10.5061/DRYAD.CVDNCJT23
Gorczynski, Daniel
0000-0003-0395-0434
Rice University
Beaudrot., L.
Rice University
Functional diversity and redundancy of tropical forest mammals over time
Dryad
dataset
2020
protected area
Costa Rica.
2020-08-20T00:00:00Z
2020-08-20T00:00:00Z
en
17791 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Globally, tropical rain forests comprise some of the most diverse and
functionally rich ecosystems but are increasingly degraded by human
impacts. Protected areas have been shown to conserve species diversity,
but their effectiveness at maintaining functional diversity over time is
less well known, despite the fact that functional diversity likely reveals
more ecological information than taxonomic diversity. By extension, the
degree to which species loss decreases functional diversity within
protected areas is also unknown; functional redundancy may buffer
communities from loss of functional diversity from some local extinctions.
Using eight years of camera trap data, we quantified annual functional
dispersion of the large mammal community in the Volcán Barva region of
Costa Rica and tested for changes in functional dispersion over time in
response to environmental and anthropogenic predictors. We quantified
functional redundancy based on modeled declines in functional dispersion
with species loss. Functional dispersion did not change significantly over
time and was not associated with measured environmental or anthropogenic
predictors. Quantitative modeling of change in functional traits over time
did not identify significant changes. We did however find qualitative
trends in relative trait proportions, which could be indicative of
functional change in the future. We found high functional redundancy, with
average functional dispersion declining significantly only after 9 out of
21 large mammal species were lost from the community. We cautiously
suggest that protected tropical rain forests can conserve functional
diversity over the course of a decade even in heavily fragmented
landscapes.
METHODS 1. Study taxa Large mammals have a disproportionate
impact on their ecosystem becuase of their large body size, dietary
requirements and home ranges (Bakker et al. 2016). For these reasons,
large mammals also have the potential to respond strongly to environmental
change based on their functional traits (Chiarello 1999, Newbold et al.
2014). For millenia, large-bodied mammals have consistently exhibited the
highest extinction rates (Dirzo et al. 2014), particularly within
fragmented tropical landscapes (Crooks 2002; Jorge et al. 2013). Tropical
mammal communities may therefore be particularly vulnerable to functional
diversity loss in a changing environment. We analyzed a
terrestrial large mammal community, which had 21 species (Beaudrot et al.
2016a). We included all predominately ground-dwelling mammal species over
100 grams in average body mass. There were several additional co-occuring
mammal species but they either fell outside this definition or were
excluded due to very low detection (Table S3). Functions performed by
large mammal species were considered functionally distinct in this study,
but may also be provided by other taxa (e.g. arboreal mammals, birds,
etc.). 2. Study site Volcán Barva in Costa Rica consists of tropical
rain forest that encompasses La Selva Biological Research station and
Braulio Carrillo National Park; La Selva has a long, rich history of
ecological research (Pringle et al. 1984). With camera trap points ranging
in elevation from 49 to 2569 meters above sea level (Ahumada et al. 2013),
and an elevational coefficient of variation of 1.01, the survey area
encompasses considerable habitat heterogeneity. The terrestrial mammal
community at this site has been monitored annually with camera traps since
2007 as part of the Tropical Ecology Assessment and Monitoring (TEAM)
Network. TEAM was established to monitor ground-dwelling mammal and bird
communities in protected tropical forests worldwide using a standardized
camera trapping protocol (Jansen et al. 2014). We selected Volcán Barva
because it is the longest running TEAM site. There is no evidence of large
mammal species loss at Volcán Barva since 2007 (Beaudrot et al. 2016b),
but populations of some mammal species have declined (Ahumada, Hurtado
& Lizcano 2013). Human disturbance is also prevalent
in and around Volcán Barva. Poachers are a threat to mammal species in the
area, and deforestation occurs along the border of Braulio Carrillo
National Park (Rovero & Zimmerman 2016; Schelhas &
Sánchez-Azofeifa 2006). Over 50% of the park’s border is classified as
fragmented landscape (Ahumada et al. 2011). Nevertheless, little forest
cover change has been observed within the protected area in recent years
(Beaudrot et al. 2019), which suggests that human disturbance from illegal
logging has been minimal. 3. Data collection 1. Camera trap data and
occupancy estimates We used camera-trap data collected by TEAM between
2007 and 2014. TEAM surveys terrestrial (i.e., ground-dwelling) tropical
mammal populations on an annual basis, using a standardized protocol with
large-scale arrays of permanent camera-trap points (Jansen et al. 2014).
Sixty camera traps were deployed at a density of 1 camera per 1 - 2 km2,
encompassing a survey area of 21,049 hectares out of the 49,317 hectares
within the boundaries of the protected area. Each camera trap was
activated for 30 consecutive days annually at the same time every year
during the dry season. This was done to account for seasonality across
TEAM sites and across years. While mammal community compositions can shift
in response to seasonal differences such as plant productivity and fruit
availability (Ramírez-Bautista & Williams 2019, Wen et al. 2014,
Marshall et al. 2014), the consistent temporal camera trap deployment each
year likely reduced any potential bias. Camera trap images were identified
by TEAM personnel following the standard IUCN Red List (IUCN 2014).
Species-specific annual occupancy values for the Volcán Barva community
were obtained from a previously published study (Beaudrot et al. 2016b),
which used single species Bayesian dynamic occupancy modeling that
provided a posterior distribution of 1000 occupancy values for each
species for each year based on the TEAM camera trap data. We selected the
median species-specific posterior occupancy value for each species for use
in this study. 2. Functional trait data We obtained mammal functional
trait data through an extensive literature search in which all monitored
terrestrial mammal species were assigned ranked values for six functional
traits: average body mass, diet, average social group size, habitat type,
activity period, and average litter size (Table 1; Table S1; Table S2). We
selected these functional traits for their association with aspects of
individual species ecology relevant to how species utilize their
environment and impact their ecosystem (Weiss & Ray 2019). For
example, body mass affects the quality of resources needed for survival
(Jarman 1974) and approximates the degree of impact that species will have
on its ecosystem in terms of quantity of nutrients dispersed, quantity of
food consumed and spatial range of impact (Wolf, Doughty & Malhi
2013). The selected traits are used extensively in studies of mammal
functional traits (Flynn et al. 2009, Hempson, Archibald & Bond
2015, Jones et al. 2009). Nevertheless, functional traits studies are
highly dependent on the selection of relevant functional traits (Petchey
& Gaston 2006), and although we cover a wide suite of ecological
attributes, it is possible that important traits have been inadvertently
omitted. In addition, although these ordered traits cover a breadth of
functional aspects, their categorical nature may obfuscate patterns that
would be revealed by continuous trait measurements (Kohli & Rowe
2019). 3. Environmental data We collected data on climatic, biological
and anthropogenic factors that could impact mammal functional diversity
(Table S4). We selected four variables that were not strongly correlated
with other considered variables (r < 0.6) as potential predictors
of mammal functional diversity. The four predictors were 1) annual
precipitation, 2) area of new canopy gaps within Volcán Barva each year,
3) mean area of forest fragments within the Zone of Interaction (Defries
et al. 2010a) each year and 4) area of forest loss within the Zone of
Interaction each year. The Zone of Interaction is the spatial extent
believed to most likely affect biodiversity within the sampling area and
is systematically quantified based on human settlements, watersheds and
migration corridors (Defries et al. 2010a; Beaudrot et al. 2016b). We
selected the above variables for the following reasons. Annual
precipitation can affect plant and animal functional traits because water
is required by all organisms for metabolism and is a limiting resource for
many (Wright et al. 1999; Dwyer & Laughlin 2017). Canopy gaps are
critical components of vegetation dynamics and represent important aspects
of plant community diversity (Denslow 1987). For example, approximately
75% of the tree species in La Selva are dependent on canopy gaps for seed
germination and growth (Hartshorn 1978). Vegetation dynamics, in turn,
have the potential to strongly affect mammal community functional traits
by altering resource availability and habitat structure (Laurance 1991;
Laurance et al. 2008). Edge effects and isolation from fragmentation have
been shown to affect community composition in other systems (Newmark 1987;
Malcolm 1994; Krishnadas et al. 2018). Deforestation has also been shown
to be one of the primary drivers of defaunation in the tropics (Canale et
al. 2012) and has the potential to disturb community structure. Mean
annual precipitation data were collected from the NASA POWER project at a
0.5°x0.5° resolution (POWER data access viewer). We used remotely sensed
vegetation classification data (Hansen et al. 2013) to calculate canopy
gaps, mean fragment size, and forest loss over the study period (package
‘SDMTools’ in R; VanDerWal et al. 2014). 4. Analysis 1. Overview
First, we calculated annual functional diversity for the mammal community
in the Volcán Barva region of Costa Rica for an eight-year period and
assessed these values for linear trends over time. We then used linear
regression to examine the relationship between temporal change in
functional diversity and our four environmental predictor variables. We
tested for temporal trends in occupancy-weighted trait values to examine
quantitatively how individual functional traits changed over time within
the Volcán Barva large mammal community. Lastly, we used bootstrapping to
quantify functional redundancy and segmented linear regression to analyze
how functional diversity changed with simulated removal of species from
the community. 2. Functional traits We selected functional traits for
assessment and ranking based on established methods for examining effects
of anthropogenic change on mammalian functional traits that reflect
species responses to environmental conditions (response traits) (Flynn et
al. 2009, Díaz et al. 2013). The selected traits were also doubly valuable
as they are associated with functional impacts of mammals (effect traits)
on the environment (Hempson, Archibald & Bond 2015). Two of the
trait variables were continuous (i.e. average body mass, litter size) and
one was an ordered category (i.e. average social group size). For the
remaining three traits, we imposed ordered categories following previous
work on mammal functional traits (Hempson, Archibald & Bond 2015,
Jones et al. 2009, Flynn et al. 2009). Specifically, we ordered diet from
the lowest quality food (grass - grazers) to the highest quality food
(vertebrate meat - carnivore), habitat from the most horizontally oriented
(terrestrial) to the most vertically oriented (scansorial), and activity
period from highest light (diurnal) to lowest light (nocturnal). The
traits with natural or imposed ordered categories (diet, social group
size, habitat, activity period) were analyzed as ordinal variables in the
functional diversity calculations and in our assessment for temporal
trends. No traits were strongly correlated with each other (r <
0.6). 3. Functional diversity We used the abundance-weighted
functional dispersion metric (package `FD` in R; Laliberte &
Lengendre 2010) to calculate annual functional diversity in Volcán Barva
over the eight-year study, using occupancy values as a proxy for abundance
weights. We determined that a functional diversity metric based on
functional dispersion would be the most effective for understanding the
functional effects of this community on its ecosystem based on extensive
work showing the link between community functional dispersion and
ecosystem functioning (Cadotte 2017, Frainer et al. 2014). Functional
dispersion is the mean distance of individual species to the centroid of
all species in a community along functional trait dimensions (Laliberte
& Lengendre 2010). Because species occupancy changed and species
richness remained constant over the eight-year study period (Beaudrot et
al. 2016b), we determined that an investigation into a potential
trait-abundance shift measured with an occupancy-weighted functional
dispersion metric would give the most meaningful results for assessing
functional diversity within a single site (Boersma et al. 2016). Laméris
and colleagues (2019) found differences in functional dispersion in large
mammal communities in Cameroon based on conservation efforts, further
justifying our selection of this metric. 4. Linear modeling To test for
change in functional dispersion over time, we ran a simple linear
regression model with year as the predictor. We evaluated the estimate,
standard error and p-value of the year coefficient from this model. We
also constructed linear models using Gaussian distributions and performed
model selection (package `MuMIn` in R; Barton 2019) to evaluate the
predictive power of environmental and anthropogenic variables for
functional dispersion. The environmental variables were annual
precipitation, new canopy gap area within Volcán Barva, mean fragment size
within the Zone of Interaction of the protected area, and annual rate of
deforestation in the Zone of Interaction. We used AICc model selection to
compare models and defined the best model as the model that had the lowest
AICc value by a margin of 2 or more (Anderson & Burnham 2004).
5. Functional trait distributions over time To test for changes in
individual functional traits over time, we ran linear regression models
for quantitative traits (i.e., body mass, litter size) and ordinal
regression models for ordered traits (i.e., diet, group size, habitat,
activity period) with year as a predictor. We weighted the response
variable for each trait based on the occupancies of the species with the
given trait. We used the estimates, standard deviations and p-values to
assess the statistical significance of temporal trends. 6.
Functional redundancy To estimate functional redundancy, we used occupancy
and functional data to simulate how community functional dispersion
changed as species were removed from the community. We calculated this
change with bootstrapping, randomly drawing subsets of species from the
community and calculating their functional dispersion (see above). Species
richness values ranged from 2 to N-1, where N was the number of species in
the full community (N = 21). We randomly selected 1000 species
combinations without replacement of each species richness value to
generate a distribution of functional dispersion values and used the mean
functional dispersion value in our calculations. For higher species
richness, repetition of species combinations was necessary in the analysis
to generate 1000 functional dispersion values, but the combinations were
still selected randomly without replacement. We performed this analysis
for each year using occupancy values from the respective year of the
eight-year study period. We fit a segmented linear regression model
(package `strucchange` in R; Zeilies et al. 2002) to the bootstrapped
functional dispersion models from all eight years and performed model
selection to determine the number of break points (0-4) in the most robust
regression. The best-fit model was determined by the lowest AIC value. We
used break points to identify functional redundancy, or the number of
species lost from the community before rate of functional dispersion loss
increased, if ever.
Code for using these data to calculate values found in the paper is
available on Github