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Weather, not climate, defines distributions of vagile bird species.

Reside AE, Vanderwal JJ, Kutt AS, Perkins GC - PLoS ONE (2010)

Bottom Line: We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species.These weather models were compared against models built using long-term (30 year) averages of the same climatic variables.Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions.

View Article: PubMed Central - PubMed

Affiliation: Climate Adaptation Flagship and Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation, Townsville, Queensland, Australia. april.reside@gmail.com

ABSTRACT

Background: Accurate predictions of species distributions are essential for climate change impact assessments. However the standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. The benefit of using temporally explicit weather and distribution data has not been assessed. We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species.

Methodology: We tested our hypothesis by generating distribution models for 157 bird species found in Australian tropical savannas (ATS) using modelling algorithm Maxent. The variable weather of the ATS supports a bird assemblage with variable movement patterns and a high incidence of nomadism. We developed "weather" models by relating climatic variables (mean temperature, rainfall, rainfall seasonality and temperature seasonality) from the three month, six month and one year period preceding each bird record over a 58 year period (1950-2008). These weather models were compared against models built using long-term (30 year) averages of the same climatic variables.

Conclusions: Weather models consistently achieved higher model scores than climate models, particularly for wide-ranging, nomadic and desert species. Climate models predicted larger range areas for species, whereas weather models quantified fluctuations in habitat suitability across months, seasons and years. Models based on long-term climate averages over-estimate availability of suitable habitat and species' climatic tolerances, masking species potential vulnerability to climate change. Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions.

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The contribution of different variables to the weather and climate models.mean temperature, temperature seasonality, precipitation and precipitation seasonality(mean ±25th and 75th percentiles). Bars representing weather are shown in grey, while bars representing climate are white.
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pone-0013569-g004: The contribution of different variables to the weather and climate models.mean temperature, temperature seasonality, precipitation and precipitation seasonality(mean ±25th and 75th percentiles). Bars representing weather are shown in grey, while bars representing climate are white.

Mentions: Altering the temporal scale of the model variables from 30 year to six month and one year periods changes the relative contributions of the variables. Precipitation contributed significantly more to climate models (Wilcoxon matched pairs test, p = 0.003), and precipitation seasonality contributed significantly more to weather models (p<0.001) (Figure 4). Temperature was on average the most influential variable across for both climate and weather models, followed by temperature seasonality, precipitation and then precipitation seasonality. We examined the differences in variable contribution to models depending on a species' life history, and how this changed with temporal scale. All variables contributed differently for species across biogeographic affiliations to the p≤0.01 level (Kruskal-Wallis ANOVA; Figure S1). For the other life history characteristics, results were varied. The contribution of temperature differed significantly according to range size (climate models: p = 0.061; weather models: p = 0.019), and the contribution of precipitation differed according to movement (climate models: p = 0.01; weather models: p = 0.024).


Weather, not climate, defines distributions of vagile bird species.

Reside AE, Vanderwal JJ, Kutt AS, Perkins GC - PLoS ONE (2010)

The contribution of different variables to the weather and climate models.mean temperature, temperature seasonality, precipitation and precipitation seasonality(mean ±25th and 75th percentiles). Bars representing weather are shown in grey, while bars representing climate are white.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2962630&req=5

pone-0013569-g004: The contribution of different variables to the weather and climate models.mean temperature, temperature seasonality, precipitation and precipitation seasonality(mean ±25th and 75th percentiles). Bars representing weather are shown in grey, while bars representing climate are white.
Mentions: Altering the temporal scale of the model variables from 30 year to six month and one year periods changes the relative contributions of the variables. Precipitation contributed significantly more to climate models (Wilcoxon matched pairs test, p = 0.003), and precipitation seasonality contributed significantly more to weather models (p<0.001) (Figure 4). Temperature was on average the most influential variable across for both climate and weather models, followed by temperature seasonality, precipitation and then precipitation seasonality. We examined the differences in variable contribution to models depending on a species' life history, and how this changed with temporal scale. All variables contributed differently for species across biogeographic affiliations to the p≤0.01 level (Kruskal-Wallis ANOVA; Figure S1). For the other life history characteristics, results were varied. The contribution of temperature differed significantly according to range size (climate models: p = 0.061; weather models: p = 0.019), and the contribution of precipitation differed according to movement (climate models: p = 0.01; weather models: p = 0.024).

Bottom Line: We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species.These weather models were compared against models built using long-term (30 year) averages of the same climatic variables.Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions.

View Article: PubMed Central - PubMed

Affiliation: Climate Adaptation Flagship and Ecosystem Sciences, Commonwealth Scientific and Industrial Research Organisation, Townsville, Queensland, Australia. april.reside@gmail.com

ABSTRACT

Background: Accurate predictions of species distributions are essential for climate change impact assessments. However the standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. The benefit of using temporally explicit weather and distribution data has not been assessed. We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species.

Methodology: We tested our hypothesis by generating distribution models for 157 bird species found in Australian tropical savannas (ATS) using modelling algorithm Maxent. The variable weather of the ATS supports a bird assemblage with variable movement patterns and a high incidence of nomadism. We developed "weather" models by relating climatic variables (mean temperature, rainfall, rainfall seasonality and temperature seasonality) from the three month, six month and one year period preceding each bird record over a 58 year period (1950-2008). These weather models were compared against models built using long-term (30 year) averages of the same climatic variables.

Conclusions: Weather models consistently achieved higher model scores than climate models, particularly for wide-ranging, nomadic and desert species. Climate models predicted larger range areas for species, whereas weather models quantified fluctuations in habitat suitability across months, seasons and years. Models based on long-term climate averages over-estimate availability of suitable habitat and species' climatic tolerances, masking species potential vulnerability to climate change. Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions.

Show MeSH
Related in: MedlinePlus