Header

UZH-Logo

Maintenance Infos

Imperfect detection and its consequences for monitoring for conservation


Kéry, M; Schmidt, B R (2008). Imperfect detection and its consequences for monitoring for conservation. Community Ecology, 9(2):207-216.

Abstract

Biodiversity monitoring is important to identify conservation needs and test the efficacy of management actions. Variants of “abundance” (N) are among the most widely monitored quantities, e.g., (true) abundance, number of occupied sites (distribution, occupancy) or species richness.We propose a sampling-based view of monitoring that clearly acknowledges two sampling processes involved when monitoring N. First, measurements from the surveyed sample area are generalized to a larger area, hence the importance of a probability sample. Second, even within sampled areas only a sample of units (individuals, occupied sites, species) is counted owing to imperfect detectability p. If p < 1, counts are random variables and their expectation
E(n) is related to N via the relationship E(n) ÿ*p. Whenever p < 1, counts vary even under identical conditions and underestimate N, and patterns in counts confound patterns in N with those in p. In addition, part of the population N may be unavailable for detection, e.g., temporarily outside the sampled quadrat, underground or for another reason not exposed to sampling; hence a more general way of describing a count is E(n) ÿ*a*p, where a is availability probability and p detection, given availability. We give two examples of monitoring schemes that highlight the importance of explicitly accounting for availability and detectability. In the Swiss reptile Red List update, the widespread and abundant slow worm (Anguis fragilis) was recorded in only 22.1% of all sampled quadrats. Only an analysis that accounted for both availability and detectability gave realistic estimates of the species’ distribution. Among 128 bird species monitored in the Swiss breeding bird survey, de
tection in occupied 1 km quadrats averaged only 64% and varied tremendously by species (3–99 %); hence observed
distributions greatly underestimated range sizes and should not be compared among species.We believe that monitoring design and analyses should properly account for these two sampling processes to enable valid inferences about biodiversity. We argue
for a more rigorous approach to both monitoring design and analysis to obtain the best possible information about the state of nature. An explicit recognition of, and proper accounting for, the two sampling processes involved in most monitoring programs will go a long way towards this goal

Abstract

Biodiversity monitoring is important to identify conservation needs and test the efficacy of management actions. Variants of “abundance” (N) are among the most widely monitored quantities, e.g., (true) abundance, number of occupied sites (distribution, occupancy) or species richness.We propose a sampling-based view of monitoring that clearly acknowledges two sampling processes involved when monitoring N. First, measurements from the surveyed sample area are generalized to a larger area, hence the importance of a probability sample. Second, even within sampled areas only a sample of units (individuals, occupied sites, species) is counted owing to imperfect detectability p. If p < 1, counts are random variables and their expectation
E(n) is related to N via the relationship E(n) ÿ*p. Whenever p < 1, counts vary even under identical conditions and underestimate N, and patterns in counts confound patterns in N with those in p. In addition, part of the population N may be unavailable for detection, e.g., temporarily outside the sampled quadrat, underground or for another reason not exposed to sampling; hence a more general way of describing a count is E(n) ÿ*a*p, where a is availability probability and p detection, given availability. We give two examples of monitoring schemes that highlight the importance of explicitly accounting for availability and detectability. In the Swiss reptile Red List update, the widespread and abundant slow worm (Anguis fragilis) was recorded in only 22.1% of all sampled quadrats. Only an analysis that accounted for both availability and detectability gave realistic estimates of the species’ distribution. Among 128 bird species monitored in the Swiss breeding bird survey, de
tection in occupied 1 km quadrats averaged only 64% and varied tremendously by species (3–99 %); hence observed
distributions greatly underestimated range sizes and should not be compared among species.We believe that monitoring design and analyses should properly account for these two sampling processes to enable valid inferences about biodiversity. We argue
for a more rigorous approach to both monitoring design and analysis to obtain the best possible information about the state of nature. An explicit recognition of, and proper accounting for, the two sampling processes involved in most monitoring programs will go a long way towards this goal

Statistics

Citations

107 citations in Web of Science®
112 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

212 downloads since deposited on 21 Jan 2009
27 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Uncontrolled Keywords:Abundance, Availability, Biodiversity, Detectability, Distribution, Inventory, Monitoring, Occupancy, Sampling, Survey
Language:English
Date:2008
Deposited On:21 Jan 2009 17:07
Last Modified:05 Apr 2016 12:44
Publisher:Akadémiai Kiadó
ISSN:1585-8553
Publisher DOI:https://doi.org/10.1556/ComEc.9.2008.2.10

Download

Preview Icon on Download
Preview
Content: Published Version
Filetype: PDF
Size: 1MB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations