Methodology matters when estimating deer abundance: a global systematic review and recommendations for improvements

Abstract

Deer (Cervidae) are key components of many ecosystems and estimating deer abundance or density is important to understanding these roles. Many field methods have been used to estimate deer abundance and density, but the factors determining where, when, and why a method was used, and its usefulness, have not been investigated. We systematically reviewed journal articles published during 2004–2018 to evaluate spatio‐temporal trends in study objectives, methodologies, and deer abundance and density estimates, and determine how they varied with biophysical and anthropogenic attributes. We also reviewed the precision and bias of deer abundance estimation methods. We found 3,870 deer abundance and density estimates. Most estimates (58%) were for white‐tailed deer (Odocoileus virginianus), red deer (Cervus elaphus), and roe deer (Capreolus capreolus). The 6 key methods used to estimate abundance and density were pedestrian sign (track or fecal) counts, pedestrian direct counts, vehicular direct counts, aerial direct counts, motion‐sensitive cameras, and harvest data. There were regional differences in the use of these methods, but a general pattern was a temporal shift from using harvest data, pedestrian direct counts, and aerial direct counts to using pedestrian sign counts and motion‐sensitive cameras. Only 32% of estimates were accompanied by a measure of precision. The most precise estimates were from vehicular spotlight counts and from capture–recapture analysis of images from motion‐sensitive cameras. For aerial direct counts, capture–recapture methods provided the most precise estimates. Bias was robustly assessed in only 16 studies. Most abundance estimates were negatively biased, but capture–recapture methods were the least biased. The usefulness of deer abundance and density estimates would be substantially improved by 1) reporting key methodological details, 2) robustly assessing bias, 3) reporting the precision of estimates, 4) using methods that increase and estimate detection probability, and 5) staying up to date on new methods. The automation of image analysis using machine learning should increase the accuracy and precision of abundance estimates from direct aerial counts (visible and thermal infrared, including from unmanned aerial vehicles [drones]) and motion‐sensitive cameras, and substantially reduce the time and cost burdens of manual image analysis.

Estimating deer density and abundance using spatial mark–resight models with camera trap data

“…It is particularly valuable when target populations inhabit densely vegetated terrain that is difficult to survey using visual counts of animals, as is often the case for sambar deer ( Leslie 2011 ). A global systematic review of over 5,000 deer abundance and density estimates concluded that mark–recapture surveys using camera traps provided greater precision, on average (mean CV( ) = 0.39), than other survey methods for which sufficient data were available ( Forsyth et al 2022 ). Eleven of our 12 surveys provided greater precision than this average, with seven surveys attaining CV( ) ≤ 0.25.…”

“…The relative use of each method has varied regionally and temporally. Counts of deer or their sign from terrestrial or aerial transects have been most common in recent years ( Forsyth et al 2022 ). However, the use of camera traps for deer surveys has increased since the 2000s, particularly in North America ( Forsyth et al 2022 ).…”

“…Counts of deer or their sign from terrestrial or aerial transects have been most common in recent years ( Forsyth et al 2022 ). However, the use of camera traps for deer surveys has increased since the 2000s, particularly in North America ( Forsyth et al 2022 ). Camera trap surveys are particularly well-suited to habitats with dense canopy cover in which the detection probability of methods such as terrestrial or aerial transects are typically too low to be useful ( Gardner et al 2019 ).…”

“…A survey aiming to detect a small change in or , for example, will need greater precision than one aiming to detect a larger change. A recent review reported that most (72%) published deer abundance and density estimates did not report precision, and only 26% of those that did reported a CV ≤ 0.25 ( Forsyth et al 2022 ).…”

Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance

“…In addition to the PO and PA data introduced above, we gathered hunting culling returns from the National Park & Wildlife Service (NPWS, https://www.npws.ie/), responsible for issuing hunting licences. Culling returns are an alternative source of data (Milner et al 2006, Forsyth et al 2022, and we retained this dataset to validate the ISDMs we built by integrating PO and PA data.…”