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.
Similar content being viewed by others
References
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Auto Control 19:716–723. https://doi.org/10.1109/TAC.1974.1100705
Andresen E, Feer F (2005) The role of dung beetles as secondary seed dispersers and their effect on plant regeneration in tropical rainforests. In: Forget P, Lambert J, Hulm P (eds) Seed fate: predation, dispersal, and seedling establishment. CABI Pub, Wallingford, pp 331–341
Andresen E, Laurance SGW (2007) Possible indirect effects of mammal hunting on dung beetle assemblages in Panama. Biotropica 39:141–146. https://doi.org/10.1111/j.1744-7429.2006.00239.x
Barlow J et al (2018) The future of hyperdiverse tropical ecosystems. Nature 559:517–526. https://doi.org/10.1038/s41586-018-0301-1
Barragan F, Moreno CE, Escobar F, Halffter G, Navarrete D (2011) Negative impacts of human land use on dung beetle functional diversity. PLoS ONE 6:e17976. https://doi.org/10.1371/journal.pone.0017976
Berkes F, Colding J, Folke C (2000) Rediscovery of traditional ecological knowledge as adaptive management. Ecol Appl 10:1251–1262. https://doi.org/10.1890/1051-0761(2000)010%5b1251:ROTEKA%5d2.0.CO;2
Blangiardo M, Cameletti M (2015) Spatial and spatio-temporal Bayesian models with R-INLA. Wiley, New York
Cambefort Y (1991) Dung beetles in tropical savannas. In: Hanski I, Cambefort Y (eds) Dung beetle ecology. Princeton University Press, Princeton, pp 156–178
Caro T (2010) Conservation by proxy: indicator, umbrella, keystone, flagship, and other surrogate species. Island Press, Washington
Chapin FS III et al (2000) Consequences of changing biodiversity. Nature 405:234. https://doi.org/10.1038/35012241
Choo J, Zent EL, Simpson BB (2009) The importance of traditional ecological knowledge for palm-weevil cultivation in the Venezuelan Amazon. J Ethnobiol 29:113–128. https://doi.org/10.2993/0278-0771-29.1.113
Davis ALV, Philips TK (2005) Effect of deforestation on a southwest Ghana dung beetle assemblage (Coleoptera: Scarabaeidae) at the periphery of Ankasa conservation area. Environ Entomol 34:1081–1088. https://doi.org/10.1603/0046-225x(2005)034%5b1081:Eodoas%5d2.0.Co;2
Edwards DP, Tobias JA, Sheil D, Meijaard E, Laurance WF (2014) Maintaining ecosystem function and services in logged tropical forests. Trends Ecol Evol 29:511–520. https://doi.org/10.1016/j.tree.2014.07.003
Escobar F, Halffter G, Solis A, Halffter V, Navarrete D (2008) Temporal shifts in dung beetle community structure within a protected area of tropical wet forest: a 35-year study and its implications for long-term conservation. J Appl Ecol 45:1584–1592. https://doi.org/10.1111/j.1365-2664.2008.01551.x
Frazer G, Canham C, Lertzman K (1999) Gap Light Analyzer (GLA), Version 2.0: Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs, users’ manual and program documentation. Simon Fraser University, Burnaby, British Columbia, and the Institute of Ecosystem Studies, Millbrook, New York
Gadgil M, Berkes F, Folke C (1993) Indigenous knowledge for biodiversity conservation. Ambio 22:151–156
Gardner TA, Hernández MIM, Barlow J, Peres CA (2007) Understanding the biodiversity consequences of habitat change: the value of secondary and plantation forests for neotropical dung beetles. J Appl Ecol 45:883–893. https://doi.org/10.1111/j.1365-2664.2008.01454.x
Gill BD (1991) Dung beetles in tropical American forests. In: Hanski I, Cambefort Y (eds) Dung beetle ecology. Princeton University Press, Princeton, pp 211–229
Gotelli NJ (2000) Null model analysis of species co-occurrence patterns. Ecology 81:2606–2621. https://doi.org/10.2307/177478
Griffiths HM, Bardgett RD, Louzada J, Barlow J (2016) The value of trophic interactions for ecosystem function: dung beetle communities influence seed burial and seedling recruitment in tropical forests. Proc Biol Sci. https://doi.org/10.1098/rspb.2016.1634
Halffter G, Edmonds WD (1982) The nesting behavior of dung beetles (Scarabaeinae): an ecological and evolutive approach. Instituto de Ecologia, Mexico
Hanski I (1980) Spatial variation in the timing of the seasonal occurrence in Coprophagous beetles. Oikos 34:311–321. https://doi.org/10.2307/3544290
Hanski I, Cambefort Y (1991) Resource partitioning. In: Hanski I, Cambefort Y (eds) Dung beetle ecology. Princeton University Press, Princeton, pp 330–349
Hill D, Fasham M, Tucker G, Shewry M, Shaw P (2005) Handbook of biodiversity methods: survey, evaluation and monitoring. Cambridge University Press, Cambridge
Hosaka T, Niino M, Kon M, Ochi T, Yamada T, Fletcher CD, Okuda T (2014) Impacts of small-scale clearings due to selective logging on dung beetle communities. Biotropica 46:720–731. https://doi.org/10.1111/btp.12158
Huber O (1995) Geographical and physical features. In: Berry P, Holst B, Yatskievych K (eds) Flora of the Venezuelan Guayana, vol 1. Introduction. Timber Press, Portland, pp 1–61
Hui FKC, Poisot T (2016) Boral - Bayesian ordination and regression analysis of multivariate abundance data in R. Methods Ecol Evol 7:744–750. https://doi.org/10.1111/2041-210x.12514
Hui FKC, Taskinen S, Pledger S, Foster SD, Warton DI, O’Hara RB (2015) Model-based approaches to unconstrained ordination. Methods Ecol Evol 6:399–411. https://doi.org/10.1111/2041-210x.12236
Jakovac CC, Dutrieux LP, Siti L, Pena-Claros M, Bongers F (2017) Spatial and temporal dynamics of shifting cultivation in the middle-Amazonas river: expansion and intensification. PLoS ONE 12:e0181092. https://doi.org/10.1371/journal.pone.0181092
Janzen DH (1983) Seasonal change in abundance of large nocturnal dung beetles (Scarabaeidae) in a Costa Rican deciduous forest and adjacent horse pasture. Oikos 41:274–283. https://doi.org/10.2307/3544274
Kembel SW et al (2010) Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26:1463–1464. https://doi.org/10.1093/bioinformatics/btq166
Laliberte E, Legendre P (2010) A distance-based framework for measuring functional diversity from multiple traits. Ecology 91:299–305. https://doi.org/10.1890/08-2244.1
Laliberté E, Legendre P, Shipley B (2014) FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12
Larsen TH, Forsyth A (2005) Trap spacing and transect design for dung beetle biodiversity studies. Biotropica 37:322–325. https://doi.org/10.1111/j.1744-7429.2005.00042.x
Laurance WF et al (2012) Averting biodiversity collapse in tropical forest protected areas. Nature 489:290
Lawton JH et al (1998) Biodiversity inventories, indicator taxa and effects of habitat modification in tropical forest. Nature 391:72. https://doi.org/10.1038/34166
Levis C et al (2017) Persistent effects of pre-Columbian plant domestication on Amazonian forest composition. Science 355:925–931. https://doi.org/10.1126/science.aal0157
Liu C, Guenard B, Blanchard B, Peng YQ, Economo EP (2016) Reorganization of taxonomic, functional, and phylogenetic ant biodiversity after conversion to rubber plantation. Ecol Monogr 86:215–227. https://doi.org/10.1890/15-1464.1
López-Zent E, Zent S (2004) Amazonian Indians as ecological disturbance agents: the Hotï of the Sierra de Maigualida, Venezuelan Guayana. Adv Econ Bot 15:79–112
Louzada JNC (1998) Considerations on the perching behavior of tropical dung beetles (Coleoptera, Scarabaeidae). Rev Bras Entomol 41:125–128
Luz AC, Paneque-Gálvez J, Guèze M, Pino J, Macía MJ, Orta-Martínez M, Reyes-García V (2017) Continuity and change in hunting behaviour among contemporary indigenous peoples. Biol Conserv 209:17–26. https://doi.org/10.1016/j.biocon.2017.02.002
Mason NWH, Mouillot D, Lee WG, Wilson JB (2005) Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111:112–118. https://doi.org/10.1111/j.0030-1299.2005.13886.x
Mouillot D, Graham NA, Villeger S, Mason NW, Bellwood DR (2013) A functional approach reveals community responses to disturbances. Trends Ecol Evol 28:167–177. https://doi.org/10.1016/j.tree.2012.10.004
Nichols ES, Gardner TA (2011) Dung beetles as a candidate study taxon in applied biodiversity conservation research. In: Simmons W, Ridsdill-Smith T (eds) Ecology and evolution of dung beetles. Blackwell Publishing Ltd, West Sussex, pp 267–290
Nichols E, Larsen T, Spector S, Davis AL, Escobar F, Favila M, Vulinec K (2007) Global dung beetle response to tropical forest modification and fragmentation: a quantitative literature review and meta-analysis. Biol Conserv 137:1–19. https://doi.org/10.1016/j.biocon.2007.01.023
Nichols E et al (2013) Trait-dependent response of dung beetle populations to tropical forest conversion at local and regional scales. Ecology 94:180–189. https://doi.org/10.1890/12-0251.1
Pakeman RJ (2011) Functional diversity indices reveal the impacts of land use intensification on plant community assembly. J Ecol 99:1143–1151. https://doi.org/10.1111/j.1365-2745.2011.01853.x
Plummer M (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In: Hornik K, Leisch F, Zeileis A (eds) Proceedings of the 3rd international workshop on distributed statistical computing (DSC 2003), Vienna, Austria
R Development Core Team (2017) R: a language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria. www.R-project.org/
Redford KH, Sanderson SE (2000) Extracting humans from nature. Conserv Biol 14:1362–1364. https://doi.org/10.1046/j.1523-1739.2000.00135.x
Rue H, Martino S (2009) INLA: functions which allow to perform a full Bayesian analysis of structured additive models using Integrated Nested Laplace Approximation. http://www.r-inla.org
Scheffler PY (2005) Dung beetle (Coleoptera: Scarabaeidae) diversity and community structure across three disturbance regimes in eastern Amazonia. J Trop Ecol 21:9–19. https://doi.org/10.1017/S0266467404001683
Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464
Silva PG, Hernández MIM (2014) Local and regional effects on community structure of dung beetles in a mainland-island scenario. PLoS ONE 9:e111883. https://doi.org/10.1371/journal.pone.0111883
Slade EM, Mann DJ, Villanueva JF, Lewis OT (2007) Experimental evidence for the effects of dung beetle functional group richness and composition on ecosystem function in a tropical forest. J Anim Ecol 76:1094–1104. https://doi.org/10.1111/j.1365-2656.2007.01296.x
Sowig P (1995) Habitat selection and offspring survival rate in three paracoprid dung beetles: the influence of soil type and soil moisture. Ecography 18:147–154. https://doi.org/10.1111/j.1600-0587.1995.tb00335.x
Stearman A (2000) A pound of flesh: social change and modernization as factors in hunting sustainability among neotropical indigenous societies. In: Robinson J, Bennett E (eds) Hunting for sustainability in tropical forests. Columbia University Press, New York, pp 233–250
Warton DI, Blanchet FG, O’Hara RB, Ovaskainen O, Taskinen S, Walker SC, Hui FKC (2015) So many variables: joint modeling in community ecology. Trends Ecol Evol 30:766–779. https://doi.org/10.1016/j.tree.2015.09.007
Watanabe S (2013) A widely applicable bayesian information criterion. J Mach Learn Res 14:867–897
Zent E (2005) The Hunter-self: perforations, prescriptions and primordial beings among the Hodï, Venezuelan Guayana. Tipiti 3:35–76
Zent S, López-Zent E (2004) Ethnobotanical convergence, divergence, and change among the Hoti of the Venezuelan Guayana. Adv Econ Bot 15:37–78
Zent EL, Zent S (2004) Floristic composition, structure, and diversity of four forest plots in the Sierra Maigualida, Venezuelan Guayana. Biodivers Conserv 13:2453–2483. https://doi.org/10.1023/B:BIOC.0000048447.40238.f2
Zent S, Zent E (2012) Jodï horticultural belief, knowledge and practice: incipient or integral cultivation? Bol Mus Para Emílio Goeldi 7:293–338
Zuur AF, Ieno EN (2016a) A protocol for conducting and presenting results of regression-type analyses. Methods Ecol Evol 7:636–645. https://doi.org/10.1111/2041-210x.12577
Zuur AF, Ieno EN (2016b) Beginner´s guide to zero-inflated models with R. Highland Statistics Ltd, Newburgh
Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1:3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x
Zuur AF, Hilbe JM, Ieno EN (2013) A beginner’s guide to GLM and GLMM with R: a frequentist and Bayesian perspective for ecologists. Highland Statistics Ltd, Newburgh
Zuur AF, Ieno EN, Saveliev AA (2017) Beginner’s guide to spatial, temporal, and spatial-temporal ecological data analysis with R-INLA. Highland Statistics Ltd, Newburgh
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by David L Hawksworth.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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
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.
Length of burn-in i.e., the number of iterations to discard at the beginning of the MCMC sampler was 1000.
- 2.
Number of iterations including burn-in was 40,000.
- 3.
Thinning rate was 30.
- 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:
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.
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.
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
.
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 axis | 1 axis | 2 axes | |
---|---|---|---|
WAIC | 1034.081 | 778.5258 | 447.1811* |
EAIC | 1098.503 | 920.0688 | 758.1535* |
EBIC | 1363.391* | 1454.0476 | 1557.0195 |
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10531-019-01879-5