Text analysis tools for identification of emerging topics and research gaps in conservation science

Conservation Biology

Volumn 29, Issue 6, 2015, Pages 1606-1614

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

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Hot topics; Indicators; Latent Dirichlet allocation; Surrogates; Synthesis

Indexed keywords

AUTOMATION; CONSERVATION MANAGEMENT; ECOSYSTEM MANAGEMENT; ENVIRONMENTAL INDICATOR; IDENTIFICATION METHOD; META-ANALYSIS; STRATEGIC APPROACH; SURROGATE METHOD;

ANIMAL; DATA MINING; ECOSYSTEM; ENVIRONMENTAL PROTECTION; GENETICS; INVERTEBRATE; PLANT; PROCEDURES; STATISTICS; VERTEBRATE;

ANIMALS; CONSERVATION OF NATURAL RESOURCES; DATA MINING; ECOSYSTEM; INVERTEBRATES; PLANTS; STATISTICS AS TOPIC; VERTEBRATES;

EID: 84955190240     PISSN: 08888892     EISSN: 15231739     Source Type: Journal    
DOI: 10.1111/cobi.12605     Document Type: Article

References (48)

1. Barton PS, Westgate MJ, Lane PW, MacGregor C, Lindenmayer DB. 2014. Robustness of habitat-based surrogates of animal diversity: a multi-taxa comparison over time and after fire. Journal of Applied Ecology 51:1434-1443.
2. Bates D, Maechler M, Bolker B, Walker S. 2014. Lme4: linear mixed-effects models using Eigen and s4. R package version 1:1-6.
3. Beja-Pereira A, Oliveira R, Alves PC, Schwartz MK, Luikart G. 2009. Advancing ecological understandings through technological transformations in noninvasive genetics. Molecular Ecology Resources 9:1279-1301.
4. Bennett EM, Cumming GS, Peterson GD. 2005. A systems model approach to determining resilience surrogates for case studies. Ecosystems 8:945-957.
5. Blei DM, Ng AY, Jordan MI. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3:993-1022.
6. Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White J-SS. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution 24:127-135.
7. Boyack KW, Klavans R. 2014. Creation of a highly detailed, dynamic, global model and map of science. Journal of the Association for Information Science and Technology 65:670-685.
8. Boyack KW, Small H, Klavans R. 2013. Improving the accuracy of co-citation clustering using full text. Journal of the American Society for Information Science and Technology 64:1759-1767.
9. Chen C, Chen Y, Horowitz M, Hou H, Liu Z, Pellegrino D. 2009. Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics 3:191-209.
10. Collen B, Nicholson E. 2014. Taking the measure of change. Science 346:166-167.
11. De Cáceres M, Legendre P, Moretti M. 2010. Improving indicator species analysis by combining groups of sites. Oikos 119:1674-1684.
12. Ewers RM, Marsh CJ, Wearn OR. 2010. Making statistics biologically relevant in fragmented landscapes. Trends in Ecology & Evolution 25:699-704.
13. Gaston KJ, Blackburn TM, Greenwood JJD, Gregory RD, Quinn RM, Lawton JH. 2000. Abundance-occupancy relationships. Journal of Applied Ecology 37:39-59.
14. Griffiths TL, Steyvers M. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences (USA) 101:5228-5235.
15. Grimmer J, Stewart BM. 2013. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis:1-31.
16. Gruen B, Hornik K. 2011. Topicmodels: An r package for fitting topic models. Journal of Statistical Software 40:1-30.
17. Harte J. 2011. Maximum entropy and ecology: a theory of abundance, distribution, and energetics. Oxford University Press, Oxford.
18. Herrando-Pérez S, Delean S, Brook B, Bradshaw CA. 2012. Density dependence: an ecological tower of babel. Oecologia 170:585-603.
19. Ings TC, et al. 2009. Review: ecological networks-beyond food webs. Journal of Animal Ecology 78:253-269.
20. Larsen P, von Ins M. 2010. The rate of growth in scientific publication and the decline in coverage provided by science citation index. Scientometrics 84:575-603.
21. Legendre P, Legendre LFJ. 2012. Numerical ecology, 3rd English edn. Elsevier, Amsterdam.
22. Lindenmayer D, Likens G. 2011. Direct measurement versus surrogate indicator species for evaluating environmental change and biodiversity loss. Ecosystems 14:47-59.
23. Lindenmayer DB, Barton PS, Lane PW, Westgate MJ, McBurney L, Blair D, Gibbons P, Likens GE. 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.
24. Lindenmayer DB, Zammit C, Attwood SJ, Burns E, Shepherd CL, Kay G, Wood J. 2012. A novel and cost-effective monitoring approach for outcomes in an Australian biodiversity conservation incentive program. PLoS ONE 7:e50872.
25. Lortie CJ. 2014. Formalized synthesis opportunities for ecology: Systematic reviews and meta-analyses. Oikos 123:897-902.
26. Mace GM. 2014. Whose conservation? Science 345:1558-1560.
27. Margules CR, Pressey RL. 2000. Systematic conservation planning. Nature 405:243-253.
28. McGeogh MA. 1998. The selection, testing and application of terrestrial insects as bioindicators. Biological Reviews 73:181-201.
29. McGill BJ. 2010. Towards a unification of unified theories of biodiversity. Ecology Letters 13:627-642.
30. Newbold T, et al. 2015. Global effects of land use on local terrestrial biodiversity. Nature 520:45-50.
31. Niebles J, Wang H, Fei-Fei L. 2008. Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision 79:299-318.
32. Noss RF. 1990. Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology 4:355-364.
33. Pollock LJ, Morris WK, Vesk PA. 2012. The role of functional traits in species distributions revealed through a hierarchical model. Ecography 35:716-725.
34. R Core Development Team. 2014. R: A language and environment for statistical computing, version 3.1.0. R Foundation for Statistical Computing, Vienna.
35. Rodrigues ASL, et al. 2011. Complete, accurate, mammalian phylogenies aid conservation planning, but not much. Philosophical Transactions of the Royal Society B-Biological Sciences 366:2652-2660.
36. Rusch T, Hofmarcher P, Hatzinger R, Hornik K. 2013. Model trees with topic model preprocessing: an approach for data journalism illustrated with the wikileaks Afghanistan war logs. The Annals of Applied Statistics 7:613-639.
37. Scheper J, Holzschuh A, Kuussaari M, Potts SG, Rundlöf M, Smith HG, Kleijn D. 2013. Environmental factors driving the effectiveness of European agri-environmental measures in mitigating pollinator loss - a meta-analysis. Ecology Letters 16:912-920.
38. Silva C, Ribeiro B. 2003. The importance of stop word removal on recall values in text categorization. Proceedings of the International Joint Conference on Neural Networks, 2003 3:1661-1666.
39. Sutherland WJ, et al. 2009. One hundred questions of importance to the conservation of global biological diversity. Conservation Biology 23:557-567.
40. Sutherland WJ, Fleishman E, Mascia MB, Pretty J, Rudd MA. 2011. Methods for collaboratively identifying research priorities and emerging issues in science and policy. Methods in Ecology and Evolution 2:238-247.
41. Valle D, Baiser B, Woodall CW, Chazdon R. 2014. Decomposing biodiversity data using the latent dirichlet allocation model, a probabilistic multivariate statistical method. Ecology Letters 17:1591-1601.
42. Van Noorden R, Maher B, Nuzzo R. 2014. The top 100 papers: nature explores the most-cited research of all time. Nature 514:550-553.
43. Wang D, Song C, Barabási A-L. 2013. Quantifying long-term scientific impact. Science 342:127-132.
44. Wang Y, Naumann U, Wright ST, Warton DI. 2012. Mvabund-an R package for model-based analysis of multivariate abundance data. Methods in Ecology and Evolution 3:471-474.
45. Weng J, Lim E-P, Jiang J, He Q. 2010. Twitterrank: finding topic-sensitive influential twitterers. Pages 261-270. Proceedings of the third ACM international conference on Web search and data mining. ACM, New York.
46. Westgate MJ, Barton PS, Lane PW, Lindenmayer DB. 2014. Global meta-analysis reveals low consistency of biodiversity congruence relationships. Nature Communications 5:3899.
47. Westgate MJ, Likens GE, Lindenmayer DB. 2013. Adaptive management of biological systems: a review. Biological Conservation 158:128-139.
48. Whittingham MJ. 2011. The future of agri-environment schemes: Biodiversity gains and ecosystem service delivery? Journal of Applied Ecology 48:509-513.