Abstract
BackgroundAccurate 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.MethodologyWe 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.ConclusionsWeather 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.
Highlights
Impacts of climate change on species are frequently predicted by projecting species distribution models (SDM) onto future climate change scenarios
Models based on long-term climate averages overestimate 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 organismappropriate temporal scales, improves understanding of species distributions
Summary
Impacts of climate change on species are frequently predicted by projecting species distribution models (SDM) onto future climate change scenarios. SDM predict the geographic distribution of suitable climatic space for a species by relating species occurrence records to long-term average climate variables. Such models are generally a good representation of a species’ broad range [2], as species are closely connected to climatic conditions through exchanges of energy and mass [3,4]. The temporal scales important to a mobile individual’s location are likely to be much shorter than a 30 year average [13,14]; short-term weather may be more appropriate. The standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. 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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have