We integrate information from veterinary science to discover interesting facts where mathematics, computer science, biology, and resource management converge.
Life history of moose (Alces alces)
We used the life cycles of cow and bull moose in conjunction with a projection matrix to identify the population dynamics in the Adirondacks of New York state between 2015-2019. We assumed that the life history of moose was by sex, then further subdivided into segments of individuals that share common transition probabilities during the spring and the rut. The resulting 3-stage matrices modeled calves, young adults and mature adults across the spring and rut of each calendar year. The semi-annual spring and rut matrices for each sex were then folded into a single annual matrix.
a. Average annual fertility of young cows
b. Average annual fertility of mature cows
f. Average semi-annual survival of young cows
g. Average semi-annual survival of mature cows
h. Average semi-annual survival of cow calves
b. Average annual fertility of mature bulls
d. Average spring survival of young bulls (without transition into a breeder)
f. Average spring survival of young bulls (with transition into a breeder)
g. Average spring survival of breeding bulls
h. Average rut survival of bull calves
i. Average rut survival of young bulls (without transition into a breeder)
m. Average rut survival of breeding bulls
Population dynamics of moose in New York
We integrated time series data into each annual matrix, then initiated a combinatorial optimization algorithm to estimate the survival and fertility rates of the moose population in Adirondack Park. The algorithm-predicted matrices were then used to calculate 17 asymptotic and transient demographic properties, which may be accessed by clicking the buttons below.
Explore the preliminary 2015-2019 demographic results in NY1:
Sensitivity analysis of the life cycle
Population matrix models are used to assess population growth and extinction risk, while sensitivity analyses are conducted to identify management actions that propel population responses (Morris & Doak 2002; Saltelli et al. 2004). The button below allow you to explore how alterations to vital rates will influence the population growth rate. For further information please read Hanley, Connelly, & Dennis (2019).
Conduct your own sensitivity analysis using the peer-reviewed software2:
The population scale impact of lethal parasites on moose
Necropsies of moose in New York have revealed the presence of two lethal parasites: brain worm (P. tenuis) and liver flukes (Fasciola sp.), lending questions regarding the population scale impact of associated mortalities. We compared the population dynamics of moose in the Adirondacks to those dynamics that would have ensued had mortalities from liver fluke or brain worm not occurred.
Compare the preliminary results for comparisons among the current and parasite-free dynamics in NY3:
Further details of this research
Questions can be directed to Drs. Krysten Schuler (email@example.com) or Jacqui Frair (firstname.lastname@example.org).
Funding provided by the Federal Aid in Wildlife Restoration Act and the New York State Department of Environmental Conservation. The contents of this web site, the links, the interactive apps, cited literature, and the narratives have not been reviewed nor endorsed by the NYSDEC, and the views expressed in this content do not necessarily reflect the views of the NYSDEC, its officers, directors, affiliates or agents.
This interdisciplinary research contains data contributions from the New York State Department of Environmental Conservation and academic contributions from our collaborators at the Cornell Wildlife Health Lab at Cornell University and at the State University of New York Environmental Science and Forestry. Contributions may have provided intellectual property, knowledge, data, software code, time, suggestions, comments, and extension and communication products to one or more research products. Contributors, we thank you.
Additional thanks to:
Niki Keith, who aided the project while at the Cornell Wildlife Health Lab, but has since moved on to Hamilton College of New York
Connelly, P, Hanley, B, Friar, J, Hurst, J, Stickles, J, Kramer, D, & Schuler, K. 201X. The effect of disease on Moose in New York, USA from 2015-2019. In preparation.
Hanley, B, Dennis, B, Kramer, D, & Schuler, K. Estimating parameters from adult time series data for population matrix models. In peer revision.
Hanley B, Connelly P, & Dennis B. 2019. Another look at the eigenvalues of a population matrix model. PeerJ 7:e8018. doi: https://doi.org/10.7717/peerj.8018
1 Connelly, P., Hanley, B., Frair, J., & Schuler, K. 2020. MoosePOPd Web Interactive: Software to investigate the demography of Moose in New York, USA from 2015-2019 [Software]. Cornell University Library eCommons Repository. doi: https://doi.org/10.7298/k033-va79
2 Hanley, B, Connelly, P, & Dennis, B. 2019. IsoPOPd: Interactive software to understand how elements in a population matrix model influence the asymptotic population growth rate [Software]. Cornell University Library eCommons Repository. doi: https://doi.org/10.7298/bcmg-7w08
3 Connelly, P., Hanley, B., Frair, J., & Schuler, K. 2020. MooseCounterPOPd Web Interactive: Software to investigate the population scale impact of Brain Worm and Liver Fluke in Moose in New York, USA from 2015-2019 [Software]. Cornell University Library eCommons Repository. doi: https://doi.org/10.7298/kxme-kq04
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