Optimal taxonomic groups for biodiversity assessment: a meta-analytic approach

Ecography

Volumn 40, Issue 4, 2017, Pages 539-548

  Westgate, Martin J ^a   Tulloch, Ayesha I T ^a   Barton, Philip S ^a   Pierson, Jennifer C ^a   Lindenmayer, David B ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ASSESSMENT METHOD; BIODIVERSITY; BIOMONITORING; BIRD; DECISION ANALYSIS; GLOBAL PERSPECTIVE; META-ANALYSIS; OPTIMIZATION; SPECIES RICHNESS; STATISTICAL ANALYSIS; TAXONOMY; VASCULAR PLANT;

AVES; TRACHEOPHYTA;

EID: 84963987432     PISSN: 09067590     EISSN: 16000587     Source Type: Journal    
DOI: 10.1111/ecog.02318     Document Type: Article

References (67)

1. Araujo, M. B. et al. 2004. Representing species in reserves from patterns of assemblage diversity. – J. Biogeogr. 31: 1037–1050.
2. Arnqvist, G. and Wooster, D. 1995. Meta-analysis: synthesizing research findings in ecology and evolution. – Trends Ecol. Evol. 10: 236–240.
3. Balmford, A. and Gaston, K. J. 1999. Why biodiversity surveys are good value. – Nature 398: 204–205.
4. Barberan, A. et al. 2012. Using network analysis to explore co-occurrence patterns in soil microbial communities. – ISME J. 6: 343–351.
5. Barlow, J. et al. 2007. Quantifying the biodiversity value of tropical primary, secondary, and plantation forests. – Proc. Natl Acad. Sci. USA 104: 18555–18560.
6. Barreto de Andrade, R. et al. 2014. Biotic congruence in humid tropical forests: a multi-taxa examination of spatial distribution and responses to forest disturbance. – Ecol. Indicators 36: 572–581.
7. Barton, P. S. et al. 2013. The spatial scaling of beta diversity. – Global Ecol. Biogeogr. 22: 639–647.
8. Barton, P. S. et al. 2014. Robustness of habitat-based surrogates of animal diversity: a multi-taxa comparison over time and after fire. – J. Appl. Ecol. 51: 1434–1443.
9. Boccaletti, S. et al. 2006. Complex networks: structure and dynamics. – Phys. Rep. 424: 175–308.
10. Brown, C. J. et al. 2015. Effective conservation requires clear objectives and prioritizing actions, not places or species. – Proc. Natl Acad. Sci. USA 112: E4342.
11. Butchart, S. H. M. et al. 2012. Protecting important sites for biodiversity contributes to meeting global conservation targets. – PLoS One 7: e32529.
12. Canessa, S. et al. 2015. When do we need more data? A primer on calculating the value of information for applied ecologists. – Methods Ecol. Evol. 6: 1219–1228.
13. Castagneyrol, B. and Jactel, H. 2012. Unraveling plant–animal diversity relationships: a meta-regression analysis. – Ecology 93: 2115–2124.
14. Chadès, I. et al. 2015. Benefits of integrating complementarity into priority threat management. – Conserv. Biol. 29: 525–536.
15. Chamberlain, S. A. et al. 2014. How context dependent are species interactions? – Ecol. Lett. 17: 881–890.
16. Darwall, W. R. T. et al. 2011. Implications of bias in conservation research and investment for freshwater species. – Conserv. Lett. 4: 474–482.
17. Dehling, D. M. et al. 2014. Functional relationships beyond species richness patterns: trait matching in plant–bird mutualisms across scales. – Global Ecol. Biogeogr. 23: 1085–1093.
18. Dumbrell, A. J. et al. 2008. Changes in species diversity following habitat disturbance are dependent on spatial scale: theoretical and empirical evidence. – J. Appl. Ecol. 45: 1531–1539.
19. Eglington, S. M. et al. 2012. A meta-analysis of spatial relationships in species richness across taxa: birds as indicators of wider biodiversity in temperate regions. – J. Nat. Conserv. 20: 301–309.
20. Ekroos, J. et al. 2013. Correlations in species richness between taxa depend on habitat, scale and landscape context. – Ecol. Indicators 34: 528–535.
21. Faith, D. P. 2003. Environmental diversity (ED) as surrogate information for species-level biodiversity. – Ecography 26: 374–379.
22. Field, S. A. et al. 2004. Minimizing the cost of environmental management decisions by optimizing statistical thresholds. – Ecol. Lett. 7: 669–675.
23. Gardner, T. A. et al. 2008. The cost-effectiveness of biodiversity surveys in tropical forests. – Ecol. Lett. 11: 139–150.
24. Gaspar, C. et al. 2010. Arthropods as surrogates of diversity at different spatial scales. – Biol. Conserv. 143: 1287–1294.
25. Gaston, K. J. and Williams, P. H. 1993. Mapping the World's species – the higher taxon approach. – Biodivers. Lett. 1: 2–8.
26. Grantham, H. S. et al. 2010. Effectiveness of biodiversity surrogates for conservation planning: different measures of effectiveness generate a kaleidoscope of variation. – PLoS One 5: e11430.
27. Hawkins, B. A. and Porter, E. E. 2003. Does herbivore diversity depend on plant diversity? The case of California butterflies. – Am. Nat. 161: 40–49.
28. Heino, J. 2010. Are indicator groups and cross-taxon congruence useful for predicting biodiversity in aquatic ecosystems? – Ecol. Indicators 10: 112–117.
29. Heino, J. 2014. Taxonomic surrogacy, numerical resolution and responses of stream macroinvertebrate communities to ecological gradients: are the inferences transferable among regions? – Ecol. Indicators 36: 186–194.
30. Hess, G. R. et al. 2006. Effectiveness of biodiversity indicators varies with extent, grain, and region. – Biol. Conserv. 132: 448–457.
31. Hunter Jr, M. et al. 2016. Two roles for ecological surrogacy: indicator surrogates and management surrogates. – Ecol. Indicators 63: 121–125.
32. Ings, T. C. et al. 2009. Review: ecological networks – beyond food webs. – J. Anim. Ecol. 78: 253–269.
33. Johnson, F. A. et al. 2015. Multilevel learning in the adaptive management of waterfowl harvests: 20 years and counting. – Wildl. Soc. Bull. 39: 9–19.
34. Kessler, M. et al. 2011. Cost-effectiveness of plant and animal biodiversity indicators in tropical forest and agroforest habitats. – J. Appl. Ecol. 48: 330–339.
35. Landeiro, V. L. et al. 2012. How far can we go in simplifying biomonitoring assessments? An integrated analysis of taxonomic surrogacy, taxonomic sufficiency and numerical resolution in a megadiverse region. – Ecol. Indicators 23: 366–373.
36. Langford, E. et al. 2001. Is the property of being positively correlated transitive? – Am. Stat. 55: 322–325.
37. Larsen, F. W. et al. 2009. Indicator taxa revisited: useful for conservation planning? – Divers. Distrib. 15: 70–79.
38. Lawler, J. J. et al. 2015. The theory behind, and the challenges of, conserving nature's stage in a time of rapid change. – Conserv. Biol. 29: 618–629.
39. Legendre, P. et al. 2015. Should the Mantel test be used in spatial analysis? – Methods Ecol. Evol. 6: 1239–1247.
40. Lewandowski, A. S. et al. 2010. The effectiveness of surrogate taxa for the representation of biodiversity. – Conserv. Biol. 24: 1367–1377.
41. Lindenmayer, D. B. and Likens, G. E. 2009. Adaptive monitoring: a new paradigm for long-term research and monitoring. – Trends Ecol. Evol. 24: 482–486.
42. Lindenmayer, D. et al. 2015. A new framework for selecting environmental surrogates. – Sci. Total Environ. 538: 1029–1038.
43. Lung, T. et al. 2014. Biodiversity funds and conservation needs in the EU under climate change. – Conserv. Lett. 7: 390–400.
44. Martin, L. J. et al. 2012. Mapping where ecologists work: biases in the global distribution of terrestrial ecological observations. – Front. Ecol. Environ. 10: 195–201.
45. McArthur, M. A. et al. 2010. On the use of abiotic surrogates to describe marine benthic biodiversity. – Estuarine Coastal Shelf Sci. 88: 21–32.
46. McGeogh, M. A. 1998. The selection, testing and application of terrestrial insects as bioindicators. – Biol. Rev. 73: 181–201.
47. McGill, B. J. 2010. Towards a unification of unified theories of biodiversity. – Ecol. Lett. 13: 627–642.
48. McGill, B. J. et al. 2015. Fifteen forms of biodiversity trend in the Anthropocene. – Trends Ecol. Evol. 30: 104–113.
49. McGowan, C. P. et al. 2015. Implementation of a framework for multi-species, multi-objective adaptive management in Delaware Bay. – Biol. Conserv. 191: 759–769.
50. Mellin, C. et al. 2011. Effectiveness of biological surrogates for predicting patterns of marine biodiversity: a global meta-analysis. – PLoS One 6: e20141.
51. Moilanen, A. 2008. Generalized complementarity and mapping of the concepts of systematic conservation planning. – Conserv. Biol. 22: 1655–1658.
52. Rigby, R. A. and Stasinopoulos, D. M. 2005. Generalized additive models for location, scale and shape. – J. R. Stat. Soc. Ser. C 54: 507–554.
53. Rodrigues, A. S. L. and Brooks, T. M. 2007. Shortcuts for biodiversity conservation planning: the effectiveness of surrogates. – Annu. Rev. Ecol. Evol. Syst. 38: 713–737.
54. Sachs, J. D. et al. 2009. Biodiversity conservation and the millennium development goals. – Science 325: 1502–1503.
55. Sarkar, S. and Margules, C. 2002. Operationalizing biodiversity for conservation planning. – J. Biosci. 27: 299–308.
56. Schall, J. J. and Pianka, E. R. 1978. Geographical trends in numbers of species. – Science 201: 679–686.
57. Schneidman, E. et al. 2006. Weak pairwise correlations imply strongly correlated network states in a neural population. – Nature 440: 1007–1012.
58. Song, S. et al. 2005. Highly nonrandom features of synaptic connectivity in local cortical circuits. – PLoS Biol. 3: e68.
59. Supp, S. R. and Ernest, S. K. M. 2014. Species-level and community-level responses to disturbance: a cross-community analysis. – Ecology 95: 1717–1723.
60. Tulloch, A. I. T. et al. 2013. Accounting for complementarity to maximize monitoring power for species management. – Conserv. Biol. 27: 988–999.
61. Tulloch, A. I. T. et al. 2016. Dynamic species co-occurrence networks require dynamic biodiversity surrogates. – Ecography In press.
62. Velghe, K. and Gregory-Eaves, I. 2013. Body size is a significant predictor of congruency in species richness patterns: a meta-analysis of aquatic studies. – PLoS One 8: e57019.
63. Westgate, M. J. et al. 2013. Adaptive management of biological systems: a review. – Biol. Conserv. 158: 128–139.
64. Westgate, M. J. et al. 2014. Global meta-analysis reveals low consistency of biodiversity congruence relationships. – Nat. Commun. 5: 3899.
65. Westgate, M. J. et al. 2016. Data from: Optimal taxonomic groups for biodiversity assessment: a meta-analytic approach. – Dryad Digital Repository, < http://dx.doi.org/10.5061/dryad.s1660 >.
66. Wisz, M. S. et al. 2013. The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. – Biol. Rev. 88: 15–30.
67. Wolters, V. et al. 2006. Relationship among the species richness of different taxa. – Ecology 87: 1886–1895.