DOI: 10.5061/dryad.f1vhhmgv0
title: Tropical mammal functional diversity increases with productivity but decreases with anthropogenic disturbance

description: A variety of factors can affect the biodiversity of tropical mammal
 communities, but their relative importance and directionality remain
 uncertain. Previous global investigations of mammal functional diversity
 have relied on range maps instead of observational data to determine
 community composition. We test the effects of species pools, habitat
 heterogeneity, primary productivity and human disturbance on the
 functional diversity (dispersion and richness) of mammal communities using
 the largest standardized tropical forest camera trap monitoring system,
 the Tropical Ecology Assessment and Monitoring (TEAM) Network. We use
 occupancy values derived from the camera trap data to calculate
 occupancy-weighted functional diversity and use Bayesian generalized
 linear regression to determine the effects of multiple predictors. Mammal
 community functional dispersion increased with primary productivity, while
 functional richness decreased with human-induced local extinctions and was
 significantly lower in Madagascar than other tropical regions. The
 significant positive relationship between functional dispersion and
 productivity was evident only when functional dispersion was weighted by
 species' occupancies. Thus, observational data from standardized
 monitoring can reveal the drivers of mammal communities in ways that are
 not readily apparent from range map-based studies. The positive
 association between occupancy-weighted functional dispersion of tropical
 forest mammal communities and primary productivity suggests that unique
 functional traits may be more beneficial in more productive ecosystems and
 may allow species to persist at higher abundances.
description: <b>Study sites</b> We examined
 mammal communities from 15 moist tropical forest protected areas around
 the world, including sites in Africa, Asia, Madagascar, and the Neotropics
 (Table S1). All sites have been part of the Tropical Ecology Assessment
 and Monitoring (TEAM) Network and followed a standardized annual camera
 trapping protocol to monitor terrestrial (i.e. ground-dwelling) mammals
 (Jansen et al. 2014). <b>Data</b>
 <i>Camera trapping and Occupancy </i>
 Sixty camera
 traps were deployed at each TEAM site during the site’s dry season, which
 was defined as months with &lt;100 mm average rainfall, or the drier
 part of the year at sites with no dry season. The camera traps used to
 monitor the mammal community were deployed at a density of 1 camera per 1
 to 2 km<sup>2</sup>, depending on the size of the
 protected area. Camera trap points were each monitored for at least 30
 consecutive days for multiple years at each site, spanning from 2007 to
 2014. From the TEAM camera traps, we used species-specific
 protected area-level occupancy estimates from Beaudrot et al. (2016) as a
 proxy for abundances in calculating occupancy-weighted functional
 dispersion. Our use of occupancy-weighted functional diversity is
 particularly suitable for identifying underlying community assembly
 processes for two reasons. First, camera traps capture the realized local
 community whereas range maps typically overestimate the geographical
 distribution of species by including potentially non-suitable habitat
 areas (Pimm et al. 2017). Secondly, the occupancy estimates we used
 account for biases that result from imperfect detection and would
 otherwise skew results in favor of species that are more likely to be
 detected on camera traps. Species occupancy estimates were calculated as
 the median of posterior distributions of 1000 values estimated from
 single-species multi-year Bayesian dynamic occupancy models (from Beaudrot
 et al. 2016). For functional dispersion, we used species occupancy values
 from the most recent year with occupancy estimates available, which varied
 among sites from 2012 to 2014. The realized mammal
 community within each TEAM study site consisted of mammal species observed
 by camera traps in the camera trap survey area, which covered about 20,000
 hectares of the typical TEAM protected area (Range: 14,239- 27,445
 hectares; Table S1). Specifically, we included ground-dwelling mammal
 species with an average body mass greater than one kilogram that were
 monitored by the TEAM network camera traps. We set a body mass threshold
 of one kilogram because other field techniques may be more effective for
 monitoring small mammal communities (Glen et al. 2013). Previous studies
 have shown that TEAM camera trap survey areas varied in the size of their
 realized mammal community from 5 to 31 species (Beaudrot et al. 2016,
 Rovero et al. 2020). One hundred twenty-seven species of mammals were
 observed (Table S2) and 310 populations of these species were monitored by
 TEAM camera traps with 77 species found at more than one site.
 <i>Functional traits and diversity</i>
 We collected functional trait data for all study species through
 a search of published literature and databases (Table 1, Table S2, Table
 S3). We chose six functional traits to calculate functional diversity: 1)
 average body mass, 2) diet composition, 3) social or asocial behavior, 4)
 scansorial or entirely terrestrial substrate use, 5) activity period, and
 6) average litter size. These functional traits relate to both the
 response of the species to environmental conditions (response traits) and
 to the role of the species in an ecosystem (effect traits, Weiss &
 Ray 2019). Body mass affects the quality and quantity of resources
 necessary for survival, and also approximates the impact that the species
 may have on the ecosystem in terms of spatial range use, nutrient
 dispersal and trophic regulation. Diet composition characterizes the
 resources a species requires, but also identifies other taxa in the
 ecosystem with which a species potentially interacts. Social group size
 can alter species’ allocation of time to different behaviors including
 foraging, predator avoidance, and care of offspring, and also indicates
 how the species’ impact will be distributed in space. Substrate use
 characterizes where a species can obtain resources and where a species
 will directly interact with an ecosystem. Activity period characterizes
 when a species obtains resources and interacts with an ecosystem. Finally,
 litter size characterizes the life history strategy of a species and
 indicates how the ecological impact of a species can vary temporally based
 on population dynamics. As with any functional trait study, our results
 are in part dependent upon the traits considered and the diversity
 encompassed by functional metrics can change with the inclusion or
 exclusion of specific traits (45). Traits used in
 this study were gathered at the species-level and multiple traits were
 reduced to binary variables, which may reduce the breath of trait
 variation to some extent. Values for average body mass and average litter
 size were continuous and numeric. Diet composition and activity period
 involved a suite of binary categories, of which species could be assigned
 a positive value for more than one category. Values for sociality and
 substrate use were each a single binary category (Table 1). Average body
 mass and average litter size were log-transformed because their
 distributions tend to be log-normal and to prevent inflation of functional
 diversity in sites with species with large body and litter
 sizes. To preserve total inertia and distance between
 the same species occurring in different assemblages (e.g. Villéger, Maire & Leprieur
 2017), we calculated functional dispersion (Laliberté & Legendre
 2010) and functional richness (Mason et al. 2005) for all
 sites and species pools in a single trait space (Fig. 1) using the dbFD
 function from the `FD` R package (Laliberté et al.
 2014). We had no a priori expectations that certain traits
 were more important for functional diversity than others. Similarly,
 previous studies of mammal functional diversity have given equal weight to
 the functional traits included in this study (Oliveira et al. 2016, Safi
 et al. 2011, Penone et al. 2016). We therefore weighted traits with
 multiple categories so that the sum all of categories within a single
 trait was the same as the weight of a trait with a single category.
 Functional dispersion measures the distribution of species in trait space
 or how similar a community is in terms of its functional traits.
 Functional dispersion can either be unweighted, with all species accounted
 for equally, or weighted by species abundances, with distance from the
 community centroid to abundant species in trait space contributing more to
 the metric. To calculate occupancy-weighted functional dispersion, the
 occupancy data were arranged in a species by site matrix and coupled with
 the trait matrix. Functional richness was calculated using a matrix of all
 species and their traits and measured as the volume of the convex hull
 encompassing all species found at a given site in trait space. The
 functional richness metric is incapable of taking into account species
 abundances.  <i>Predictor
 variables</i> We quantified species pool
 functional richness, habitat heterogeneity, primary productivity and two
 measures of anthropogenic disturbance to use as predictors of functional
 richness and occupancy-weighted functional dispersion. The species pool
 included all forest-dwelling species that may inhabit the park based on
 their geographic ranges. We extracted species pools for each protected
 area using global mammal ranges from the IUCN Redlist (IUCN 2019).
 Protected area extents were determined from publicly available shapefiles
 from the United Nations Environment Program World Database on Protected
 Areas (UNEP-WCMC &
 IUCN 2020). For one site that did not have an
 accurate shapefile available on WDPA (Pasoh Forest Reserve), we used a
 two-kilometer buffer of the minimum convex-polygon surrounding the camera
 traps to define this boundary. Any forest-dwelling,
 ground-dwelling mammal species with an average body mass over one kilogram
 that had a range that overlapped with the protected area or was detected
 on camera traps within the park was considered part of the species pool.
 Species pool richness ranged from 5 to 40 species, with a total of 170
 species composing the TEAM site species pools (Table S2). Species pool
 functional richness was calculated using the same function as realized
 community functional richness. We calculated habitat
 heterogeneity as the Shannon diversity of landcover types within a
 two-kilometer buffer of the minimum convex polygon surrounding the camera
 traps at each site. Specifically, landcover classifications were obtained
 from Copernicus satellite data from 2015, and classifications were
 according to Copernicus Global Land Service Dynamic Landcover Map at
 100-meter resolution (CGLS-LC100, Bucchorn et al. 2019). Landcover
 classifications for the habitat heterogeneity calculation included closed
 and open forest categories (evergreen broadleaf, deciduous broadleaf,
 mixed, and indeterminate for each), shrubs, herbaceous vegetation, sparse
 vegetation, and herbaceous wetlands. We removed landcover types associated
 with large bodies of water (permanent water bodies and oceans) as these
 would be unsuitable habitat for ground-dwelling mammals. We also removed
 anthropogenic landcover classifications from the calculation of this
 variable to prevent overlap with the anthropogenic disturbance
 variables. We used published primary productivity and
 human disturbance values for TEAM sites from Rovero et al. 2020.
 Normalized difference vegetation index (NDVI), which measures plant
 reflectance of different wavelengths of light and indicates vegetation
 health, was used as a proxy of productivity and calculated over the camera
 trap array at each forest site. Human density was quantified within the
 Zone of Interaction, which incorporates land cover, elevation, waterways
 and anthropogenic developments to delineate the spatial extent expected to
 impact natural processes inside the protected area (DeFries et al.
 2010). Finally, we included the impact of local species
 extinction on functional richness as an additional measure of
 anthropogenic disturbance and as an offset to control for losses in
 functional richness due to extirpations that have been documented at four
 TEAM sites over recent decades. Specifically, Korup, Cameroon lost the
 leopard, golden cat and giant pangolin; Barro Colorado Island and
 Soberania National Park, Panama lost the white-lipped peccary, giant
 anteater and likely the jaguar; Bwindi Impenetrable Forest, Uganda lost
 the buffalo, leopard, and giant forest hog; Nam Kading, Laos lost both the
 tiger and leopard (38). We note that local extinctions may not necessarily
 impact functional diversity because of the functional redundancy that
 occurs in tropical forest mammal communities (48). We quantified local
 extinction for each TEAM site as the difference in functional richness
 between the functional richness of the current realized community and the
 functional richness of current realized community as well as the recently
 extirpated species.
 <b>Analysis</b> We modeled
 occupancy-weighted functional dispersion and community functional richness
 as a function of environmental and anthropogenic predictor variables using
 Bayesian generalized linear regression. None of the predictor variables
 were highly correlated (r &lt; 0.6) and all continuous variables were
 scaled and centered to produce standardized beta coefficient estimates
 with a mean of zero and standard deviation of one. Given the left-skew in
 the distribution of both functional diversity metrics, we specified a
 Weibull distribution for both of our models. The Weibull distribution is
 an unbounded continuous probability distribution that can accommodate
 skews and long tails in the data.  The global model consisted of all
 potential predictor variables, plus a categorical fixed effect for
 biogeographic region (Neotropics, Africa, Asia, Madagascar) to control for
 region-specific patterns in functional diversity. The Neotropics was the
 category to which all other bioregions were compared. We used the brm
 function from the brms (Bürkner 2017) package in R to fit the models. We
 visually assessed model trace plots and used Rhat criteria for
 convergence. We interpreted the contribution of our predictor variables to
 the functional diversity metrics based on whether the 95% credible
 interval of the parameter’s posterior distribution included
 zero. To assess the importance of occupancy in
 functional dispersion, we also ran regression models of two additional
 unweighted functional dispersion calculations. Unlike occupancy-weighted
 functional dispersion, the additional calculations treated all species
 equally and did not incorporate variation in species abundances. As a
 result, they likely omit important ecological information about the
 relative abundances of functional traits.  We calculated unweighted
 functional dispersion using 1) presence data of the observed community
 from camera trap data, and separately using 2) presence data extracted
 from IUCN range maps.
 <strong>Citations</strong>
 Beaudrot, L.,
 Ahumada, J. A., O’Brien, T., Alvarez-Loayza, P., Boekee, K.,
 Campos-Arceiz, A., … Andelman, S. J. (2016). Standardized Assessment of
 Biodiversity Trends in Tropical Forest Protected Areas: The End Is Not in
 Sight. PLOS Biology, 14(1), e1002357. 
 Buchhorn, M., Smets, B., Bertels, L., Lesiv, M., Tsendbazar, N.,
 Herold, M., Fritz, S. (2019) Copernicus Global Land Service: Land Cover
 100m: epoch 2015: Globe. <i>Dataset of the global component of the
 Copernicus Land Monitoring Service 2019</i>.
 Bürkner, P. C.
 (2017). brms: An R package for Bayesian multilevel models using
 Stan. <i>Journal of statistical
 software</i>, <i>80</i>(1), 1-28. DeFries, R., Rovero, F., Wright, P., Ahumada, J., Andelman, S., Brandon, K., ... & Liu, J. (2010). From plot to landscape scale: linking tropical biodiversity measurements across spatial scales. <i>Frontiers in Ecology and the Environment</i>, <i>8</i>(3), 153-160. Glen, A. S., Cockburn, S., Nichols, M., Ekanayake, J., & Warburton, B. (2013). Optimising camera traps for monitoring small mammals. <i>PloS one</i>, <i>8</i>(6), e67940. Gorczynski, D., & Beaudrot, L. (2020). Functional diversity and redundancy of tropical forest mammals over time. <i>Biotropica</i>. IUCN. (2019).  The IUCN Red List of Threatened Species. Version 2019‐2.  Jansen, P. A., Ahumada, J., Fegraus, E., & O’Brien, T. (2014). TEAM: a standardized camera trap survey to monitor terrestrial vertebrate communities in tropical forests. Camera Trapping: Wildlife Research and Management, 263–270. Laliberté, E., Legendre, P., Shipley, B., & Laliberté, M. E. (2014). Package ‘FD’. <i>Measuring functional diversity from multiple traits, and other tools for functional ecology</i>. Laliberté, E., & Legendre, P. (2010). A distance‐based framework for measuring functional diversity from multiple traits. <i>Ecology</i>, <i>91</i>(1), 299-305. Mason, N. W., Mouillot, D., Lee, W. G., & Wilson, J. B. (2005). Functional richness, functional evenness and functional divergence: the primary components of functional diversity. <i>Oikos</i>, <i>111</i>(1), 112-118. Penone, C., Weinstein, B. G., Graham, C. H., Brooks, T. M., Rondinini, C., Hedges, S. B., ... & Costa, G. C. (2016). Global mammal beta diversity shows parallel assemblage structure in similar but isolated environments. <i>Proceedings of the Royal Society B: Biological Sciences</i>, <i>283</i>(1837), 20161028. Pimm, S. L., Harris, G., Jenkins, C. N., Ocampo‐Peñuela, N., & Li, B. V. (2017). Unfulfilled promise of data‐driven approaches: response to Peterson et al. <i>Conservation Biology</i>, <i>31</i>(4), 944-947. Rovero, F., Ahumada, J., Jansen, P. A., Sheil, D., Alvarez, P., Boekee, K., ... & Tenan, S. (2020). A standardized assessment of forest mammal communities reveals consistent functional composition and vulnerability across the tropics. <i>Ecography</i>, <i>43</i>(1), 75-84. Safi, K., Cianciaruso, M. V., Loyola, R. D., Brito, D., Armour-Marshall, K., & Diniz-Filho, J. A. F. (2011). Understanding global patterns of mammalian functional and phylogenetic diversity. <i>Philosophical Transactions of the Royal Society B: Biological Sciences</i>, <i>366</i>(1577), 2536-2544. UNEP-WCMC and IUCN (2020), Protected Planet: The World Database on Protected Areas (WDPA) [On-line], [insert month/year of the version downloaded], Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net<strong>.</strong> Villéger, S., Maire, E., & Leprieur, F. (2017). On the risks of using dendrograms to measure functional diversity and multidimensional spaces to measure phylogenetic diversity: a comment on Sobral et al.(2016). <i>Ecology Letters</i>, <i>20</i>(4), 554-557. Weiss, K. C., & Ray, C. A. (2019). Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides. <i>Ecography</i>, <i>42</i>(12), 2012-2020.
description: Additional files and descriptions are available on the github
 repository of the project.