Local frequency as a key to interpreting species occurrence data when recording effort is not known

Local frequency as a key to interpreting species occurrence data when recording effort is not known Summary 1. Data on the occurrence of species in grid cells are collected by biological recording schemes, typically with the intention of publishing an atlas. Interpretation of such data is often hampered by the lack of information on the effort that went into collecting them. This is the ‘recorder effort problem’. 2. One measure of recorder effort is the proportion of a suite of common species (‘benchmark species’) found at a given location and time. Benchmark species have in the past been taken as a uniform set across a territory. However, if records are available from a neighbourhood surrounding a given location, then a local set benchmark species can be defined by pooling records from the neighbourhood and selecting the commonest species in the pooled set. 3. Neighbourhoods differ in species richness, so that the list of species that ‘ought’ to be found in one location may be longer than that for another. If the richness of a neighbourhood can be estimated, then a suite of benchmark species can be standardized to be the commonest of a fixed proportion of the total expected for the neighbourhood. Recording effort is then defined as the proportion of benchmark species that were found. 4. A method of estimating species richness is proposed here, based on the local frequencies fj of species in neighbouring grid cells. Species discovery is modelled as a Poisson process. It is argued that when a neighbourhood is well sampled, the frequency‐weighted mean frequency /∑fj of species in the neighbourhood will assume a standard value. 5. The method was applied to a data set of 2 000 000 records detailing the occurrence of bryophytes in 3695 out of the total 3854 hectads (10‐km squares) in Great Britain, Ireland, the Isle of Man and the Channel Islands. 6. Three main applications are outlined: estimation of recording effort, scanning data for unexpected presences or absences and measurement of species trends over time. An explicit statistical model was used to estimate trends, modelling the probability of species j being found at location i and time t as the outcome of Poisson process with intensity Qijtxjt, where xjt is a time factor for species j, and Qijt depends on recording effort at location i and time t and on the time‐independent probability of species j being found in hectad i. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Methods in Ecology and Evolution Wiley

Local frequency as a key to interpreting species occurrence data when recording effort is not known

Methods in Ecology and Evolution, Volume 3 (1) – Feb 1, 2012

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Publisher
Wiley
Copyright
© 2011 The Author. Methods in Ecology and Evolution © 2011 British Ecological Society
ISSN
2041-210X
eISSN
2041-210X
DOI
10.1111/j.2041-210X.2011.00146.x
Publisher site
See Article on Publisher Site

Abstract

Summary 1. Data on the occurrence of species in grid cells are collected by biological recording schemes, typically with the intention of publishing an atlas. Interpretation of such data is often hampered by the lack of information on the effort that went into collecting them. This is the ‘recorder effort problem’. 2. One measure of recorder effort is the proportion of a suite of common species (‘benchmark species’) found at a given location and time. Benchmark species have in the past been taken as a uniform set across a territory. However, if records are available from a neighbourhood surrounding a given location, then a local set benchmark species can be defined by pooling records from the neighbourhood and selecting the commonest species in the pooled set. 3. Neighbourhoods differ in species richness, so that the list of species that ‘ought’ to be found in one location may be longer than that for another. If the richness of a neighbourhood can be estimated, then a suite of benchmark species can be standardized to be the commonest of a fixed proportion of the total expected for the neighbourhood. Recording effort is then defined as the proportion of benchmark species that were found. 4. A method of estimating species richness is proposed here, based on the local frequencies fj of species in neighbouring grid cells. Species discovery is modelled as a Poisson process. It is argued that when a neighbourhood is well sampled, the frequency‐weighted mean frequency /∑fj of species in the neighbourhood will assume a standard value. 5. The method was applied to a data set of 2 000 000 records detailing the occurrence of bryophytes in 3695 out of the total 3854 hectads (10‐km squares) in Great Britain, Ireland, the Isle of Man and the Channel Islands. 6. Three main applications are outlined: estimation of recording effort, scanning data for unexpected presences or absences and measurement of species trends over time. An explicit statistical model was used to estimate trends, modelling the probability of species j being found at location i and time t as the outcome of Poisson process with intensity Qijtxjt, where xjt is a time factor for species j, and Qijt depends on recording effort at location i and time t and on the time‐independent probability of species j being found in hectad i.

Journal

Methods in Ecology and EvolutionWiley

Published: Feb 1, 2012

References

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