Monitoring large and complex wildlife aggregations with drones

Lyons, Mitchell; Brandis, Kate J.; Murray, Nick; Wilshire, J. P.; McCann, Justin; Callaghan, Corey T.

doi:10.32942/osf.io/w247h

Cited by 12 publications

(12 citation statements)

References 33 publications

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“…Drones are increasingly valuable for monitoring wildlife populations [24,33,68], including hippos. Our analyses show that drone data can provide accurate estimates of hippo pods, including their demographic structure.…”

Section: Resultsmentioning

confidence: 99%

Drone-based effective counting and ageing of hippopotamus (Hippopotamus amphibius) in the Okavango Delta in Botswana

Inman

Leggett²,

Chase

2019

Preprint

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Accurately estimating hippopotamus (Hippopotamus amphibius) numbers is difficult due to their aggressive nature, amphibious lifestyle, and habit of diving and surfacing. Traditionally, hippos are counted using aerial surveys and land/boat surveys. We compared estimates of numbers of hippos in a lagoon in the Okavango Delta, counted from land and video taken from a DJI Phantom 4 TM drone, testing for effectiveness at three heights (40 m, 80 m, and 120 m) and four times of day (early morning, late morning, early afternoon, and late afternoon). In addition, we determined effectiveness for differentiating age classes (juvenile, subadult, and adult), based on visual assessment and measurements from drone images, at different times and heights.Estimates in the pool averaged 9.18 (± 0.25SE, range 1 -14, n = 112 counts). Drone counts at 40 m produced the highest counts of hippos, 10.6% higher than land counts and drone counts at 80 m, and 17.6% higher than drone counts at 120 m. Fewer hippos were counted in the early morning, when the hippos were active and most likely submerged, compared to all other times of day, when they tended to rest in shallow water with their bodies exposed. We were able to assign age classes to similar numbers of hippos from land counts and counts at 40 m, although land counts were better at identifying juveniles and subadults. Early morning was the least effective time to age hippos given their active behaviour, increasingly problematic with increasing height. Use of a relatively low-cost drone provided a rigorous and repeatable method for estimating numbers and ages of hippos, but not in the early morning. Introduction1 Hippopotamus or hippo (Hippopotamus amphibius) are Vulnerable on the IUCN Red List 2 of Threatened Species, numbering 115,000-130,000 [1]. Habitat loss and hunting for meat and 3 ivory are driving declines [1], but much of the population data originates from aerial surveys, 4 which can be inaccurate [2-6]. Reliable and accurate spatial and temporal data on abundances 5 and demographics of hippo populations are essential for effective conservation management 6 [1,7,8] but hippos are inherently difficult to count because individuals regularly submerge and 7 surface throughout the day and have uniform appearance. They are also among the more 8 dangerous animals in Africa [9-11], limiting effectiveness of on-land and water methods of 9 counting [12]. 10 Hippos are usually surveyed from the air [9-12], but also from boats and land [9,13,14]; 11 each method has advantages and disadvantages. Aerial surveys cover large areas [4] but with 12 limited time to scan waterbodies and count hippos, given their speed. Also, aircraft noise may 13 cause hippos to submerge [19], contributing to underestimation [20]. Aerial surveys are costly 14 and logistically difficult, resulting in long intervals between surveys [21-24]. Slow, low-flying 15 microlight aircraft or helicopters capturing images may overcome some of these challenges [25] 16 but remain costly and potentially logistically difficult, ...

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Section: Resultsmentioning

confidence: 99%

Drone-based effective counting and ageing of hippopotamus (Hippopotamus amphibius) in the Okavango Delta in Botswana

Inman

Leggett²,

Chase

2019

Preprint

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“…Ensuring that we collect reliable count data will provide help taking appropriate management decisions and setting conservation priorities in this context. Alternative methods based on imagery have been gaining ground over the last decades (Akçay et al., 2020; Hodgson et al., 2018; Lyons et al., 2019), and more particularly with automated computer vision software (Chabot & Francis, 2016; Hollings et al, 2018). However, computer vision software may work under some particular conditions but they are generally biased and known to fail in several situations (Chabot & Francis, 2016; Hollings et al, 2018) even if considerable improvements are underway (González‐Villa & Cruz, 2019).…”

Section: Discussionmentioning

confidence: 99%

Reliability of animal counts and implications for the interpretation of trends

Vallecillo

Gauthier‐Clerc

Guillemain

et al. 2021

Ecology and Evolution

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Population time series analysis is an integral part of conservation biology in the current context of global changes. To quantify changes in population size, wildlife counts only provide estimates because of various sources of error. When unaccounted for, such errors can obscure important ecological patterns and reduce confidence in the derived trend. In the case of highly gregarious species, which are common in the animal kingdom, the estimation of group size is an important potential bias, which is characterized by high variance among observers. In this context, it is crucial to quantify the impact of observer changes, inherent to population monitoring, on i) the minimum length of population time series required to detect significant trends and ii) the accuracy (bias and precision) of the trend estimate. We acquired group size estimation error data by an experimental protocol where 24 experienced observers conducted counting simulation tests on group sizes. We used this empirical data to simulate observations over 25 years of a declining population distributed over 100 sites. Five scenarios of changes in observer identity over time and sites were tested for each of three simulated trends (true population size evolving according to deterministic models parameterized with declines of 1.1%, 3.9% or 7.4% per year that justify respectively a “declining,” “vulnerable” or “endangered” population under IUCN criteria). We found that under realistic field conditions observers detected the accurate value of the population trend in only 1.3% of the cases. Our results also show that trend estimates are similar if many observers are spatially distributed among the different sites, or if one single observer counts all sites. However, successive changes in observer identity over time lead to a clear decrease in the ability to reliably estimate a given population trend, and an increase in the number of years of monitoring required to adequately detect the trend. Minimizing temporal changes of observers improve the quality of count data and help taking appropriate management decisions and setting conservation priorities. The same occurs when increasing the number of observers spread over 100 sites. If the population surveyed is composed of few sites, then it is preferable to perform the survey by one observer. In this context, it is important to reconsider how we use estimated population trend values and potentially to scale our decisions according to the direction and duration of estimated trends, instead of setting too precise threshold values before action.

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“…comm., 29 March). However, costs are expected to change as technological advances make sophisticated monitoring methods, such as, sensors and drones, more accessible and applicable across many indicators (Lyons et al., 2019). How likely is the indicator to detect the management triggers identified for each threat? …”

Section: Methodsmentioning

confidence: 99%