1
|
Bollen M, Casaer J, Neyens T, Beenaerts N. When and where? Day-night alterations in wild boar space use captured by a generalized additive mixed model. PeerJ 2024; 12:e17390. [PMID: 38881858 PMCID: PMC11179635 DOI: 10.7717/peerj.17390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 04/23/2024] [Indexed: 06/18/2024] Open
Abstract
Wild boar (Sus scrofa), an abundant species across Europe, is often subjected to management in agro-ecosystems in order to control population size, or to scare them away from agricultural fields to safeguard crop yields. Wild boar management can benefit from a better understanding on changes in its space use across the diel cycle (i.e., diel space use) in relation to variable hunting pressures or other factors. Here, we estimate wild boar diel space use in an agro-ecosystem in central Belgium during four consecutive "growing seasons" (i.e., April-September). To achieve this, we fit generalized additive mixed models (GAMMs) to camera trap data of wild boar aggregated over 1-h periods. Our results reveal that wild boar are predominantly nocturnal in all of the hunting management zones in Meerdaal, with activity peaks around sunrise and sunset. Hunting events in our study area tend to take place around sunrise and sunset, while non-lethal human activities occur during sunlight hours. Our GAMM reveals that wild boar use different areas throughout the diel cycle. During the day, wild boar utilized areas in the centre of the forest, possibly to avoid human activities during daytime. During the night, they foraged near (or in) agricultural fields. A post hoc comparison of space use maps of wild boar in Meerdaal revealed that their diurnal and nocturnal space use were uncorrelated. We did not find sufficient evidence to prove that wild boar spatiotemporally avoid hunters. Finally, our work reveals the potential of GAMMs to model variation in space across 24-h periods from camera trap data, an application that will be useful to address a range of ecological questions. However, to test the robustness of this approach we advise that it should be compared against telemetry-based methods to derive diel space use.
Collapse
Affiliation(s)
- Martijn Bollen
- Centre for Environmental Sciences, Hasselt University, Hasselt, Flanders, Belgium
- Research Institute for Nature and Forest (INBO), Brussels, Brussels, Belgium
- Data Science Institute, Hasselt University, Hasselt, Flanders, Belgium
| | - Jim Casaer
- Research Institute for Nature and Forest (INBO), Brussels, Brussels, Belgium
| | - Thomas Neyens
- Data Science Institute, Hasselt University, Hasselt, Flanders, Belgium
- Leuven Biostatistics and statistical Bioinformatics Centre, University of Leuven, Leuven, Flanders, Belgium
| | - Natalie Beenaerts
- Centre for Environmental Sciences, Hasselt University, Hasselt, Flanders, Belgium
| |
Collapse
|
2
|
Vélez J, McShea W, Pukazhenthi B, Stevenson P, Fieberg J. Implications of the scale of detection for inferring co-occurrence patterns from paired camera traps and acoustic recorders. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2024; 38:e14218. [PMID: 37937478 DOI: 10.1111/cobi.14218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 08/29/2023] [Accepted: 10/26/2023] [Indexed: 11/09/2023]
Abstract
Multifunctional landscapes that support economic activities and conservation of biological diversity (e.g., cattle ranches with native forest) are becoming increasingly important because small remnants of native forest may comprise the only habitat left for some wildlife species. Understanding the co-occurrence between wildlife and disturbance factors, such as poaching activity and domesticated ungulates, is key to successful management of multifunctional landscapes. Tools to measure co-occurrence between wildlife and disturbance factors include camera traps and autonomous acoustic recording units. We paired 52 camera-trap stations with acoustic recorders to investigate the association between 2 measures of disturbance (poaching and cattle) and wild ungulates present in multifunctional landscapes of the Colombian Orinoquía. We used joint species distribution models to investigate species-habitat associations and species-disturbance correlations. One model was fitted using camera-trap data to detect wild ungulates and disturbance factors, and a second model was fitted after replacing camera-trap detections of disturbance factors with their corresponding acoustic detections. The direction, significance, and precision of the effect of covariates depended on the sampling method used for disturbance factors. Acoustic monitoring typically resulted in more precise estimates of the effects of covariates and of species-disturbance correlations. Association patterns between wildlife and disturbance factors were found only when disturbance was detected by acoustic recorders. Camera traps allowed us to detect nonvocalizing species, whereas audio recording devices increased detection of disturbance factors leading to more precise estimates of co-occurrence patterns. The collared peccary (Pecari tajacu), lowland tapir (Tapirus terrestris), and white-tailed deer (Odocoileus virginianus) co-occurred with disturbance factors and are conservation priorities due to the greater risk of poaching or disease transmission from cattle.
Collapse
Affiliation(s)
- Juliana Vélez
- Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, Minnesota, USA
- Smithsonian's National Zoo and Conservation Biology Institute, Front Royal, Virginia, USA
| | - William McShea
- Smithsonian's National Zoo and Conservation Biology Institute, Front Royal, Virginia, USA
| | - Budhan Pukazhenthi
- Smithsonian's National Zoo and Conservation Biology Institute, Front Royal, Virginia, USA
| | - Pablo Stevenson
- Departamento de Ciencias Biológicas, Universidad de Los Andes, Bogotá, Colombia
| | - John Fieberg
- Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, Minnesota, USA
| |
Collapse
|
3
|
Batist CH, Dufourq E, Jeantet L, Razafindraibe MN, Randriamanantena F, Baden AL. An integrated passive acoustic monitoring and deep learning pipeline for black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar. Am J Primatol 2024; 86:e23599. [PMID: 38244194 DOI: 10.1002/ajp.23599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 01/05/2024] [Accepted: 01/09/2024] [Indexed: 01/22/2024]
Abstract
The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black-and-white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar-shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in-person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May-July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57-h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in-person observations, saving time, money, and labor while also providing re-analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open-sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies.
Collapse
Affiliation(s)
- Carly H Batist
- Department of Anthropology, City University of New York (CUNY) Graduate Center, New York, New York, USA
- New York Consortium in Evolutionary Primatology (NYCEP), New York, New York, USA
- Rainforest Connection (RFCx), Katy, Texas, USA
| | - Emmanuel Dufourq
- African Institute for Mathematical Sciences, Muizenberg, South Africa
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
- National Institute for Theoretical & Computational Sciences, Stellenbosch, South Africa
- African Institute for Mathematical Sciences, Research and Innovation Centre, Kigali, Rwanda
| | - Lorène Jeantet
- African Institute for Mathematical Sciences, Muizenberg, South Africa
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
- National Institute for Theoretical & Computational Sciences, Stellenbosch, South Africa
| | - Mendrika N Razafindraibe
- Department of Animal Biology, University of Antananarivo, Antananarivo, Madagascar
- Institut International de Science Sociale, Antananarivo, Madagascar
| | | | - Andrea L Baden
- Department of Anthropology, City University of New York (CUNY) Graduate Center, New York, New York, USA
- New York Consortium in Evolutionary Primatology (NYCEP), New York, New York, USA
- Department of Anthropology, Hunter College of City University of New York (CUNY), New York, New York, USA
| |
Collapse
|
4
|
Lawson J, Rizos G, Jasinghe D, Whitworth A, Schuller B, Banks-Leite C. Automated acoustic detection of Geoffroy's spider monkey highlights tipping points of human disturbance. Proc Biol Sci 2023; 290:20222473. [PMID: 36919432 PMCID: PMC10015327 DOI: 10.1098/rspb.2022.2473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Abstract
As more land is altered by human activity and more species become at risk of extinction, it is essential that we understand the requirements for conserving threatened species across human-modified landscapes. Owing to their rarity and often sparse distributions, threatened species can be difficult to study and efficient methods to sample them across wide temporal and spatial scales have been lacking. Passive acoustic monitoring (PAM) is increasingly recognized as an efficient method for collecting data on vocal species; however, the development of automated species detectors required to analyse large amounts of acoustic data is not keeping pace. Here, we collected 35 805 h of acoustic data across 341 sites in a region over 1000 km2 to show that PAM, together with a newly developed automated detector, is able to successfully detect the endangered Geoffroy's spider monkey (Ateles geoffroyi), allowing us to show that Geoffroy's spider monkey was absent below a threshold of 80% forest cover and within 1 km of primary paved roads and occurred equally in old growth and secondary forests. We discuss how this methodology circumvents many of the existing issues in traditional sampling methods and can be highly successful in the study of vocally rare or threatened species. Our results provide tools and knowledge for setting targets and developing conservation strategies for the protection of Geoffroy's spider monkey.
Collapse
Affiliation(s)
- Jenna Lawson
- Grantham Institute, Imperial College London, UK.,Department of Life Sciences, Imperial College London, UK
| | - George Rizos
- GLAM - Group on Language, Audio, & Music, Imperial College London, UK
| | - Dui Jasinghe
- Department of Life Sciences, Imperial College London, UK
| | - Andrew Whitworth
- Osa Conservation, Conservation Science Team, Washington, DC 20005, USA.,Institute of Biodiversity, Animal Health, and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, Scotland, UK.,Department of Biology, Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA
| | - Björn Schuller
- GLAM - Group on Language, Audio, & Music, Imperial College London, UK.,EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | | |
Collapse
|
5
|
Wessling EG, Surbeck M. Failure to account for behavioral variability significantly compromises accuracy in indirect population monitoring. Anim Conserv 2022. [DOI: 10.1111/acv.12844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- E. G. Wessling
- Department of Human Evolutionary Biology Harvard University Cambridge MA USA
- School of Psychology and Neuroscience University of St Andrews St Andrews UK
| | - M. Surbeck
- Department of Human Evolutionary Biology Harvard University Cambridge MA USA
- Max Planck Institute for Evolutionary Anthropology Leipzig Germany
| |
Collapse
|
6
|
Spider Monkeys (Ateles geoffroyi) Habituate to Anthropogenic Pressure in a Low-Impact Tourism Area: Insights from a Multi-Method Approach. INT J PRIMATOL 2022. [DOI: 10.1007/s10764-022-00310-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
7
|
Long-Distance Vocal Signaling in White-Handed Gibbons (Hylobates lar). INT J PRIMATOL 2022. [DOI: 10.1007/s10764-022-00312-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
8
|
Wu SH, Chang HW, Lin RS, Tuanmu MN. SILIC: A cross database framework for automatically extracting robust biodiversity information from soundscape recordings based on object detection and a tiny training dataset. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2021.101534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|
9
|
Abstract
AbstractObserving and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years.
Collapse
|
10
|
Radig B, Bodesheim P, Korsch D, Denzler J, Haucke T, Klasen M, Steinhage V. Automated Visual Large Scale Monitoring of Faunal Biodiversity. PATTERN RECOGNITION AND IMAGE ANALYSIS 2021. [DOI: 10.1134/s1054661821030214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
11
|
Sadhukhan S, Root-Gutteridge H, Habib B. Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method. Sci Rep 2021; 11:7309. [PMID: 33790346 PMCID: PMC8012383 DOI: 10.1038/s41598-021-86718-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 03/19/2021] [Indexed: 02/01/2023] Open
Abstract
Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture-Mark-Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model's predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves' territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.
Collapse
Affiliation(s)
- Sougata Sadhukhan
- grid.452923.b0000 0004 1767 4167Animal Ecology and Conservation Biology, Wildlife Institute of India, Dehradun, 248001 India
| | - Holly Root-Gutteridge
- grid.36511.300000 0004 0420 4262Animal Behaviour, Cognition and Welfare Group, University of Lincoln, Lincoln, UK ,grid.12082.390000 0004 1936 7590Reby Lab, School of Psychology, University of Sussex, Brighton, UK
| | - Bilal Habib
- grid.452923.b0000 0004 1767 4167Animal Ecology and Conservation Biology, Wildlife Institute of India, Dehradun, 248001 India
| |
Collapse
|
12
|
Marchant LF, Wessling EG, Lindshield SM. Introduction to the Special Issue on Savanna Chimpanzees. INT J PRIMATOL 2020. [DOI: 10.1007/s10764-020-00188-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
|