Abstract
Background
Different processes determine species’ geographic ranges, including species’ responses to changing climate, habitat, or both simultaneously. Here we ask which combination of factors best predicts shifts in the upper and lower elevation range limits and overall range of small mammal species in Yosemite National Park, California, USA across the last 100 years.
Methods
We used species distribution models (SDMs) to predict elevation range dynamics of small mammals between 1910 and 1930 and 2003 and 2010, based on combinations of habitat and climate variables, and compared the predicted SDM distribution with the “observed” range from occupancy modelling (OM).
Results
SDM model convergence was successful for eight species. Predictions of elevation range shifts from the SDMs agreed with OM for four of these species; while the other four could be partially predicted. SDMs predicted shifts in lower limits (six correct) better than upper limits (five correct). The five correctly predicted upper limit shifts were best predicted with climate; whereas five out of the six lower elevation shifts included habitat. SDMs were best at predicting range contraction at higher elevations.
Conclusions
Climate generally had a stronger effect on range dynamics than habitat, especially at higher elevations. However, at mid-elevations SDMs showed an increasing importance of habitat on range shifts at these elevations, in the cases range shifts were reliably predicted. Predicting elevation range shifts on the basis of climate or habitat alone is insufficient, as habitat and climate play varying roles at different elevations, associated with different processes underlying range shifts. Failure to predict observed range shifts may arise from biotic interactions, behaviour flexibility, or evolutionary adaptation, aspects which are only beginning to be incorporated into distribution modelling frameworks.
Different processes determine species’ geographic ranges, including species’ responses to changing climate, habitat, or both simultaneously. Here we ask which combination of factors best predicts shifts in the upper and lower elevation range limits and overall range of small mammal species in Yosemite National Park, California, USA across the last 100 years.
Methods
We used species distribution models (SDMs) to predict elevation range dynamics of small mammals between 1910 and 1930 and 2003 and 2010, based on combinations of habitat and climate variables, and compared the predicted SDM distribution with the “observed” range from occupancy modelling (OM).
Results
SDM model convergence was successful for eight species. Predictions of elevation range shifts from the SDMs agreed with OM for four of these species; while the other four could be partially predicted. SDMs predicted shifts in lower limits (six correct) better than upper limits (five correct). The five correctly predicted upper limit shifts were best predicted with climate; whereas five out of the six lower elevation shifts included habitat. SDMs were best at predicting range contraction at higher elevations.
Conclusions
Climate generally had a stronger effect on range dynamics than habitat, especially at higher elevations. However, at mid-elevations SDMs showed an increasing importance of habitat on range shifts at these elevations, in the cases range shifts were reliably predicted. Predicting elevation range shifts on the basis of climate or habitat alone is insufficient, as habitat and climate play varying roles at different elevations, associated with different processes underlying range shifts. Failure to predict observed range shifts may arise from biotic interactions, behaviour flexibility, or evolutionary adaptation, aspects which are only beginning to be incorporated into distribution modelling frameworks.
| Original language | English |
|---|---|
| Article number | 7 |
| Number of pages | 1 |
| Journal | Climate Change Responses |
| Volume | 4 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Nov 2017 |
Keywords
- Small mammals
- Occupancy modelling
- Range shifts
- Species distribution models
- Maxent
- Climate
- Habitat