Effects of social structure and prey dynamics on extinction risk in gray wolves
Extinction models based on diffusion theory generally fail to incorporate two important aspects of population biology-social structure and prey dynamics. We include these aspects in an individual-based extinction model for small, isolated populations of the gray wolf (Canis lupus). Our model predict...
| Published in: | Conservation Biology |
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| Main Authors: | , , |
| Format: | Text |
| Language: | unknown |
| Published: |
Digital Commons @ Michigan Tech
1997
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| Subjects: | |
| Online Access: | https://digitalcommons.mtu.edu/michigantech-p/8598 https://doi.org/10.1046/j.1523-1739.1997.95366.x |
| Summary: | Extinction models based on diffusion theory generally fail to incorporate two important aspects of population biology-social structure and prey dynamics. We include these aspects in an individual-based extinction model for small, isolated populations of the gray wolf (Canis lupus). Our model predicts mean times to extinction significantly longer than those predicted by more general (diffusion) models. According to our model, an isolated population of 50 wolves has a 95% chance of surviving just 9 years and only a 30% chance of surviving beyond 100 years. Reflecting the influence of social structure, a wolf population initially comprising 50 individuals is expected to persist only a few years longer, on average (71 years), than is a population initially comprising just a single reproductive pair (62 years). In contrast substantially greater average prey abundance leads to dramatically longer expected persistence times. Autocorrelated prey dynamics result in a more complex distribution of extinction times than predicted by many extinction models. We contend that demographic stochasticity may pose the greatest threat to small, isolated wolf populations, although environmental stochasticity and genetic effects may compound this threat. Our work highlights the importance of considering social structure and resource dynamics in the development of population viability analyses. |
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