Abstract
Anthropogenic climate change is already impacting ecological systems. Understanding how organisms respond to weather (short-term) and climate (long-term) variability, and the population and ecosystem-wide consequences of climate change, is a research priority. The appropriate use of information on past and potential future weather and climate conditions is crucial for such research, but uncertainties and biases in this information are seldom given full consideration, with important consequences for assessing the potential impacts of climate change on the conservation of biodiversity. Here, we highlight three important neglected issues pertaining to the major applications of weather and climate information in ecological and biogeographical studies. These are as follows: (1) the uncertainty associated with historical weather and climate information; (2) the selection of ensembles of simulated future climate conditions derived from general circulation models (GCM); and (3) the uncertainty and assumptions associated with downscaling GCM simulations to ecologically relevant spatial scales. Broadly, in order to improve the use of weather and climate information in ecological studies, we propose that ecologists must: (1) use weather and climate products that are appropriate for their intended purpose; (2) explore the consequences of uncertainty in these products for ecological conclusions; and (3) seek greater integration of ecological and climate research to create products that reflect both the requirements of ecologists and the limits of climatology. This will enable more effective research into the likely responses of ecological systems to future climate change.
Original language | English |
---|---|
Pages (from-to) | 329-340 |
Number of pages | 12 |
Journal | Diversity and Distributions |
Volume | 23 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Mar 2017 |
Keywords
- biodiversity conservation
- climate uncertainty
- conservation biogeography
- downscaling
- general circulation models
- species distribution model