Monitoring woodland deer populations in the UK: an imprecise science

Abstract

1. The need to assess population size and change is central to any population monitoring programme. A range of monitoring techniques is available for deer, but few studies have addressed the performance of these techniques in terms of their accuracy and their power to detect population changes reliably. This study compares the performance of three commonly used techniques to monitor woodland deer populations in terms of their accuracy, precision and statistical power using field data and simulation modelling. 2. Faecal Standing Crop (FSC) was found to provide the most accurate estimates at all deer densities studied, but was outperformed by distance sampling using thermal imaging in terms of precision and statistical power. Faecal Accumulation Rate (FAR) performed worst in terms of precision and statistical power, and was inferior in terms of accuracy to FSC. The accuracy of FAR and distance sampling using thermal imaging was similar. 3. Annual surveying is not the best use of resources, since 10% per annum increases or decreases in a population could not be detected by any of the techniques within a 3‐year time period. Even with the best technique under the most favourable conditions of deer density simulated in this study, a population change could only be detected reliably once the population had increased by 33% or decreased by 27%. In the least favourable conditions, the respective figures were a 380% increase or extinction. 4. All the techniques studied are relatively poor at detecting population change, so their use in practical deer management needs to be treated with caution. The consequences of relying on similar techniques to detect population change for rare species could be severe.

The success of species reintroductions: a case study of red deer in Portugal two decades after reintroduction

“…In fact, we are aware of this bias, as the used value did not have a variance or standard error associated, meaning the results obtained did not took into account rate variations. The coefficient of variation (%) value was higher than the expected, but this can occur in areas where population density is not high (Smart et al 2004), such as in our study area. Several other studies regarding deer density have also recorded wide confidence intervals range, mainly where densities are smaller (Acevedo et al 2010;Marques et al 2001;Acevedo et al 2008).…”

Control of bovine tuberculosis in British livestock: there is no ‘silver bullet’

“…There are no reliable spatial data or models of deer abundance for most of Britain [70], but species richness of deer has been shown to correlate with deer abundance at a regional scale (http://www.woodlandforlife.net/wfl-woodbank/DisplayArticle.asp?ID=2333) and more species-rich deer communities can be considered as representing a higher risk for acting as a host for disease. Deer risk classes were, therefore, based on the number of deer species occurring in each 10-km square [52].…”

PELLET: An Excel®-Based Procedure for Estimating Deer Population Density Using the Pellet-Group Counting Method

“…Although this index is an indicator of habitat use [e.g., [71][72][73][74], the index (mean pellet-groups per sampling unit) is commonly converted into an estimate of density (number of deer per km 2 ) using the model or formula proposed by Eberhardt and Van Etten [43], which assumes that: 1) the average defecation rate is 12.7 fecal pellet-groups per individual per day, 2) the time that the pellet-groups have remained in the field is accurately known, and 3) all of the pellet-groups in the plot are correctly identified and have not been counted twice. The extent to which these assumptions are fulfilled or violated introduces corresponding levels of bias to the estimates produced [47,55]. The formula or model for the estimation of density is:…”

“…Rain and humidity affect the persistence of pellet-groups, which can also be a source of bias in field counts [27,[63][64][65][66][67]. Depending on the defecation rate used and the deposition time of the feces, different density estimates will be obtained at the same site [50,55]. This complicates comparisons of densities among regions, but above all, it seriously limits the application of the method for management purposes, since it is necessary to determine the number of deer at the site as accurately as possible [23].…”

“…It is an index that correlates positively with other population estimates [2,23,45,46]; however, it is not exempt from potential errors that can introduce bias into estimates [47][48][49][50]. As a consequence, different sampling and data analysis schemes have been tested in order to evaluate the efficiency, precision, and accuracy of the method [1,27,46,[51][52][53][54][55].…”