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Non‐timber forest product harvest in variable environments: modeling the effect of harvesting as a stochastic sequence
Author(s) -
Gaoue Orou G.,
Horvitz Carol C.,
Ticktin Tamara
Publication year - 2011
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/10-0422.1
Subject(s) - markov chain , vital rates , ecology , population , khaya , environmental science , biology , population growth , mathematics , statistics , botany , demography , sociology
With increasing reports of overexploitation of wild plants for timber and non‐timber forest products, there has been an increase in the number of studies investigating the effect of harvest on the dynamics of harvested populations. However, most studies have failed to account for temporal and spatial variability in the ecological conditions in which these species occur, as well as variability in the patterns of harvest intensity. In reality, local harvesters harvest at variable rather than fixed intensity over time. Here we used Markov chains to investigate how different patterns of harvesting intensity (summarized as return time to high harvest) affected the stochastic population growth rate (λ s ) and its elasticity to perturbation of means and variances of vital rates. We studied the effect of bark and foliage harvest from African mahogany Khaya senegalensis in two contrasting ecological regions in Benin. Khaya populations declined regardless of time between harvests of high intensity. Moreover, λ s increased with decreasing harvesting pressure in the dry region but, surprisingly, declined in the moist region toward λ s = 0.956. The stochastic elasticity was dominated by the stasis of juveniles and adults. The declining growth rate with decreasing harvest pressure in the moist region was mainly driven by the declining mean survival rates of juveniles and adults. Our results suggest that modeling the temporal variability of harvest intensity as a Markov chain better mimics local practices and provides insights that are missed when temporal variability in harvest intensity is modeled as independent over time and drawn from a fixed distribution.