Landscape-scale estimation of forest ungulate density and biomass using camera traps: Applying the REST model

Nakashima, Yoshihiro; Hongo, Satoshi; Akomo-Okoue, Etienne François

doi:10.1016/j.biocon.2019.108381

Cited by 37 publications

(40 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nakashima et al [ 11 ] improved the feasibility of REM by developing the random encounter and staying time (REST) model, in which all required parameters such as population density are estimated exclusively by camera-trap data. More recently, Nakashima et al [ 12 ] have incorporated habitat covariates into the REST model, allowing for the likelihood-based estimation of the relationship between habitats and animal density. Since earlier methods assumed simple situations with no spatial structures of densities, this approach yields reliable density estimates at the landscape scale, accounting for spatial and temporal heterogeneity in animal detectability.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous estimation of seasonal population density, habitat preference and catchability of wild boars based on camera data and harvest records

et al. 2020

Self Cite

View full text Add to dashboard Cite

Analyses of life history and population dynamics are essential for effective population control of wild mammals. We developed a model for the simultaneous estimation of seasonal changes in three parameters—population density, habitat preference and trap catchability of target animals—based on camera-trapping data and harvest records. The random encounter and staying time model, with no need for individual recognition, is the core component of the model—by combining this model with the catch-effort model, we estimated density at broad spatial scales and catchability by traps. Here, the wild boar population in central Japan was evaluated as a target population. We found that the estimated population density increased after the birth period and then decreased until the next birth period, mainly due to harvesting. Habitat preference changed seasonally, but forests having abandoned fields nearby were generally preferred throughout the season. These patterns can be explained by patterns of food availability and resting or nesting sites. Catchability by traps also changed seasonally, with relatively high values in the winter, which probably reflected changes in the attractiveness of the trap bait due to activity changes in response to food scarcity. Based on these results, we proposed an effective trapping strategy for wild boars, and discussed the applicability of our model to more general conservation and management issues.

show abstract

Section: Introductionmentioning

confidence: 99%

Simultaneous estimation of seasonal population density, habitat preference and catchability of wild boars based on camera data and harvest records

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…This finding was reinforced by Kranz and Lumpkin [38] and Lumpkin and Kranz [39]. Kingdon and Lahm [73] stated that the species is essentially active in the twilight, and may be intermittently active both day and night, while Hedwig et al [10], and Nakashima et al [41] identified it as a nocturnal species. Our results showed that C. silvicultor is mostly nocturnal (85%, n = 236) with one of its main peaks of activity at dusk (crepuscular activity).…”

Section: Discussionmentioning

confidence: 97%

“…Nevertheless, a consensus among scientists still lacking (see Table S1 for taxonomy used by the International Union for Conservation of Nature "IUCN" for duikers studied). We also considered C. crusalbum and C. callipygus as the same species [3,8,41]. Analysis of circular distributions of the two taxa did not show any significant differences (results not shown, Watson's two-sample test, U 2 = 0.04, p > 0.1, Figure S2).…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, large duikers require a longer searching time for fruits to meet their energetic needs [72]. However, Nakashima et al [41] found a similar proportion of activity for P. congica (0.35) and C. silvicultor (0.36).…”

Section: Discussionmentioning

confidence: 99%

“…The daily activity patterns of duikers in the moist forest of Central Africa have been studied on only a few individual species, by telemetry (P. congica, C. callipygus, and C. castaneus) [33,36,37], by observations in captivity (C. silvicultor) [38,39], and summarily by camera trapping (P. congica, C. callipygus, C. castaneus, C. leucogaster, C. crusalbum, C. silvicultor, and C. nigrifrons) [10,40,41]. To date, the activity patterns of several species remains unquantified.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Daily Activity Patterns and Co-Occurrence of Duikers Revealed by an Intensive Camera Trap Survey across Central African Rainforests

Houngbegnon

Cornélis

Vermeulen

et al. 2020

Animals

View full text Add to dashboard Cite

The duiker community in Central African rainforests includes a diversity of species that can coexist in the same area. The study of their activity patterns is needed to better understand habitat use or association between the species. Using camera traps, we studied the temporal activity patterns, and quantified for the first time the temporal overlap and spatial co-occurrence between species. Our results show that: (i) Two species are strongly diurnal: Cephalophus leucogaster, and Philantomba congica, (ii) two species are mostly diurnal: C.callipygus and C. nigrifrons, (iii) one species is strongly nocturnal: C.castaneus, (iv) and one species is mostly nocturnal: C.silvicultor. Analyses of temporal activities (for five species) identified four species pairs that highly overlapped (Δ^≥ 0.80), and six pairs that weakly overlapped (Δ^ between 0.06 and 0.35). Finally, co-occurrence tests reveal a truly random co-occurrence (plt > 0.05 and pgt > 0.05) for six species pairs, and a positive co-occurrence (pgt < 0.05) for four pairs. Positive co-occurrences are particularly noted for pairs formed by C.callipygus with the other species (except C. nigrifrons). These results are essential for a better understanding of the coexistence of duikers and the ecology of poorly known species (C. leucogaster and C. nigrifrons), and provide clarification on the activity patterns of C. silvicultor which was subject to controversy. Camera traps proved then to be a powerful tool for studying the activity patterns of free-ranging duiker populations.

show abstract

State‐space model combining local camera data and regional administration data reveals population dynamics of wild boar

et al. 2022

Self Cite

View full text Add to dashboard Cite

Recent increases in wildlife cause negative impacts on humans through both economic and ecological damage, as well as the spread of pathogens. Understanding the population dynamics of wildlife is crucial to develop effective management strategies. However, it is difficult to estimate accurate and precise population size over large spatial and temporal scales because of the limited data availability. We addressed these issues by first fitting a random encounter and staying time (REST) model based on camera trap data to construct an informative prior distribution for a capture rate parameter in a harvest‐based Bayesian state‐space model. We constructed a Bayesian state‐space model that integrated administration data on the number of captured wild boar with the prior distribution of capture efficiency estimated by camera trap data. The model with informative prior distribution from the REST model successfully estimated population dynamics, whereas the model using only the administration data did not, owing to a lack of parameter convergence. We identified areas where (1) wild boars exhibit a high potential population growth rate and a high carrying capacity, (2) current trapping efforts are effectively suppressing local populations, and (3) trapping reinforcement is required to control populations in the whole region. The model could be used to predict future trends in populations under the assumptions of ongoing trapping pressure. This will help identify spatially explicit trapping efforts to achieve target population levels.

show abstract

Landscape-scale estimation of forest ungulate density and biomass using camera traps: Applying the REST model

Cited by 37 publications

References 45 publications

Simultaneous estimation of seasonal population density, habitat preference and catchability of wild boars based on camera data and harvest records

Simultaneous estimation of seasonal population density, habitat preference and catchability of wild boars based on camera data and harvest records

Daily Activity Patterns and Co-Occurrence of Duikers Revealed by an Intensive Camera Trap Survey across Central African Rainforests

State‐space model combining local camera data and regional administration data reveals population dynamics of wild boar

Contact Info

Product

Resources

About