Abundance estimation for line transect sampling: A comparison of distance sampling and spatial capture-recapture models

Crum, Nathan J.; Neyman, Lisa C.; Gowan, Timothy A.

doi:10.1371/journal.pone.0252231

Cited by 9 publications

(9 citation statements)

References 52 publications

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“…Detectability can be influenced by population density, distribution characteristics (i.e. open or closed population; Crum et al., 2021; Dambly et al., 2021; Westcott et al., 2012), landscape heterogeneity (Barata et al., 2017; Nuno et al., 2013; Rhodes & Jonzén, 2011), observer experience (Barata et al., 2017; Vallecillo et al., 2021) and weather conditions during monitoring (Morant et al., 2020). While we did not directly assess detectability bias and its effects on accuracy of counts in our study system, our analysis (Appendix SA) revealed that WMU‐level variability in population density and dense coniferous forest cover contributed to greater among‐plot variability and thus more imprecise population estimates.…”

Section: Discussionmentioning

confidence: 99%

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When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

Priadka

Brown

Fedy

et al. 2022

Ecol Sol and Evidence

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Monitoring widely distributed species on a budget presents challenges for the spatio‐temporal allocation of survey effort. When there are multiple discrete units to monitor, survey alternatives such as model‐based estimates can be useful to fill information gaps but may not reliably reflect biological complexity and change. The spatio‐temporal allocation of survey effort that minimizes uncertainty for the greatest number of units within a budget can help to ensure monitoring is optimized. We used aerial survey‐based population estimates of moose (Alces alces) across 30 Wildlife Management Units (WMUs) in Ontario, Canada to parameterize simulated populations and test the performance of different monitoring scenarios in capturing WMU‐specific annual variation and trends. Firstly, we tested scenarios that prioritized conducting a survey for a unit based on one of three management criteria: population state, population uncertainty or number of years between surveys. Also incorporated in the decision framework were WMU‐specific costs and annual budget constraints. Secondly, we tested how using model‐based estimates to fill information gaps improved population and trend estimates. Lastly, we assessed how the utility (based on minimizing population uncertainty) of using a model‐based estimate rather than conducting a survey was impacted by population density, severity of environmental stressors and years since the last survey. Interval‐based monitoring that minimized the number of years between surveys captured accurate trends for the highest number of WMUs, but annual variation was poorly captured regardless of management criteria prioritized. Using model‐based estimates to fill information gaps improved trend estimation. Further, the utility of conducting a survey increased with time since the last survey and was greater for populations with low densities when the severity of environmental stressors was high, while being greater for populations with high densities when environmental severity was low. Overall, the utility of aerial survey monitoring was strongly associated with WMU‐specific monitoring precision and the predictive power of model‐based estimates. If long‐term trends are evident, then there is greater value in using alternatives such as model‐based predictions to replace surveys, but model‐based estimates may be a poor substitute when there is strong annual variation and when using a simple model.

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Section: Discussionmentioning

confidence: 99%

“…Detectability can be influenced by population density, distribution characteristics (i.e. open or closed population;Crum et al, 2021;…”

mentioning

confidence: 99%

When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

Priadka

Brown

Fedy

et al. 2022

Ecol Sol and Evidence

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“…When the conservation goal is to assess abundance in an area at a specific time, line transect surveys may be a cost-effective choice. However, if one’s goal is to estimate the number of animals in an area over a longer period, CR methods could be more appropriate but have cost implications that may exceed those of conducting a line transect survey (Crum, Neyman, & Gowan, 2021; Hammond et al, 2021). Despite differences in ecological inference, DS and CR are complementary methods depending on the conservation motivations and funding.…”

Section: Discussionmentioning

confidence: 99%

“…line transect monitoring), while CR monitoring programs can be costly to develop at large spatial scales because more sampling effort is required over a longer time period to recapture individuals (Hammond et al, 2021). Even when estimating abundance over the same study area, DS and CR do not estimate exactly the same quantity (Calambokidis & Barlow, 2004;Crum, Neyman, & Gowan, 2021). DS methods estimate abundance within a study area at the time of the survey.…”

Section: Introductionmentioning

confidence: 99%

Integrated spatial models foster complementarity between monitoring programs in producing large-scale bottlenose dolphin indicators

Lauret

Labach

Turek

et al. 2021

Preprint

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Over the last decades, large-scale conservation projects have emerged that require collecting ecological data over broad spatial and temporal coverage. Yet, obtaining relevant information about large-scale population dynamics from a single monitoring program is challenging, and often several sources of data, possibly heterogeneous, need to be integrated. In this context, spatial integrated models combine multiple data types into a single analysis to quantify population dynamics of a targeted population. Using available information at different spatial or temporal scales, spatial integrated models have the potential to produce detailed ecological estimates that would be difficult to obtain if data were analyzed separately. So far, these models are available for open populations to estimate demographic parameters (survival, recruitment), therefore requiring data collected in long-term monitoring programs. In conservation biology however, we often need to quantify population abundance and density in closed populations. In this paper, we showcase the implementation of spatial integrated models to closed populations in a conservation context. We analyzed spatial capture-recapture data together with distance-sampling data to estimate abundance and density. Focusing on the Mediterranean bottlenose dolphins (Tursiops truncatus) as a case study, we combined 21,464 km of photo-identification boat surveys collecting spatial capture-recapture data with 24,624 km of aerial line-transect following a distance-sampling protocol. We compared the performances of the spatial integrated model, with that of the distance sampling model, and the spatial capture-recapture model separated. We discussed the benefits of using a spatial integrated model in the context of the assessment of French Mediterranean bottlenose dolphin conservation status to inform continental scale public policies. Overall, we emphasize the usefulness of spatial integrated model to make the most of available datasets in a conservation context. Spatial integrated models are widely applicable and relevant to conservation research and biodiversity assessment at large spatial scales.

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“…Unbiased estimates of species density and abundance are critical to understand population evolutionary processes in order to assess and select conservation management actions ( Rowcliffe et al, 2011 ; Crum, Neyman & Gowan, 2021 ; Amburgey et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Animal reactivity to camera traps and its effects on abundance estimate using distance sampling in the Taï National Park, Côte d’Ivoire

Houa

Cappelle

Bitty

et al. 2022

PeerJ

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The use of camera traps (CTs) has become an increasingly popular method of studying wildlife, as CTs are able to detect rare, nocturnal, and elusive species in remote and difficult-to-access areas. It thus makes them suited to estimate animal density and abundance, identify activity patterns and new behaviours of animals. However, animals can react when they see the CTs and this can lead to bias in the animal population estimates. While CTs may provide many advantages, an improved understanding of their impacts on individual’s behaviour is necessary to avoid erroneous density estimates. Yet, the impact of CTs on detected individuals, such as human odour near the device and the environment, or the infrared illumination, has received relatively little attention. To date, there is no clear procedure to remove this potential bias. Here, we use camera trap distance sampling (CTDS) to (1) quantify the bias resulting from the different animal responses to the CTs when determining animal density and abundance, and (2) test if olfactory, visual and auditory signals have an influence on the animals’ reaction to CTs. Between March 2019 and March 2020, we deployed CTs at 267 locations distributed systematically over the entire Taï National Park. We obtained 58,947 videos from which we analysed four medium- to-large-bodied species (Maxwell’s duiker (Philantomba maxwellii), Jentink’s duiker (Cephalophus jentinki), pygmy hippopotamus (Choeropsis liberiensis) and Western chimpanzee (Pan troglodytes verus)) displaying different behaviours towards the CTs. We then established species-specific ethograms describing the behavioural responses to the CTs. Using these species-specific responses, we observed that the Maxwell’s duiker reacted weakly to CTs (about 0.11% of the distance data), contrary to Jentink’s duiker, pygmy hippopotamus and Western chimpanzee which reacted with relatively high frequencies, representing 32.82%, 52.96% and 16.14% of the distance data, respectively. Not taking into account the species-specific responses to the CTs can lead to an artificial doubling or tripling of the populations’ sizes. All species reacted more to the CTs at close distances. Besides, the Jentink’s duiker and the pygmy hippopotamus reacted significantly more to the CTs at night than during the day. Finally, as for olfactory signals, the probability of reaction to the CTs during the first days after CTs installation was weak in Maxwell’s duiker, but concerned 18% of the video captures in Western chimpanzees which decreasing with time, but they remained high in pygmy hippopotamus and Jentink’s duiker (65% and 70% of the video captures respectively). Careful consideration should be given to animal’s response to CTs during the analysis and in the field, by reducing human’s impact around the CTs installation.

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Abundance estimation for line transect sampling: A comparison of distance sampling and spatial capture-recapture models

Cited by 9 publications

References 52 publications

When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

Integrated spatial models foster complementarity between monitoring programs in producing large-scale bottlenose dolphin indicators

Animal reactivity to camera traps and its effects on abundance estimate using distance sampling in the Taï National Park, Côte d’Ivoire

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