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Data from: Towards smarter harvesting from natural palm populations by sparing the individuals that contribute most to population growth or productivity
doi: 10.5061/dryad.q755t
1. Natural populations deliver a wide range of products that provide income for millions of people and need to be exploited sustainably. Large heterogeneity in individual performance within these exploited populations has the potential to improve population recovery after exploitation and thus help sustaining yields over time. 2. We explored the potential of using individual heterogeneity to design smarter harvest schemes, by sparing individuals that contribute most to future productivity and population growth, using the understorey palm Chamaedorea elegans as a model system. Leaves of this palm are an important non-timber forest product and long-term inter-individual growth variability can be evaluated from internode lengths. 3. We studied a population of 830 individuals, half of which was subjected to a 67 % defoliation treatment for three years. We measured effects of defoliation on vital rates and leaf size – a trait that determines marketability. We constructed integral projection models in which vital rates depended on stem length, past growth rate, and defoliation, and evaluated transient population dynamics to quantify population development and leaf yield. We then simulated scenarios in which we spared individuals that were either most important for population growth or had leaves smaller than marketable size. 4. Individuals varying in size or past growth rate responded similarly to leaf harvesting in terms of growth and reproduction. By contrast, defoliation-induced reduction in survival chance was smaller in large individuals than in small ones. Simulations showed that harvest-induced population decline was much reduced when individuals from size and past growth classes that contributed most to population growth were spared. Under this scenario cumulative leaf harvest over 20 years was somewhat reduced, but long-term leaf production was sustained. A three-fold increase in leaf yield was generated when individuals with small leaves are spared. 5. Synthesis and applications This study demonstrates the potential to create smarter systems of palm leaf harvest by accounting for individual heterogeneity within exploited populations. Sparing individuals that contribute most to population growth ensured sustained leaf production over time. The concepts and methods presented here are generally applicable to exploited plant and animal species which exhibit considerable individual heterogeneity.
Vital rate and internode dataThis data file contains annual vital rate data (stem length growth, fruit production, survival and leaf production) of 830 individuals of the understorey palm Chamaedorea elegans, collected in a 0.7 ha plot in Chiapas, Mexico, during the period November 2012 - November 2015. A 2/3 defoliation treatment was repeatedly applied to half of the individuals. The data file also contains measurements of the lengths of all internodes of all individuals.
- National Autonomous University of Mexico Mexico
- Wageningen University & Research Netherlands
leaf harvesting, harvest simulations, Chamaedorea, forest management, Life Sciences, individual heterogeneity, NFTP, PE&RC, sustainability, Life sciences, Forest Ecology and Forest Management, Integral Project Model, NTFP, medicine and health care, Chamaedorea elegans, Life sciences, medicine and health care , Centre for Crop Systems Analysis, Medicine, Bosecologie en Bosbeheer, Crop and Weed Ecology, individual heterogenity, integral projection model
leaf harvesting, harvest simulations, Chamaedorea, forest management, Life Sciences, individual heterogeneity, NFTP, PE&RC, sustainability, Life sciences, Forest Ecology and Forest Management, Integral Project Model, NTFP, medicine and health care, Chamaedorea elegans, Life sciences, medicine and health care , Centre for Crop Systems Analysis, Medicine, Bosecologie en Bosbeheer, Crop and Weed Ecology, individual heterogenity, integral projection model
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