Learning from clinical medicine to improve the use of surrogates in ecology

Oikos

Volumn 124, Issue 4, 2015, Pages 391-398

  Barton, Philip S ^a   Pierson, Jennifer C ^a   Westgate, Martin J ^a   Lane, Peter W ^a   Lindenmayer, David B ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COST-BENEFIT ANALYSIS; DETECTION METHOD; ENVIRONMENTAL CHANGE; ENVIRONMENTAL MONITORING; INTERDISCIPLINARY APPROACH; NUMERICAL MODEL; PREDICTION; SPATIOTEMPORAL ANALYSIS;

EID: 84925943745     PISSN: 00301299     EISSN: 16000706     Source Type: Journal    
DOI: 10.1111/oik.02007     Document Type: Article

References (60)

1. Aronson, J. K. 2005. Biomarkers and surrogate endpoints. - Br. J. Clinical Pharmacol. 59: 491-494.
2. Atkinson, A. J. et al. 2001. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. - Clin. Pharmacol. Therapeutics 69: 89-95.
3. Barton, P. S. et al. 2014. Robustness of habitat-based surrogates of animal diversity: a multi-taxa comparison over time. - J. Appl. Ecol. 51: 1434-1443.
4. Begg, C. B. and Leung, D. H. Y. 2000. On the use of surrogate end points in randomized trials. - J. R. Stat. Soc. Ser. A 163: 15-24.
5. Bockstaller, C. and Girardin, P. 2003. How to validate environmental indicators. - Agric. Systems 76: 639-653.
6. Boone, C. W. and Kelloff, G. J. 1993. Intraepithelial neoplasia, surrogate endpoint markers, and cancer chemoprevention. - J. Cellular Biochem. Suppl. 17F: 37-48.
7. Branton, M. and Richardson, J. S. 2011. Assessing the value of the umbrella-species concept for conservation planning with meta-analysis. - Conserv. Biol. 25: 9-20.
8. Brotman, B. and Prince, A. M. 1988. Gamma-glutamyltransferase as a potential surrogate marker for detection of the non-A, non-B carrier state. - VoxSanguinis 54: 144-147.
9. Buyse, M. and Molenberghs, G. 1998. Criteria for the validation of surrogate endpoints in randomized experiments. - Biometrics 54: 1014-1029.
10. Buyse, M. and Piedbois, P. 1996. On the relationship between response to treatment and survival time. - Stat. Med. 15: 2797-2812.
11. Buyse, M. et al. 2010. Biomarkers and surrogate end points - the challenge of statistical validation. - Nat. Rev. Clin. Oncol. 7: 309-317.
12. Caro, T. 2010. Conservation by proxy: indicator, umbrella, keystone, flagship and other surrogate species. - Island Press.
13. Caro, T. M. and O'Doherty, G. 1999. On the use of surrogate species in conservation biology. - Conserv. Biol. 13: 805-814.
14. Chown, S. L. and McGeoch, M. A. 2011. Measuring biodiversity in managed landscapes. - In: Magurran, A. and McGill, B. (eds), Biological diversity: frontiers in its measurement and assessment. Oxford Univ. Press, pp. 252-264.
15. Colburn, W. A. 2000. Optimizing the use of biomarkers, surrogate endpoints, and clinical endpoints for more efficient drug development. - J. Clin. Pharmacol. 40: 1419-1427.
16. Conti, M. E. and Cecchetti, G. 2001. Biological monitoring: lichens as bioindicators of air pollution assessment - a review. - Environ. Pollut. 114: 471-492.
17. Drever, M. C. et al. 2008. Woodpeckers as reliable indicators of bird richness, forest health and harvest. - Biol. Conserv. 141: 624-634.
18. Faith, D. P. and Walker, P. A. 1996. Environmental diversity: on the best-possible use of surrogate data for assessing the relative biodiversity of sets of areas. - Biodivers. Conserv. 5: 399-415.
19. Fazey, I. et al. 2004. Can methods applied in medicine be used to summarize and disseminate conservation research? - Environ. Conserv. 31: 190-198.
20. Feld, C. K. et al. 2009. Indicators of biodiversity and ecosystem services: a synthesis across ecosystems and spatial scales. - Oikos 118: 1862-1871.
21. Fleishman, E. et al. 2005. Using indicator species to predict species richness of multiple taxonomic groups. - Conserv. Biol. 19: 1125-1137.
22. Fleming, T. R. and Powers, J. H. 2012. Biomarkers and surrogate endpoints in clinical trials. - Stat. Med. 31: 2973-2984.
23. Ghosh, D. 2012. A causal framework for surrogate endpoints with semi-competing risks data. - Stat. Probabil. Lett. 82: 1898-1902.
24. Gibbons, P. et al. 2008. Rapidly quantifying reference conditions in modified landscapes. - Biol. Conserv. 141: 2483-2493.
25. Gilbert, P. B. et al. 2008. Evaluating a surrogate endpoint at three levels, with application to vaccine development. - Stat. Med. 27: 4758-4778.
26. Gollan, J. R. et al. 2009. Testing common habitat-based surrogates of invertebrate diversity in a semi-arid rangeland. - Biodivers. Conserv. 18: 1147-1159.
27. Goodsell, P. J. et al. 2009. Evidence necessary for taxa to be reliable indicators of environmental conditions or impacts. - Mar. Pollut. Bull. 58: 323-331.
28. Grace, J. B. et al. 2010. On the specification of structural equation models for ecological systems. - Ecol. Monogr. 80: 67-87.
29. Joffe, M. M. and Greene, T. 2009. Related causal frameworks for surrogate outcomes. - Biometrics 65: 530-538.
30. Kleijn, D. et al. 2006. Mixed biodiversity benefits of agri-environment schemes in five European countries. - Ecol. Lett. 9: 243-254.
31. Li, Y. et al. 2011. Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials. - Biostatistics 12: 478-492.
32. Lindenmayer, D. B. and Likens, G. E. 2010. The science and application of ecological monitoring. - Biol. Conserv. 143: 1317-1328.
33. Lindenmayer, D. B. and Likens, G. E. 2011. Direct measurement versus surrogate indicator species for evaluating environmental change and biodiversity loss. - Ecosystems 14: 47-59.
34. Lindenmayer, D. B. et al. 2012. A novel and cost-effective monitoring approach for outcomes in an Australian biodiversity conservation incentive program. - PLoS ONE 7: e50872.
35. Lindenmayer, D. B. et al. 2014. An empirical assessment and comparison of species-based and habitat-based surrogates: a case study of forest vertebrates and large old trees. - PloS ONE 9: e89807.
36. Manning, A. D. et al. 2013. Bringing forward the benefits of coarse woody debris in ecosystem recovery under different levels of grazing and vegetation density. - Biol. Conserv. 157: 204-214.
37. McGeoch, M. A. 1998. The selection, testing and application of terrestrial insects as bioindicators. - Biol. Rev. 73: 181-201.
38. McGeoch, M. A. et al. 2002. The verification and application of bioindicators: a case study of dung beetles in a savanna ecosystem. - J. Appl. Ecol. 39: 661-672.
39. Mellin, C. et al. 2011. Effectiveness of biological surrogates for predicting patterns of marine biodiversity: a global meta-analysis. - PLoS ONE 6: e20141.
40. Molenberghs, G. et al. 2002. Statistical challenges in the evaluation of surrogate endpoints in randomized trials. - Contr. Clin. Trials 23: 607-625.
41. Niemi, G. J. and McDonald, M. E. 2004. Apllication of ecological indicators. - Annu. Rev. Ecol. Evol. Syst. 35: 89-111.
42. Niemi, G. et al. 2004. Rationale for a new generation of indicators for coastal waters. - Environ. Health Perspect. 112: 979-986.
43. NIH Definitions Working Group 2000. Biomarkers and surrogate endpoints in clinical research: defnitions and conceptual model. - In: Downing, G. J. (ed.), Biomarkers and surrogate endpoints. Elsevier, pp. 1-9.
44. Noss, R. F. 1990. Indicators for monitoring biodiversity - a hierarchical approach. - Conserv. Biol. 4: 355-364.
45. Perkins, A. J. et al. 2011. Adaptive management and targeting of agri-environment schemes does benfit biodiversity: a case study of the corn bunting Emberiza calandra. - J. Appl. Ecol. 48: 514-522.
46. Pierson, J. C. et al. 2015. Can habitat surrogates predict the response of target species to landscape change? - Biol. Conserv. 184: 1-10.
47. Prentice, R. L. 1989. Surrogate endpoints in clinical trials - definition and operational criteria. - Stat. Med. 8: 431-440.
48. Prober, S. M. and Thiele, K. R. 2005. Restoring Australia's temperate grasslands and grassy woodlands: integrating function and diversity. - Ecol. Manage. Restor. 6: 16-26.
49. Pullin, A. S. and Knight, T. M. 2001. Effectiveness in conservation practice: pointers from medicine and public health. - Conserv. Biol. 15: 50-54.
50. 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.
51. Shipley, B. 2009. Confirmatory path analysis in a generalized multilevel context. - Ecology 90: 363-368.
52. Sinclair, R. G. et al. 2012. Criteria for selection of surrogates used to study the fate and control of pathogens in the environment. - Appl. Environ. Microbiol. 78: 1969-1977.
53. Sutherland, W. J. et al. 2004. The need for evidence-based conservation. - Trends Ecol. Evol. 19: 305-308.
54. Taylor, J. M. G. and Wang, Y. 2002. Surrogate markers and joint models for longitudinal and survival data. - Contr. Clin. Trials 23: 626-634.
55. The Cochrane Collaboration 2014. The Cochrane Collaboration. Trusted evidence. Informed decisions. Better health. . Accessed 3 March2014.
56. VanderWeele, T. J. 2013. Surrogate measures and consistent surrogates. - Biometrics 69: 561-581.
57. Weir, C. J. and Walley, R. J. 2006. Statistical evaluation of biomarkers as surrogate endpoints: a literature review. - Stat. Med. 25: 183-203.
58. Westgate, M. J. et al. 2013. Adaptive management of biological systems: a review. - Biol. Conserv. 158: 128-139.
59. Westgate, M. J. et al. 2014. Global meta-analysis reveals low consistency of biodiversity congruence relationships. - Nat. Comm. doi: 10.1038/ncomms4899.
60. Zammit, C. et al. 2010. Using markets for woodland conservation on private land: lessons from the policy-research interface. - In: Lindenmayer, D. B. et al. (eds), Temperate woodland conservation and management. CSIRO Publishing, pp. 297-307.