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Impacts of an indigenous settlement on the taxonomic and functional structure of dung beetle communities in the Venezuelan Amazon

Abstract

In the last 50 years, traditionally nomadic indigenous communities in Amazonia have increasingly adopted more sedentary lifestyles as a result of external influences. Permanent settlements lead to the concentration of disturbances (e.g., forest extraction and hunting) and threaten vulnerable species as well as those that provide important ecosystem services such as dung beetles. Here we evaluated the abundance, taxonomic, and functional structure (composition and diversity) of an ecological indicator group—dung beetles—along a disturbance gradient associated with a permanent settlement of the Jotï people in the Amazonian region of Venezuela. We applied generalized linear model to assess the response of dung beetle abundance to settlement distance and latent variable model to assess the influence of settlement distance on taxonomic diversity and functional structure. We found the abundance of roller-species increased but small-bodied beetles decreased away from the settlement. We found that proximity to the Jotï settlement did not affect metrics of taxonomic and functional diversity of the dung beetle assemblages in general, although functional evenness was lower away from the settlement. In contrast, we found impacts on the functional composition of dung beetles, with significant increase in the community-weighted means for roller species and large-bodied dung beetles away from Jotï settlement. Our findings suggest that the transition from nomadism to a more sedentary lifestyle has not caused widespread collapse in the diversity of dung beetle assemblages surrounding the settlement, however significant trends were observed in species-specific responses to human impact, and these responses were mediated by functional traits.

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Acknowledgements

We thank the Jotï people for their contribution and collaboration on this study and their kind hospitality during our stay with them. We are also grateful to our colleagues at Laboratorio Ecología Humana de IVIC for logistical support. We thank Cong Liu and Nicholas Friedman for their input, Kenneth Dudley for his help with Figure 1, and two anonymous reviewers for their comments on the manuscript. Funding for this research was provided by the Charles H. and Anne Morrow Lindbergh Foundation and the American Philosophical Society. J.C. and E.P.E. were supported by subsidy funding to the Okinawa Institute of Science and Technology.

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Correspondence to Juanita Choo.

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Communicated by David L Hawksworth.

Appendices

Appendix 1

Justification for the use of negative binomial GLMM

The reasons for using the negative binomial distribution and a GLMM in our analyses are as follows. Initial analysis using a Poisson GLMM resulted in overdispersion due to the relatively large number of zeros in the beetle abundance (60%). Zero inflated Poisson models (Zuur and Ieno 2016b) were also overdispersed and we therefore applied the negative binomial GLMM.

Model equation for negative binomial GLMM

$$Beetles_{ij} \sim NB\left( {\mu_{ij} ,\theta } \right)$$
$$E\left( {Beetles_{ij} } \right) = \mu_{ij}$$
$$\mu_{ij} = e^{{Intercept + Covariates_{ij}+a_{i}+ s_{j}}}$$
$$a_{i} \sim N\left( {0,\sigma_{Trap}^{2}}\right)$$
$$s_{j} \sim N\left( {0,\sigma_{Species}^{2}}\right)$$

Beetlesij is the number of beetles caught in trap i for species j, where i = 1,.., 55 and j = 1,..,26. To allow for dependency between multiple observations from the same trap, we included a random intercept site (ai), which is assumed to be normal and independently distributed with mean 0 and variance σ2Trap. We also included a random intercept species (bj), which is assumed to be normal and independently distributed with mean 0 and variance σ2Species. This random effect imposes a dependency structure between observed beetle numbers from the same species (i.e. observations from different traps from the same species).

Appendix 2

Model fitting using Bayesian context using Markov Chain Monte Carlo (MCMC) implemented in JAGS

Models were fit in a Bayesian context using Markov Chain Monte Carlo (MCMC) techniques implemented in JAGS (Plummer 2003) from within R (further details in Appendix 1). A burn-in of 80,000 iterations was used with 3 chains. We used a thinning rate of 100 and 4500 iterations were used for each posterior distribution. All continuous covariates were standardized. Once the models were fitted, model validation was applied to investigate the presence of any residual patterns. We plotted posterior mean Pearson residuals versus posterior mean fitted values, versus each covariate, and we also inspected the posterior mean residuals for any spatial dependency, and dependency between species.

The specification controlling the MCMC sampling for the LVM model uses the default values in the ‘boral’ package and are as follows:

  1. 1.

    Length of burn-in i.e., the number of iterations to discard at the beginning of the MCMC sampler was 1000.

  2. 2.

    Number of iterations including burn-in was 40,000.

  3. 3.

    Thinning rate was 30.

  4. 4.

    Seed for JAGS sampler was set to the value 123.

Appendix 3

Formulation of the latent variable model

The formulation of the LVM is as follows:

$$Y_{ij} \sim N\left( {\mu_{ij} ,\sigma_{j}^{2} } \right)$$
$$E\left( {Y_{ij} } \right) = \mu_{ij} = \alpha_{j} + X_{j} \times \beta_{j} + u_{ij}$$

The Yij is the value of the ith observation on diversity or functional structure index j. There are 9 response variables, hence j = 1, …, 9. And there are 55 sites, which means that i = 1, …, 55. The xj contains the 5 covariates. In an ordinary linear regression model, we assume that the residual terms uij are independent and normally distributed. In a multivariate GLMM we use a non-diagonal residual covariance matrix to model dependency between response variables. In the LVM we use the following construction.

$$u_{ij} = Z_{i}^{'} \times \lambda_{j}$$

The residuals uij are equal to a linear combination of typically 2 latent variables zi (each of length 55), very much like axes in ordination techniques (e.g. principal component analysis, canonical correspondence analysis). The λjs are factor loadings and tell us how the latent axes influence the response variables. Model validation followed the steps described in Hui et al. (2015). A more detailed description of LVM models can be found in Warton et al. (2015). We compared LVMs with 0, 1 and 2 latent axes using the widely applicable information criterion (WAIC), the expected Akaike information criterion (EAIC) and the expected Bayesian information criterion (BIC). See the ‘boral’ package documentation for or original references (Akaike 1974; Schwarz 1978; Watanabe 2013) explanation how these are calculated.

Appendix 4

Multicollinearity plot of the covariates

The multicollinearity plot of the covariates for three species traits (activity period, size, and nesting strategy), distance from settlement, and vegetation characteristics (plant species richness, and canopy openness). The plot includes values within the boxes that indicate variance inflation factor (VIF), which quantifies the severity of multicollinearity. VIF values indicate minimal collinearity among covariates. Boxes without numbers indicate the VIF values were very small. Following Zuur et al. (2010), we applied a VIF threshold value of 3 and below to support low levels of multicollinearity.

figurea

Appendix 5

Plots for model validation to verify the presence of any residual patterns

The following figures show a posterior mean Pearson residuals versus posterior mean fitted values; b posterior mean Pearson residuals versus covariates; c semivariogram plot of posterior mean residuals for any spatial dependency

figureb

.

Appendix 6

Pearson residuals for each dung beetle species combination

  AteC CanthiG CanthiK Canthisp1 Canthisp2 CanthonQ CanthonSemio CanthonT DelG DelO DelS DichB DichD
AteC   − 0.3 − 0.2 − 0.1 0.1 − 0.2 − 0.1 − 0.1 − 0.1 0.2 0.2 0.5 − 0.2
CanthiG    0.1 0.2 − 0.1 0.1 − 0.3 − 0.3 0.2 − 0.3 0.0 − 0.3 − 0.2
CanthiK     0.2 0.2 − 0.2 0.1 − 0.1 − 0.1 0.0 0.1 − 0.1 0.0
Canthisp1      0.3 − 0.2 − 0.2 − 0.3 0.1 − 0.2 0.1 − 0.2 − 0.2
Canthisp2       − 0.1 − 0.1 − 0.2 − 0.1 − 0.2 0.0 − 0.1 − 0.2
CanthonQ        0.2 0.1 − 0.1 0.3 0.0 0.0 0.0
CanthonSemio         0.4 0.1 0.1 − 0.1 − 0.1 0.1
CanthonT          − 0.1 0.0 − 0.3 − 0.1 0.0
DelG           − 0.1 0.2 − 0.1 0.2
DelO            0.2 0.4 − 0.1
DelS             0.4 − 0.2
DichB              − 0.2
DichD              
DichL              
DichM              
DichP              
EuryC              
EuryH              
EuryHy              
OntR              
OxyC              
OxyF              
OxyS              
ScyC              
SylB              
Uro              
AteC DichL DichM DichP EuryC EuryH EuryHy OntR OxyC OxyF OxyS ScyC SylB Uro
CanthiG 0.6 0.0 − 0.1 − 0.1 − 0.2 0.3 0.4 0.0 0.2 0.1 − 0.1 − 0.3 − 0.1
CanthiK − 0.1 − 0.3 − 0.1 0.1 − 0.1 − 0.3 − 0.1 − 0.2 0.1 0.1 0.1 0.0 − 0.3
Canthisp1 − 0.1 0.0 − 0.2 0.2 − 0.3 0.1 − 0.1 − 0.1 − 0.1 − 0.1 0.1 − 0.1 − 0.1
Canthisp2 0.1 − 0.2 0.2 0.4 0.0 − 0.2 − 0.1 − 0.2 − 0.1 − 0.1 0.2 0.0 0.0
CanthonQ 0.0 − 0.1 0.0 0.4 0.0 0.0 0.0 − 0.1 0.0 0.0 0.0 0.3 − 0.1
CanthonSemio − 0.2 0.1 0.2 − 0.1 0.2 0.0 − 0.2 − 0.1 − 0.1 − 0.1 − 0.1 0.0 0.0
CanthonT − 0.1 0.2 0.0 0.0 − 0.1 − 0.1 0.0 0.2 − 0.1 0.0 − 0.1 − 0.1 0.1
DelG − 0.2 0.5 0.0 − 0.1 0.3 0.0 − 0.1 0.0 − 0.2 − 0.2 − 0.2 0.0 0.2
DelO 0.0 − 0.2 0.0 0.0 0.0 − 0.1 − 0.1 − 0.2 − 0.1 0.2 0.1 0.0 − 0.1
DelS 0.1 0.2 0.0 − 0.2 0.0 0.1 − 0.1 0.1 0.0 0.0 0.1 − 0.3 0.1
DichB 0.2 − 0.2 − 0.3 − 0.1 0.1 0.1 − 0.1 − 0.2 0.1 − 0.1 0.6 − 0.1 − 0.2
DichD 0.2 0.1 − 0.1 − 0.1 − 0.2 0.3 0.2 0.0 0.0 − 0.1 0.1 − 0.1 − 0.1
DichL − 0.2 0.0 0.2 0.0 − 0.1 − 0.1 − 0.1 0.1 − 0.2 0.0 − 0.2 0.2 0.0
DichM   − 0.1 − 0.1 0.0 − 0.1 0.0 0.2 0.0 0.1 0.1 − 0.1 − 0.3 − 0.2
DichP    0.1 − 0.1 − 0.1 − 0.1 − 0.1 0.2 − 0.2 − 0.1 − 0.2 0.0 0.0
EuryC     0.0 0.0 − 0.1 − 0.1 0.0 0.0 0.1 − 0.1 0.1 − 0.1
EuryH      − 0.1 − 0.1 0.0 0.1 0.3 − 0.3 − 0.1 0.2 − 0.2
EuryHy       − 0.2 − 0.2 0.0 0.0 0.0 0.2 0.0 − 0.1
OntR        0.5 0.0 0.1 0.0 0.1 0.0 0.0
OxyC         0.1 0.2 0.0 − 0.2 0.1 − 0.1
OxyF          − 0.1 0.0 0.1 − 0.1 0.0
OxyS           − 0.1 0.0 0.0 − 0.1
ScyC            − 0.1 0.0 − 0.1
SylB             0.0 − 0.1
Uro              − 0.1

Appendix 7

Results of WAIC, EAIC, and BIC for the latent variable model

Results of the widely applicable information criterion (WAIC), the expected Akaike information criterion (EAIC) and the expected Bayesian information criterion (BIC) comparing the latent variable models with 0, 1 and 2 latent axes. Values with asterisk indicate the preferred latent variable model with 0, 1, or 2 axes.

 0 axis1 axis2 axes
WAIC1034.081778.5258447.1811*
EAIC1098.503920.0688758.1535*
EBIC1363.391*1454.04761557.0195

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Choo, J., Gill, B.D., Zuur, A.F. et al. Impacts of an indigenous settlement on the taxonomic and functional structure of dung beetle communities in the Venezuelan Amazon. Biodivers Conserv 29, 207–228 (2020). https://doi.org/10.1007/s10531-019-01879-5

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Keywords

  • Disturbance
  • Functional diversity
  • Functional evenness
  • Functional trait
  • Jotï
  • Human impact