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Cochran, M.E., and Ellner, S., 1992. Simple Methods for Calculating Age‐Based Life History Parameters for Stage‐Structured Populations: Ecological Archives M062-002. Ecological monographs, 62(3), pp. 345-364. [https://doi.org/10.2307/2937115 doi: 10.2307/2937115]</ref>. One of the appeals to matrix models is that the long term growth rate and the proportion of individuals expected to be in each stage class (known as the stable stage distribution) can be calculated directly using matrix algebraic tools, with the long term population growth rate given by the dominant eigenvalue of the matrix model and stable stage distribution given by the corresponding eigenvector<ref>Leslie, P.H., 1945. On the Use of Matrices in Certain Population Mathematics. Biometrika, 33(3), pp.183-212. [https://doi.org/10.2307/2332297 doi: 10.2307/2332297]</ref>. A key assumption in estimating long term growth rates and stable stage distributions from matrix models is that the transition rates from one life stage to the next represented by the matrix elements are constant through time. Differences in observed proportions of a population in different stages from the predicted stable stage distributions can be used to detect differences in past and present transition ratescaused by changing climatic conditions<ref>Doak, D.F., and Morris, W., 1999. Detecting Population‐Level Consequences of Ongoing Environmental Change Without Long‐Term Monitoring. Ecology, 80(5), pp. 1537-1551.  
 
Cochran, M.E., and Ellner, S., 1992. Simple Methods for Calculating Age‐Based Life History Parameters for Stage‐Structured Populations: Ecological Archives M062-002. Ecological monographs, 62(3), pp. 345-364. [https://doi.org/10.2307/2937115 doi: 10.2307/2937115]</ref>. One of the appeals to matrix models is that the long term growth rate and the proportion of individuals expected to be in each stage class (known as the stable stage distribution) can be calculated directly using matrix algebraic tools, with the long term population growth rate given by the dominant eigenvalue of the matrix model and stable stage distribution given by the corresponding eigenvector<ref>Leslie, P.H., 1945. On the Use of Matrices in Certain Population Mathematics. Biometrika, 33(3), pp.183-212. [https://doi.org/10.2307/2332297 doi: 10.2307/2332297]</ref>. A key assumption in estimating long term growth rates and stable stage distributions from matrix models is that the transition rates from one life stage to the next represented by the matrix elements are constant through time. Differences in observed proportions of a population in different stages from the predicted stable stage distributions can be used to detect differences in past and present transition ratescaused by changing climatic conditions<ref>Doak, D.F., and Morris, W., 1999. Detecting Population‐Level Consequences of Ongoing Environmental Change Without Long‐Term Monitoring. Ecology, 80(5), pp. 1537-1551.  
 
[https://doi.org/10.2307/176545 doi: 10.2307/176545]</ref>.  
 
[https://doi.org/10.2307/176545 doi: 10.2307/176545]</ref>.  
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Given that climate does influence vital rates, predicting how climate change will impact population growth requires evaluating matrix models reflecting changing transition rates. One approach is to take advantage of year to year variation in climate conditions and use models fit to data during different periods— perhaps corresponding to wet and dry years, or to cool and warm years<ref name=":3">
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Gaillard, J.M., Mark Hewison, A.J., Klein, F., Plard, F., Douhard, M., Davison, R., and Bonenfant, C., 2013. How does climate change influence demographic processes of widespread species? Lessons from the comparative analysis of contrasted populations of roe deer. Ecology Letters, 16(1), pp.48-57. [https://doi.org/10.1111/ele.12059 doi: 10.1111/ele.12059] [[Media:Gaillard2013.pdf | Article pdf]]</ref>. The same technique can be used to evaluate changes in the timing of seasonal shifts in climate. For example, Gaillard et al. (2013)<ref name=":3" /> used matrix models to show that the impact of earlier onset of spring weather on roe deer (''Capreolus capreolus'') was almost entirely due to differences in fecundity between periods of earlier and later spring weather conditions. This observation highlights another key assumption about using sensitivity or elasticity values to determine monitoring or management priorities with respect to climate change: that climate-driven changes in different vital rates are of the same, relatively small, magnitude.
  
 
==References==
 
==References==
 
<references />
 
<references />

Revision as of 15:32, 24 March 2022

As the global climate continues to warm, changes in local climate conditions put populations of many species at risk of severe decline and even extinction. Predicting which species are most vulnerable to changing conditions is challenging, because climate interacts different life stages in complex ways. Population models allow natural resource managers to integrate the effects of climate across life stages and provide a powerful tool to inform management decisions. However, care must be taken to match model structure to a species’ biology and recognize the limitations of the data used to parameterize models when interpreting predictions.

Related Article(s):


Contributor(s): Dr. Brian Hudgens


Key Resource(s):

  • Quantitative Conservation Biology[1]
  • Evaluating the Use of Spatially Explicit Population Models to Predict Conservation Reliant Species in Nonanalogue Future Environments on DoD Lands, Strategic Environmental Research and Development Program (SERDP)[2].

Climate Change and No-Analogue Environmental Conditions

The global climate has been changing throughout the past century, with continued changes predicted over coming decades. Generally, temperatures are getting warmer throughout U.S. and worldwide[3]. Precipitation patterns are also changing, with some regions of the U.S.  getting drier, others getting wetter[3][4].  Further changes are occurring in the timing and duration of precipitation events, with extreme weather events becoming more common[3][4].

As a result of these changes, populations, even entire species, are likely to experience novel conditions that impact individual fitness and population viability. In the case of polar bears, extended periods of low sea ice resulting from atmospheric and oceanic warming have led to the species being protected under the Endangered Species Act[5].

The potential for a changing climate to put populations at risk of extinction creates two imperatives for natural resource managers: 1) detecting climate effects on vital rates such as fecundity, growth and survival, and 2) predicting the overall impact of changing climate on managed species. Importantly, changing climate conditions may have different short and long term effects on populations. For example, a population of eastern tiger salamanders (Ambystoma tigrinum) studied at a breeding pond in Fort Bragg, NC, has been observed to suffer consecutive years of no successful offspring when breeding ponds dry out before larvae could undergo metamorphosis[6][7]. This population would initially benefit from increases in precipitation leading to higher fecundity (reproductive success). However, if higher levels of precipitation convert ephemeral ponds to permanent ponds, many resident amphibians would become vulnerable to increased mortality due to predation by fish and bullfrogs (Lithobates catesbeianus)[8][9].

Previous studies have shown that the effect of changing environmental conditions on a species is best understood by monitoring effects on all life stages[10]. This is particularly true for understanding how a changing climate may impact a species. Climate change typically involves simultaneous changes in numerous climate variables (e.g. temperature and precipitation), which may interact with a species at different points of its life-cycle. For example, summer temperatures impact young (small) plant growth of the tundra plant moss campion (Silene acaulis) while snow cover influences growth and survival of larger plants[11].

Even when there is a single, highly dominant climate driver, it is likely to have different effects on different stages of a species life-cycle. For example, drought reduced San Clemente Bell's sparrow fecundity, but not adult survival[12]. Moreover, the variation in the same climate variable can have opposing effects on different life stages. For example, warmer summer temperatures tend to help moss campion plants grow larger, but decrease the number of fruits produced by plants of a given size[11].

Population Models Integrating Effects Across Different Life-Stages

The complex ways in which climate change can impact different species creates a significant challenge to predicting future management needs. Population models can provide a powerful tool for meeting this challenge. Generally, more complex population models capable of integrating climate effects on different life stages are the most useful for predicting species' responses to climate change.  The two types of population models most commonly used for integrating across different life stages are matrix models[13] and individual-based simulations.

Matrix Models

Matrix models are widely used because there are algebraic tools that facilitate evaluating these models and they have a flexible enough structure to accommodate a wide range of life history strategies[14][15]. One of the appeals to matrix models is that the long term growth rate and the proportion of individuals expected to be in each stage class (known as the stable stage distribution) can be calculated directly using matrix algebraic tools, with the long term population growth rate given by the dominant eigenvalue of the matrix model and stable stage distribution given by the corresponding eigenvector[16]. A key assumption in estimating long term growth rates and stable stage distributions from matrix models is that the transition rates from one life stage to the next represented by the matrix elements are constant through time. Differences in observed proportions of a population in different stages from the predicted stable stage distributions can be used to detect differences in past and present transition ratescaused by changing climatic conditions[17].

Given that climate does influence vital rates, predicting how climate change will impact population growth requires evaluating matrix models reflecting changing transition rates. One approach is to take advantage of year to year variation in climate conditions and use models fit to data during different periods— perhaps corresponding to wet and dry years, or to cool and warm years[18]. The same technique can be used to evaluate changes in the timing of seasonal shifts in climate. For example, Gaillard et al. (2013)[18] used matrix models to show that the impact of earlier onset of spring weather on roe deer (Capreolus capreolus) was almost entirely due to differences in fecundity between periods of earlier and later spring weather conditions. This observation highlights another key assumption about using sensitivity or elasticity values to determine monitoring or management priorities with respect to climate change: that climate-driven changes in different vital rates are of the same, relatively small, magnitude.

References

  1. ^ Morris, W.F., and Doak, D.F., 2002. Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis. Sinauer Associates, Inc. Publishers, Sunderland, Massachusetts, USA. ISBN: 978-087893546-8
  2. ^ Hudgens, B., Abbott, J., Haddad, N., Kiekebusch E., Louthan A., Morris W., Stenzel L., Walters J., 2020. Evaluating the Use of Spatially Explicit Population Models to Predict Conservation Reliant Species in Nonanalogue Future Environments on DoD Lands. Strategic Environmental Research and Development Program (SERDP), Project RC-2512 Final Report pdf
  3. ^ 3.0 3.1 3.2 IPCC, 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Report pdf
  4. ^ 4.0 4.1 Abatzoglou, J. T., 2013. Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), pp. 121–131. doi: 10.1002/joc.3413
  5. ^ U.S. Fish and Wildlife (USFWS), 2016. Polar Bear (Ursus maritimus) Conservation Management Plan, Final. U.S. Fish and Wildlife, Region 7, Anchorage, Alaska. 104 pp. Report pdf
  6. ^ Woodward, D., Hudgens, B., and Haddad, N., 2005. Status and ecology of the Northern Pine Snake, Southern Hognose Snake, Tiger Salamander, and Carolina Gopher Frog on Ft. Bragg, NC. Unpublished report to Ft. Bragg Endangered Species Branch.
  7. ^ Haddad, N., Woodward, D., Hudgens, B., Davidai, N., Fields, W., Chesser, M., 2007. Status and ecology of the Northern Pine Snake, Southern Hognose Snake, Tiger Salamander, and Carolina Gopher Frog on Ft. Bragg, NC. Unpublished report to Ft. Bragg Endangered Species Branch.
  8. ^ Fisher, R.N., and Shaffer, H.B., 1996. The Decline of Amphibians in California’s Great Central Valley. Conservation Biology, 10(5), pp. 1387-1397. doi:10.1046/J.1523-1739.1996.10051387.X
  9. ^ Cook, M.T., Heppell, S.S., and Garcia, T.S., 2013. Invasive Bullfrog Larvae Lack Developmental Plasticity to Changing Hydroperiod. The Journal of Wildlife Management, 77(4), pp. 655-662. doi:10.1002/jwmg.509 Article pdf
  10. ^ Brown, L.M., Breed, G.A., Severns, P.M. and Crone, E.E., 2017. Losing a battle but winning the war: moving past preference–performance to understand native herbivore–novel host plant interactions. Oecologia, 183(2), pp. 441-453. doi: 10.1007/s00442-016-3787-y
  11. ^ 11.0 11.1 Doak, D.F., and Morris, W.F., 2010. Demographic compensation and tipping points in climate-induced range shifts. Nature, 467, p.p. 959-962. doi:10.1038/nature09439
  12. ^ Hudgens, B., Beaudry, F., George, T.L., Kaiser, S., and Munkwitz, N.M., 2011. Shifting threats faced by the San Clemente sage sparrow. The Journal of Wildlife Management, 75(6), pp. 1350-1360. doi: 10.1002/jwmg.165 Article pdf
  13. ^ Ehrlén, J., and Morris, W.F., 2015. Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters, 18(3), pp. 303-314. doi: 10.1111/ele.12410 Article pdf
  14. ^ Caswell, H., 2001. Matrix Population Models – Construction, Analysis, and Interpretation. Sinauer Associates, Sunderland, MA, USA, 722 pp. ISBN: 0-87893-096-5
  15. ^ Cochran, M.E., and Ellner, S., 1992. Simple Methods for Calculating Age‐Based Life History Parameters for Stage‐Structured Populations: Ecological Archives M062-002. Ecological monographs, 62(3), pp. 345-364. doi: 10.2307/2937115
  16. ^ Leslie, P.H., 1945. On the Use of Matrices in Certain Population Mathematics. Biometrika, 33(3), pp.183-212. doi: 10.2307/2332297
  17. ^ Doak, D.F., and Morris, W., 1999. Detecting Population‐Level Consequences of Ongoing Environmental Change Without Long‐Term Monitoring. Ecology, 80(5), pp. 1537-1551. doi: 10.2307/176545
  18. ^ 18.0 18.1 Gaillard, J.M., Mark Hewison, A.J., Klein, F., Plard, F., Douhard, M., Davison, R., and Bonenfant, C., 2013. How does climate change influence demographic processes of widespread species? Lessons from the comparative analysis of contrasted populations of roe deer. Ecology Letters, 16(1), pp.48-57. doi: 10.1111/ele.12059 Article pdf
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