publication . Article . 2017

Characterisation of false-positive observations in botanical surveys

Quentin J. Groom; Sarah J. Whild;
Open Access English
  • Published: 01 May 2017 Journal: PeerJ, volume 5 (issn: 2167-8359, eissn: 2167-8359, Copyright policy)
  • Publisher: PeerJ Inc.
Abstract
Errors in botanical surveying are a common problem. The presence of a species is easily overlooked, leading to false-absences; while misidentifications and other mistakes lead to false-positive observations. While it is common knowledge that these errors occur, there are few data that can be used to quantify and describe these errors. Here we characterise false-positive errors for a controlled set of surveys conducted as part of a field identification test of botanical skill. Surveys were conducted at sites with a verified list of vascular plant species. The candidates were asked to list all the species they could identify in a defined botanically rich area. The...
Subjects
mesheuropmc: nutritional and metabolic diseasesnervous system diseases
free text keywords: Shropshire, Type 1 errors, Biodiversity, Habitat survey, Medicine, Field identification, R, Ecology, Specificity, Plant Science, Sensitivity, False-presence, Phylogenetic signal, Rarity
Related Organizations
Funded by
EC| EU BON
Project
EU BON
EU BON: Building the European Biodiversity Observation Network
  • Funder: European Commission (EC)
  • Project Code: 308454
  • Funding stream: FP7 | SP1 | ENV
31 references, page 1 of 3

Bird, TJ, Bates, AE, Lefcheck, JS, Hill, NA, Thomson, RJ, Edgar, GJ, Stuart-Smith, RD, Wotherspoon, S, Krkosek, M, Stuart-Smith, JF, Pecl, GT, Barrett, N, Frusher, S. Statistical solutions for error and bias in global citizen science datasets. Biological Conservation. 2014; 173: 144-154 [DOI]

Chapman, AD. Principles of data quality, version 1.0. Report for the Global Biodiversity Information Facility, Copenhagen. 2005

Chen, G, Kéry, M, Plattner, M, Ma, K, Gardner, B. Imperfect detection is the rule rather than the exception in plant distribution studies. Journal of Ecology. 2013; 101: 183-191 [DOI]

Chen, G, Kery, M, Zhang, J, Ma, K. Factors affecting detection probability in plant distribution studies. Journal of Ecology. 2009; 97: 1383-1389 [DOI]

Conn, PB, McClintock, BT, Cameron, MF, Johnson, DS, Moreland, EE, Boveng, PL. Accommodating species identification errors in transect surveys. Ecology. 2013; 94: 2607-2618 [PubMed] [DOI]

Dorazio, RM. Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Global Ecology and Biogeography. 2014; 23: 1472-1484 [DOI]

Durka, W, Michalski, SG. Daphne: a dated phylogeny of a large European flora for phylogenetically informed ecological analyses. Ecology. 2012; 93: 2297 [DOI]

Ellis, R. Jizz and the joy of pattern recognition: virtuosity, discipline and the agency of insight in UK naturalists’ arts of seeing. Social Studies of Science. 2011; 41: 769-790 [OpenAIRE] [PubMed] [DOI]

Elphick, CS. How you count counts: the importance of methods research in applied ecology. Journal of Applied Ecology. 2008; 45: 1313-1320 [DOI]

Farmer, RG, Leonard, ML, Horn, AG. Observer effects and avian-call-count survey quality: rare-species biases and overconfidence. The Auk. 2012; 129: 76-86 [DOI]

Fielding, AH, Bell, JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation. 1997; 24 (01): 38-49 [DOI]

Fritz, SA, Purvis, A. Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conservation Biology. 2010; 24: 1042-1051 [PubMed] [DOI]

Groom, QJ. Estimation of vascular plant occupancy and its change using kriging. New Journal of Botany. 2013; 3: 33-46 [DOI]

MacKenzie, DI, Royle, JA. Designing occupancy studies: general advice and allocating survey effort. Journal of Applied Ecology. 2005; 42: 1105-1114 [DOI]

Manel, S, Dias, JM, Buckton, ST, Ormerod, SJ. Alternative methods for predicting species distribution: an illustration with Himalayan river birds. Journal of Applied Ecology. 1999; 36: 734-747 [DOI]

31 references, page 1 of 3
Abstract
Errors in botanical surveying are a common problem. The presence of a species is easily overlooked, leading to false-absences; while misidentifications and other mistakes lead to false-positive observations. While it is common knowledge that these errors occur, there are few data that can be used to quantify and describe these errors. Here we characterise false-positive errors for a controlled set of surveys conducted as part of a field identification test of botanical skill. Surveys were conducted at sites with a verified list of vascular plant species. The candidates were asked to list all the species they could identify in a defined botanically rich area. The...
Subjects
mesheuropmc: nutritional and metabolic diseasesnervous system diseases
free text keywords: Shropshire, Type 1 errors, Biodiversity, Habitat survey, Medicine, Field identification, R, Ecology, Specificity, Plant Science, Sensitivity, False-presence, Phylogenetic signal, Rarity
Related Organizations
Funded by
EC| EU BON
Project
EU BON
EU BON: Building the European Biodiversity Observation Network
  • Funder: European Commission (EC)
  • Project Code: 308454
  • Funding stream: FP7 | SP1 | ENV
31 references, page 1 of 3

Bird, TJ, Bates, AE, Lefcheck, JS, Hill, NA, Thomson, RJ, Edgar, GJ, Stuart-Smith, RD, Wotherspoon, S, Krkosek, M, Stuart-Smith, JF, Pecl, GT, Barrett, N, Frusher, S. Statistical solutions for error and bias in global citizen science datasets. Biological Conservation. 2014; 173: 144-154 [DOI]

Chapman, AD. Principles of data quality, version 1.0. Report for the Global Biodiversity Information Facility, Copenhagen. 2005

Chen, G, Kéry, M, Plattner, M, Ma, K, Gardner, B. Imperfect detection is the rule rather than the exception in plant distribution studies. Journal of Ecology. 2013; 101: 183-191 [DOI]

Chen, G, Kery, M, Zhang, J, Ma, K. Factors affecting detection probability in plant distribution studies. Journal of Ecology. 2009; 97: 1383-1389 [DOI]

Conn, PB, McClintock, BT, Cameron, MF, Johnson, DS, Moreland, EE, Boveng, PL. Accommodating species identification errors in transect surveys. Ecology. 2013; 94: 2607-2618 [PubMed] [DOI]

Dorazio, RM. Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Global Ecology and Biogeography. 2014; 23: 1472-1484 [DOI]

Durka, W, Michalski, SG. Daphne: a dated phylogeny of a large European flora for phylogenetically informed ecological analyses. Ecology. 2012; 93: 2297 [DOI]

Ellis, R. Jizz and the joy of pattern recognition: virtuosity, discipline and the agency of insight in UK naturalists’ arts of seeing. Social Studies of Science. 2011; 41: 769-790 [OpenAIRE] [PubMed] [DOI]

Elphick, CS. How you count counts: the importance of methods research in applied ecology. Journal of Applied Ecology. 2008; 45: 1313-1320 [DOI]

Farmer, RG, Leonard, ML, Horn, AG. Observer effects and avian-call-count survey quality: rare-species biases and overconfidence. The Auk. 2012; 129: 76-86 [DOI]

Fielding, AH, Bell, JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation. 1997; 24 (01): 38-49 [DOI]

Fritz, SA, Purvis, A. Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conservation Biology. 2010; 24: 1042-1051 [PubMed] [DOI]

Groom, QJ. Estimation of vascular plant occupancy and its change using kriging. New Journal of Botany. 2013; 3: 33-46 [DOI]

MacKenzie, DI, Royle, JA. Designing occupancy studies: general advice and allocating survey effort. Journal of Applied Ecology. 2005; 42: 1105-1114 [DOI]

Manel, S, Dias, JM, Buckton, ST, Ormerod, SJ. Alternative methods for predicting species distribution: an illustration with Himalayan river birds. Journal of Applied Ecology. 1999; 36: 734-747 [DOI]

31 references, page 1 of 3
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publication . Article . 2017

Characterisation of false-positive observations in botanical surveys

Quentin J. Groom; Sarah J. Whild;