i,j, red lines) and equally remarkably low temperatures during winter (again, the lowest within the study area; Fig. 1f,g, blue lines). In another example, upwelling, which is typically stronger around shores F and L during summer23, can be seen driving ‘summer daily range’ and ‘summer microhabitat range’ (Fig. 1d,e, red lines), likely due to the co-occurrence of low water temperatures and high air temperatures. Again, this effect does not result in any important deviation of the grand mean from the latitudinal pattern for these shores, and would be largely missed if data at the appropriate spatial scale had not been collected. Additionally, the combination of regional factors and local context can result in surprising temperature distributions, such as seen at shore N. At this shore, all metrics were found to be lower or equal to the expected value based on latitude. However, the grand mean does not reflect the magnitude of this difference, especially considering that shore N is the coldest in the study area for some of the metrics calculated. The many patterns encapsulated within the distribution of values of a single stressor clearly indicates that the grand mean may largely misrepresent many other ecologically relevant aspects of that stressor. This is in accordance with previous studies2124 and reinforces the notion that a-priori knowledge of the physiological requirements of a species and a detailed characterisation of the thermal extremes at the study area are fundamental to ascertain the real stress landscape imposed on organisms.

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.