Camera Traps as Sensor Networks for Monitoring Animal Communities

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location – specific information on movement and behavior. Modern digital camera traps that record video present not only new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year-long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time span

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

WiseEye: next generation expandable and programmable camera trap platform for wildlife research

Funding: The work was supported by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1. The work of S. Newey and RJI was part funded by the Scottish Government's Rural and Environment Science and Analytical Services (RESAS). Details published as an Open Source Toolkit, PLOS Journals at: http://dx.doi.org/10.1371/journal.pone.0169758Peer reviewedPublisher PD

Tasa de registros fotográficos con cámaras trampa en caminos vs. fuera de ellos: la ubicación es importante

Tasa de registros fotográficos con cámaras trampa en caminos vs. fuera de ellos: la ubicación es importante. Presentamos los resultados de muestreos con cámaras trampa que realizamos en el Parque Nacional Iguazú, Misiones, Argentina, en 2008 para evaluar si ubicar las cámaras trampa en caminos o senderos o fuera de ellos afecta el ensamble de mamíferos muestreado. Siete pares de estaciones estuvieron activas durante 26.6 ± 8.9 días. Una estación de cada par estuvo ubicada en un camino de tierra angosto y no transitado; la otra a 50 m de distancia perpendicular del camino dentro del bosque. Usamos los registros de otro muestreo con cámaras trampa realizado en el parque nacional Iguazú en 2006-2007 para evaluar si las especies con una mayor proporción de fotos caminando sobre los senderos en lugar de cruzándolos transversal o tangencialmente (índice de uso de senderos) fueron relativamente más registradas en las estaciones ubicadas en senderos en 2008. Usamos el estimador Jackknife de primer orden para comparar la riqueza de especies en estaciones de senderos y fuera de ellos. Un ANOVA multivariado basado en disimilitudes (ADONIS) fue usado para comparar los ensambles de mamíferos registrados en caminos y fuera de ellos. Obtuvimos 228 registros independientes de 15 especies de mamíferos terrestres medianos-grandes. Las estaciones ubicadas en caminos tuvieron una mayor tasa de registros (1.06±0.57 vs. 0.24±0.13 registros/día) y una mayor riqueza que las estaciones fuera de ellos (15 vs. 10 especies observadas; 19.3, SE=2.8 vs. 14.3, SE=2.8 especies estimadas con el modelo Jackknife de 1er orden). Las especies difirieron en sus probabilidades relativas de ser registradas en caminos vs fuera de ellos, algo que puede predecirse a partir del índice de uso de senderos. El ADONIS indicó que el ensamble de mamíferos muestreado en caminos fue estadísticamente distinto al muestreado fuera de ellos, un resultado que puede ser explicado por la tendencia diferencial de las especies a usar los caminos.We present the results of a camera trap survey conducted in 2008 in the Atlantic Forest of Iguazú National Park, Argentina, testing whether placing camera traps on dirt roads/ trails or in off-road locations produce important biases in the recorded species. Seven pairs of camera trap stations were active for 26.6 ± 8.9 days; for each pair, one station was located on a narrow unpaved road and the other about 50 m from the road. We used the first order Jackknife estimator to compare species richness between on-road vs. off-road locations. We used records from another camera trap survey conducted at Iguazú National Park in 2006-2007 to assess whether species with a high Road-use Index (ratio of photographs of animals walking along roads to photographs of animals crossing the roads) had a higher ratio of records on roads / off road stations in the 2008 survey. Multivariate ANOVA based on dissimilarities (ADONIS) was used to compare mammal assemblages recorded at stations located on roads vs. off roads. We obtained 228 independent records of 15 species of medium-large sized terrestrial mammals. Stations located on roads had a higher recording rate (1.06, SD=0.57 vs. 0.24, SD=0.13 records per day) and recorded more species than off-road stations (15 vs. 10 recorded species; 19.3, SE=2.8 vs. 14.3, SE=2.8 species estimated with the 1st order Jackknife model). Species differ in their relative probabilities of being recorded on roads vs. off roads, something that can be predicted with the Road-use Index. The ADONIS indicated that the mammal assemblage surveyed on roads was statistically dissimilar to that surveyed off roads, a result that can be explained by the differential tendency of the species to use roads and trails.Fil: Di Bitetti, Mario Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentina. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; ArgentinaFil: Paviolo, Agustin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentina. Asociación Civil Centro de Investigaciones del Bosque Atlántico; ArgentinaFil: de Angelo, Carlos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentin

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

A semi-automatic workflow to process images from small mammal camera traps

Camera traps have become popular for monitoring biodiversity, but the huge amounts of image data that arise from camera trap monitoring represent a challenge and artificial intelligence is increasingly used to automatically classify large image data sets. However, it is still challenging to combine automatic classification with other steps and tools needed for efficient, quality-assured and adaptive processing of camera trap images in long-term monitoring programs. Here we propose a semi-automatic workflow to process images from small mammal cameras that combines all necessary steps from downloading camera trap images in the field to a quality checked data set ready to be used in ecological analyses. The workflow is implemented in R and includes (1) managing raw images, (2) automatic image classification, (3) quality check of automatic image labels, as well as the possibilities to (4) retrain the model with new images and to (5) manually review subsets of images to correct image labels. We illustrate the application of this workflow for the development of a new monitoring program of an Arctic small mammal community. We first trained a classification model for the specific small mammal community based on images from an initial set of camera traps. As the monitoring program evolved, the classification model was retrained with a small subset of images from new camera traps. This case study highlights the importance of model retraining in adaptive monitoring programs based on camera traps as this step in the workflow increases model performance and substantially decreases the total time needed for manually reviewing images and correcting image labels. We provide all R scripts to make the workflow accessible to other ecologists

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Alarm guard systems for the prevention of damage produced by ungulates in a chestnut grove of Middle Italy

ArticleWildlife populations, in particular ungulates and carnivores, have had a significant increase in most Italian regions over the last decades and for this reason ecosystems and agricultural and forest productions are threatened by damage produced by wildlife. In order to evaluate effective met hodologies and technologies to mitigate the impact of this phenomenon, innovative protection systems, such as electronic acoustic alarm guard sensors, were tested. These devices are able to randomly produce a significant number of sounds and light projecti ons. At the same time, camera traps were used, as a support instrument to show the presence or absence of wild fauna. Video analysis has provided information on the effectiveness of security systems, on the most suitable methods of installation and managem ent of devices and their ecological impact. Experimental trials were carried out in a chestnut grove located in an Apennine area of the Middle Italy during the harvesting period (autumn). The results obtained have shown that these technologies seem to be particularly suitable for crops that concentrate production in a short time (e.g. vine and chestnut) and in areas not excessively large. Widespread use of devices could mitigate the conflict between public bodies involved in the management of wildlife and farmers

Integrating Technologies for Scalable Ecology and Conservation

Integration of multiple technologies greatly increases the spatial and temporal scales over which ecological patterns and processes can be studied, and threats to protected ecosystems can be identified and mitigated. A range of technology options relevant to ecologists and conservation practitioners are described, including ways they can be linked to increase the dimensionality of data collection efforts. Remote sensing, ground-based, and data fusion technologies are broadly discussed in the context of ecological research and conservation efforts. Examples of technology integration across all of these domains are provided for large-scale protected area management and investigation of ecological dynamics. Most technologies are low-cost or open-source, and when deployed can reach economies of scale that reduce per-area costs dramatically. The large-scale, long-term data collection efforts presented here can generate new spatio-temporal understanding of threats faced by natural ecosystems and endangered species, leading to more effective conservation strategies