Pine marten density in lowland riparian woods: A test of the Random Encounter Model based on genetic data

Balestrieri, Alessandro; Ruiz‐González, Aritz; Vergara, Maria; Capelli, Enrica; Tirozzi, Pietro; Alfino, Sara; Minuti, Gianmarco; Prigioni, Claudio; Saino, Nicola

doi:10.1016/j.mambio.2016.05.005

Cited by 27 publications

(28 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another aspect to consider when using these models is that both REMs and gREMs assume that animals move randomly with respect to recording units. Although gREMs have not been tested on empirical data, when compared to count data and DNA‐based censuses, REM estimates seem biased (Balestrieri et al, ; Cusack et al, ). These biases were probably due to the nonrandom behavior of the authors’ study organisms (lion Panthera leo and pine marten Martes martes , respectively).…”

Section: Discussionmentioning

confidence: 99%

Estimating population density of insectivorous bats based on stationary acoustic detectors: A case study

Milchram

Suarez‐Rubio

Schröder

et al. 2020

Ecology and Evolution

View full text Add to dashboard Cite

Automated recording units are commonly used by consultants to assess environmental impacts and to monitor animal populations. Although estimating population density of bats using stationary acoustic detectors is key for evaluating environmental impacts, estimating densities from call activity data is only possible through recently developed numerical methods, as the recognition of calling individuals is impossible. We tested the applicability of generalized random encounter models (gREMs) for determining population densities of three bat species (Common pipistrelle Pipistrellus pipistrellus, Northern bat Eptesicus nilssonii, and Natterer's bat Myotis nattereri) based on passively collected acoustical data. To validate the results, we compared them to (a) density estimates from the literature and to (b) Royle–Nichols (RN) models of detection/nondetection data. Our estimates for M. nattereri matched both the published data and RN‐model results. For E. nilssonii, the gREM yielded similar estimates to the RN‐models, but the published estimates were more than twice as high. This discrepancy might be because the high‐altitude flight of E. nilssonii is not accounted for in gREMs. Results of gREMs for P. pipistrellus were supported by published data but were ~10 times higher than those of RN‐models. RN‐models use detection/nondetection data, and this loss of information probably affected population estimates of very active species like P. pipistrellus. gREM models provided realistic estimates of bat population densities based on automatically recorded call activity data. However, the average flight altitude of species should be accounted for in future analyses. We suggest including flight altitude in the calculation of the detection range to assess the detection sphere more accurately and to obtain more precise density estimates.

show abstract

Section: Discussionmentioning

confidence: 99%

Estimating population density of insectivorous bats based on stationary acoustic detectors: A case study

Milchram

Suarez‐Rubio

Schröder

et al. 2020

Ecology and Evolution

View full text Add to dashboard Cite

show abstract

“…The REM model contains a number of significant assumptions including that animals behave like ideal gas particles moving randomly and independently of one another and of cameratraps (Rowcliffe et al, 2008). Field-based comparisons of estimates from the REM with more widely used approaches for estimating density, such as capture mark recapture and distancebased line transects, have generated both similar and diverging estimates (Rovero & Marshall, 2009;Zero et al, 2013;Anile et al, 2014;Cusack et al, 2015a;Balestrieri et al, 2016;Caravaggi et al, 2016). While it is possible to obtain some data on movement parameters from camera-traps (Rahman et al, 2016;Rowcliffe et al, 2016) estimating movement parameters for REM is one of the approaches most difficult and controversial aspects.…”

Section: Discussionmentioning

confidence: 99%

“…I obtained lesser oriental chevrotain movement data from a radio-telemetry study conducted elsewhere in the species' range (Matsubayashi et al, 2003); and this may not truly reflect movement patterns in the Cardamom Forest Landscape. While data from radio-telemetry has been widely used in REM studies (Anile et al, 2014;Balestrieri et al, 2016;Caravaggi et al, 2016;Soofi et al, 2017) it is possible that the limited precision of such data may underestimate total daily movements and thus inflate density estimates.…”

Section: Discussionmentioning

confidence: 99%

“…Following Balestrieri et al . (), the detection radius ( r ) was obtained through measuring the detection distance from a random subset ( n = 20) of lesser oriental chevrotain camera‐trap detections from the same camera‐traps deployed in a separate study elsewhere in the Cardamom Forest Landscape. These cameras were set by the same field teams.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring tropical forest ungulates using camera‐trap data

Gray¹

2018

Journal of Zoology

View full text Add to dashboard Cite

Tropical forest ungulates are poorly known and, despite their ecological and conservation significance, there are few studies on their density and abundance. I estimated densities of lesser oriental chevrotain Tragulus kanchil in the Southern Cardamom National Park, south‐west Cambodia using camera‐trap encounter rates and fitting a random encounter model (REM) that does not require individual identification of animals. This represents the first use of REM on forest ungulates in mainland tropical Asia and provides the first density estimate for any chevrotain species in mainland South East Asia. Sixty‐five camera‐trap stations generated 501 encounters of lesser oriental chevrotain across >8200 camera‐trap nights within a systematic random camera‐trap grid of 200 km2 in the Southern Cardamom National Park. Density of lesser oriental chevrotain was estimated at between 57 and 98 (trueXfalse¯ 81) individuals per km2. The random deployment of camera‐traps, a prerequisite of the REM, did not prevent the detection of the majority of ground‐dwelling large mammal species likely to be present in the study site. Despite its slow uptake by field conservationists, the REM may have potential for monitoring tropical ungulates particularly in dense evergreen forest where other methodologies, for example, distance‐based line transect sampling are unsuitable.

show abstract

“…Nonetheless, this method still requires intensive labour and associated costs in the field. Likely because of the difficulty in estimating animal movement speed, only a handful of studies have applied the REM to date (Anile, Ragni, Randi, Mattucci, & Rovero, ; Balestrieri et al., ; Caravaggi et al., ; Carbajal‐Borges, Godínez‐Gómez, & Mendoza, ; Cusack et al., ; Manzo, Bartolommei, Rowcliffe, & Cozzolino, ; Rademaker et al., ; Rovero & Marshall, ; Zero, Sundaresan, O'Brien, & Kinnaird, ). Development of a more efficient approach to estimate animal movement speed would improve the feasibility of density estimation using the REM.…”

Section: Introductionmentioning

confidence: 99%

Estimating animal density without individual recognition using information derivable exclusively from camera traps

Nakashima

Fukasawa

Samejima

2017

Journal of Applied Ecology

158

205

View full text Add to dashboard Cite

Efficient and reliable methods for estimating animal density are essential to wildlife conservation and management. Camera trapping is an increasingly popular tool in this area of wildlife research, with further potential arising from technological improvements, such as video‐recording functions that allow for behavioural observation of animals. This information may be useful in the estimation of animal density, even without individual recognition. Although several models applicable to species lacking individual markings (i.e. unmarked populations) have been developed, a methodology incorporating behavioural information from videos has not yet been established. We developed a likelihood‐based model: the random encounter and staying time (REST) model. It is an extension of the random encounter model by Rowcliffe et al. (J Appl Ecol 45:1228, 2008). The REST model describes the relationship among staying time, trapping rate, and density, which is estimable using a frequentist or Bayesian approach. We tested the reliability and feasibility of the REST model using Monte Carlo simulations. We also applied the approach in the African rainforest and compared the results with those of a line‐transect survey. The simulations showed that the REST model provided unbiased estimates of animal density. Even when animal movement speeds varied among individuals, and when animals travelled in pairs, the model provided unbiased density estimates. However, the REST model was vulnerable to unsynchronized activity patterns among individuals. Moreover, it is necessary to use a camera model with a fast and reliable infrared sensor and to set the camera trap's parameters appropriately (i.e. video length, delay period). The field survey showed that the staying time of two ungulate species in the African rainforest exhibited good fit with a temporal parametric distribution, and the REST model provided density estimates consistent with those of a line‐transect survey. Synthesis and applications. The random encounter and staying time model provides better efficiency and higher feasibility than the random encounter model in estimating animal density without individual recognition. Careful application of the random encounter and staying time model provides the potential to estimate density of many ground‐dwelling vertebrates lacking individually recognizable markings, and thus should be an effective method for population monitoring.

show abstract

Pine marten density in lowland riparian woods: A test of the Random Encounter Model based on genetic data

Cited by 27 publications

References 74 publications

Estimating population density of insectivorous bats based on stationary acoustic detectors: A case study

Estimating population density of insectivorous bats based on stationary acoustic detectors: A case study

Monitoring tropical forest ungulates using camera‐trap data

Estimating animal density without individual recognition using information derivable exclusively from camera traps

Contact Info

Product

Resources

About