DOI: 10.5061/dryad.69p8cz91x
title: Phylogeny explains why less therapeutically redundant plant species are not necessarily facing greater use pressure

description: Understanding which factors influence medicinal plant species selection
 and harvest or use pressure can provide valuable insights for sustainable
 management of natural resources and conservation efforts. The utilitarian
 redundancy model, a theoretical framework in ethnobotany, suggests that
 species that are therapeutically redundant or fulfill similar therapeutic
 functions within traditional ethnomedicine are less likely to be under
 greater use pressure. However, species’ evolutionary relatedness and the
 preference of certain species over others to treat a given illness can
 directly affect how use pressure is diffused across several groups of
 species. These factors may alter the strength of the therapeutic
 redundancy-use pressure relationship. Medicinal plant species that fulfill
 the same therapeutic functions may experience greater use pressure despite
 their level of therapeutic redundancy because they are preferred—where
 most people select these species preferably over other species that are
 equally available for a given treatment. Further, species that are closely
 related evolutionarily may be more likely to be harvested not because they
 are therapeutically unique but because they share evolutionary traits such
 as secondary chemistry with other medicinally important species which may
 make them more prone to being harvested. We investigate the effects of
 species therapeutic redundancy, use value, preference, and evolutionary
 relatedness on species use pressure in the Shipibo-Konibo community of
 Paoyhan in the Peruvian Amazon region. We used phylogenetic generalized
 least squares models to identify significant predictors of species use
 pressure for 62 medicinal plant species cited by 30 participants and
 fulfilling 31 therapeutic functions in Shipibo-Konibo ethnomedicine. Our
 model controlling for species’ shared evolutionary history indicated that
 therapeutically redundant medicinal plants experienced greater levels of
 use pressure. However, as preference increased, the effect of
 therapeutic redundancy on species use-pressure became less positive.
 Contrary to predictions, species preference by local people alone did not
 predict use pressure. Further, when we control for species’ shared
 evolutionary history, the effect of preference on species use pressure was
 dependent on therapeutic redundancy. Our study illustrates the importance
 of controlling for evolutionary relatedness between species in studying
 plant-human interactions and the complexity involved in employing the
 utilitarian redundancy model to inform management and conservation
 efforts.
description: We used semi-structured interviews and free listing, an
 elucidation technique commonly used in the social sciences that seeks to
 identify specific information on a given cultural domain of the
 investigated community (Albuquerque et al., 2014).  As such, each
 participant was asked to list the medicinal plants they know and their
 uses for treating illnesses in ethnomedicinal contexts according to the
 participants’ emic perspective.  Semi-structured interviews were used
 because they allow for greater flexibility during the interview process
 compared to structured interviews and they allow for guided or specific
 data collection on an given cultural domain compared to unstructured
 interviews.  Further, this approach allowed for more precise data
 collection as we aimed to get an in-depth understanding of medicinal plant
 use by knowledgeable participants without having to interview the same
 participant more than once, which, is not always possible (Albuquerque et
 al., 2014; Bernard, 2017).  These approaches were coupled with focus group
 discussions supplemented by participant observations and <i>walk in
 the woods </i>(Albuquerque et al., 2014) to collect data that were
 used to estimate species therapeutic redundancy, use values and preference
 and to test their effects on species use pressure of medicinal plants used
 by the Shipibo-Konibo for healing.  The use of such <i>triangulation
 of methods</i> recommended in ethnobiology seeks to ensure the
 reliability of data collected where one method is used to verify or
 cross-reference the responses obtained from another elicitation
 technique<i> </i>(Albuquerque et al.,
 2014)<i>.</i>
description: To test the effects of species use preference, redundancy and use values, on species use pressure, we used general linear models (GLM) and phylogenetic generalized least squares (PGLS) in<i> R</i> 3.4.3 (R Development Core Team, 2019).  Because our response variable, use pressure, was estimated as a measurement (biomass) we used a gaussian error structure for both models (Crawley, 2013). Prior to running our models, we tested for a correlation between predictor variables and excluded any that were significant (McGarigal et al.,  2013).  The PGLS model in addition to testing the effects of preference, redundancy and use values, also controlled for phylogenetic relatedness between medicinal plant species. We compared outcomes of both models to understand role of species shared evolutionary history (Heinrich & Verpoorte, 2012) on the prediction of species use pressure.  Controlling for species evolutionary relatedness in our models allows us to account for non-independence between observations due to phylogenetic history between species (Mundry, 2014). This allowed us to provide a direct test of our predictor variables on medicinal plant species use pressure.   To develop the PGLS model, we built a phylogeny of medicinal species cited by participants using the <i>S. PhyloMaker</i>function in R (Qian & Jin, 2016; Jin & Qian, 2019).  Our phylogenetic tree was built by pruning a mega-tree comprised of molecular data from GenBank, phylogenetic data from the Open Tree of Life and fossil records.  It includes all plant families, approximately 10, 000 genera and 70,000 species of vascular plants in the world.  As such, our phylogenetic tree generated by the <i>S. PhyloMake</i>r function consists of plant species cited by participants where all plant families and the majority of genera were resolved (Jin & Qian, 2019).  For both GLM and PGLS models, we started with a saturated model that included all the three predictors (preference, redundancy and use value), and developed subsequent nested models’ candidates by sequentially removing one of the predictors.   To select the best fitting models,  we estimated the ∆ AIC (Akaike information criterion) for each model (Crawley, 2013) as the difference in the AIC between each model and the model with the lowest AIC.  We then selected models with ∆ AIC&lt; 2.