How long should we ignore imperfect detection of species in the marine environment when modelling their distribution

The application of the ‘ecosystem approach’ to marine conservation management demands knowledge of the distribution patterns of the target species or communities. This information is commonly obtained from species distribution models (SDMs). This article explores an important but rarely acknowledged assumption in these models: almost all species may be present, but simply not detected by the particular survey method. However, nearly all of these SDM approaches neglect this important characteristic. This leads to the violation of a fundamental assumption of these models, which presuppose the detection of a species is equal to one (i.e. at each survey locality, a species is perfectly detected). In this article, the concept of imperfect detection is discussed, how it potentially influences the prediction of species' distributions is examined, and some statistical methods that could be used to incorporate the detection probability of species in estimates of their distribution are suggested. The approaches discussed here could improve the collection and interpretation of marine biological survey data and provide a coherent way to incorporate detection probability estimates in the modelling of species distributions. This will ultimately lead to an unbiased and more rigorous understanding of the distribution of species in the marine environment.

[1]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[2]  M. Araújo,et al.  Presence-absence versus presence-only modelling methods for predicting bird habitat suitability , 2004 .

[3]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[4]  Terry Walshe,et al.  Designing occupancy surveys and interpreting non‐detection when observations are imperfect , 2012 .

[5]  D. MacKenzie Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence , 2005 .

[6]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[7]  James D. Nichols,et al.  Local extinction and turnover rates at the edge and interior of species' ranges , 2003 .

[8]  William L. Kendall,et al.  ROBUSTNESS OF CLOSED CAPTURE-RECAPTURE METHODS TO VIOLATIONS OF THE CLOSURE ASSUMPTION , 1999 .

[9]  M. Kulbicki,et al.  Factors affecting the detection distances of reef fish: implications for visual counts , 2011 .

[10]  J. Andrew Royle,et al.  Modelling occurrence and abundance of species when detection is imperfect , 2005 .

[11]  Steven J. Phillips,et al.  The art of modelling range‐shifting species , 2010 .

[12]  K. Pollock,et al.  ESTIMATING SITE OCCUPANCY AND SPECIES DETECTION PROBABILITY PARAMETERS FOR TERRESTRIAL SALAMANDERS , 2004 .

[13]  Hannah M Murphy,et al.  Observational methods used in marine spatial monitoring of fishes and associated habitats: a review , 2010 .

[14]  D. Mandel,et al.  The inverse fallacy: An account of deviations from Bayes’s theorem and the additivity principle , 2002, Memory & cognition.

[15]  Pedro P. Olea,et al.  Spatially explicit estimation of occupancy, detection probability and survey effort needed to inform conservation planning , 2011 .

[16]  M. Conroy,et al.  Accounting for detectability in reef-fish biodiversity estimates , 2008 .

[17]  M. Kéry,et al.  Predicting species distributions from checklist data using site‐occupancy models , 2010 .

[18]  R. Swihart,et al.  Improving selection of indicator species when detection is imperfect , 2012 .

[19]  J. Nichols,et al.  Investigating species co-occurrence patterns when species are detected imperfectly , 2004 .

[20]  J. Andrew Royle,et al.  ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE , 2002, Ecology.

[21]  H. Possingham,et al.  IMPROVING PRECISION AND REDUCING BIAS IN BIOLOGICAL SURVEYS: ESTIMATING FALSE‐NEGATIVE ERROR RATES , 2003 .

[22]  J. Andrew Royle,et al.  Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities , 2008 .

[23]  Marc Kéry,et al.  Towards the modelling of true species distributions , 2011 .

[24]  Jason M. Evans,et al.  Does accounting for imperfect detection improve species distribution models , 2011 .

[25]  Robert P. Mueller,et al.  Video and acoustic camera techniques for studying fish under ice: a review and comparison , 2006, Reviews in Fish Biology and Fisheries.

[26]  J. Sarmiento,et al.  Projecting global marine biodiversity impacts under climate change scenarios , 2009 .

[27]  J. Andrew Royle,et al.  Species richness and occupancy estimation in communities subject to temporary emigration. , 2009, Ecology.

[28]  E. Harvey,et al.  Habitat suitability for marine fishes using presence-only modelling and multibeam sonar , 2010 .

[29]  J. Franklin Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients , 1995 .

[30]  JACOB T. JACKSON,et al.  Inferring Absence of Houston Toads Given Imperfect Detection Probabilities , 2006 .

[31]  Trevor Hastie,et al.  Making better biogeographical predictions of species’ distributions , 2006 .

[32]  Hugh P. Possingham,et al.  Improving the efficiency of wildlife monitoring by estimating detectability: a case study of foxes (Vulpes vulpes) on the Eyre Peninsula, South Australia , 2005 .

[33]  Larissa L. Bailey,et al.  Modeling co-occurrence of northern spotted and barred owls: Accounting for detection probability differences , 2009 .

[34]  Steven J. Phillips,et al.  Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. , 2009, Ecological applications : a publication of the Ecological Society of America.

[35]  E. Harvey,et al.  Remotely sensed hydroacoustics and observation data for predicting fish habitat suitability , 2011 .

[36]  Rebecca E. Ross,et al.  Use of predictive habitat modelling to assess the distribution and extent of the current protection of ‘listed’ deep‐sea habitats , 2013 .

[37]  James D. Nichols,et al.  Monitoring of biological diversity in space and time , 2001 .

[38]  M. Conroy,et al.  Detection heterogeneity in underwater visual‐census data , 2008 .

[39]  J. Andrew Royle,et al.  Presence‐only modelling using MAXENT: when can we trust the inferences? , 2013 .

[40]  Brendan A. Wintle,et al.  ESTIMATING AND DEALING WITH DETECTABILITY IN OCCUPANCY SURVEYS FOR FOREST OWLS AND ARBOREAL MARSUPIALS , 2005 .

[41]  E. Harvey,et al.  Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes? , 2012, PloS one.

[42]  Robert M Dorazio,et al.  Predicting the Geographic Distribution of a Species from Presence‐Only Data Subject to Detection Errors , 2012, Biometrics.