Should we account for imperfect detection when evaluating patterns in fish abundance?

Our recent paper sheds light on the issue of imperfect detection when evaluating patterns in fish abundance. We synthesize the information from numerous studies to develop a realistic expectation of when and how the detection probability should vary when sampling fish. Our discussion is presented in the context of evaluating lotic fish responses to flow-related aspects of the environment, but our conclusions are broadly applicable to any fish sampling, whether lotic, lentic or marine.  Link to paper



There is an ongoing debate in the ecological literature about when and how to account for scientists’ imperfect ability to observe and detect research organisms during field work. The participants in this debate tend to fall into two camps: those that claim that ignoring the detection process in ecological studies poses unacceptable risk of incorrect conclusions and promote the use of statistical modeling to account for the detection process (e.g. Yoccoz et al. 2001, Guillera-Arroita et al. 2014, Hayward and Marlow 2014), and those that claim that the use of indices of abundance that ignore the detection process is often equally acceptable (e.g. Johnson 2008, Welsh et al. 2013, Nimmo et al. 2015). Although both sides of this argument can have merit under different circumstances, the debate has become quite heated with many scientific papers promoting one perspective with responses promoting the opposite (e.g. Welsh et al.2013, Guillera-Arroita et al. 2014, Hayward and Marlow 2014, Nimmo et al. 2015). The debate has extended across taxonomic groups, including birds (Johnson 2008), reptiles and amphibians (Durso and Seigel 2015), fish (Monk 2013, Gwinn et al. 2016), mammals (Hayward and Marlow 2014, Nimmo et al. 2015), and plants (Chen et al. 2009, Chen et al. 2013).

So, what exactly is the problem with using indices of abundance? The issue is that an index of abundance needs to reflect variation in abundance, but if the sampling efficiency (i.e. detection detection_tableprobability) varies across experimental units (e.g. time or space), the index may reflect this variation in detection instead. The results of this variation in sampling efficiency may be as innocuous as adding noise to the catch/count data, but it could also cause spurious patterns in the index leading to incorrect conclusions about patterns in abundance (e.g. Archaux et al. 2012, Hangsleben et al. 2013).

For fish, imperfect detection is particularly important to consider because sampling efficiency of fish tends to be low and variable, with many influencing factors. The table to the left can be found in our recent paper (Gwinn et al. 2016) and gives an overview of the many factors that detection of fish can vary across.  Ultimately, whether imperfect detection is a “problem” is dependent mainly on three interrelated factors, i.e., how the data will be used (Durso and Seigel 2015), how variable detection probability is relative to the change in the population/community desired to detect (Johnson 2008), and how detection varies with factors that also influence abundance (Gwinn et al. 2016). We hope that our paper will help fish researchers make an informed decision as to whether accounting for detection probability is important within the context of their research question and methods.


Archaux F., Henry P.Y. & Gimenez O. (2012) When can we ignore the problem of imperfect detection in comparative studies? Methods in Ecology and Evolution, 3, 188-194.

Beesly L.S., Gwinn D.C., Price A., King A.J., Gawne B., Koehn J.D. & Nielsen D.L. (2014) Juvenile fish response to wetland inundation: how antecedent conditions can inform environmental flow policies for native fish. Journal of Applied Ecology, 51, 1613-1621.

Chen G., Kery M., Zhang J. & Ma K. (2009) Factors affecting detection probability in plant distribution studies. Journal of Ecology, 97, 1383-1389.

Chen G., Kery M., Plattner M., Ma K. & Gardner B. (2013) Imperfect detection is the rule rather than the exception in plant distribution studies. Journal of Ecology, 101, 183-191.

Durso A.M. & Seigel R.A. (2015) A snake in the hand is worth 10,000 in the bush. Journal of Herpetology, 49, 503-506.

Guillera-Arroita G., Lahoz-Monfort J.J., MacKenzie D.I., Wintle B.A. & McCarthy M.A. (2014) Ignoring imperfect detection in biological surveys is dangerous: a response to ‘fitting and interpreting occupancy models’. PLoS ONE, 9, e99571.

Gwinn D.C., Beesley, L.S., Close P., Gawne B. & Davies P.M. (2016) Imperfect detection and the determination of environmental flows for fish: challenges, implications and solutions. Freshwater Biology, 61, 172-180.

Hangsleben, M.A., Allen, M.S., and Gwinn, D.C. (2013) Evaluation of electrofishing catch per unit effort for indexing fish abundance in Florida lakes. Transactions of the American Fisheries Society, 142, 247-256.

Hayward M.W. & Marlow N. (2014) Will dingoes really conserve wildlife and can our methods tell? Journal of Applied Ecology, 51, 835-838.

Johnson D.H. (2008) In defense of indices: the case of bird surveys. The Journal of Wildlife Management, 72, 857-868.

Monk J. (2013) How long should we ignore imperfect detection of species in the marine environment when modelling their distribution. Fish and Fisheries, 15, 352-358.

Nimmo, D., Watson, S., Forsyth, D. & Bradshaw, C. (2015) Dingoes can help conserve wildlife and our methods can tell. Journal of Applied Ecology, doi: 10.1111/1365-2664.12369

Welsh A.H., Lindenmayer D.B. & Donnelly C.F. (2013) Fitting and interpreting occupancy models. PLos ONE, 8, e52015.

Yoccoz N.G., Nichols J.D. & Boulinier T. (2001) Monitoringf of biological diversity in space and time. Trends in Ecology and Evolution, 16, 446-453.


About dangwinn

I am a quantitative ecologist interested in patterns in biodiversity and natural resource management.
This entry was posted in Ecological statistics, Ecology, Experimental design, fisheries management, population modeling, recreational fisheries, Uncategorized. Bookmark the permalink.

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