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

A Dynamic Occupancy Model for Interacting Species with Two Spatial Scales

Occupancy models have been extended to account for either multiple spatial scales or species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant to models of interacting species. Here we bridge these two model frameworks by developing a multi-scale, two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities—including probabilities conditional to the other species’ presence. With a simulation study, we demonstrate that the model is able to estimate most parameters without marked bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities, with only a small bias for some parameters in low-detection scenarios. We further evaluate the model’s ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator–prey system. Most parameters are estimated with low uncertainty (i.e. narrow posterior distributions). More broadly, our model framework creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasting movement ranges with camera traps.Supplementary materials accompanying this paper appear online.publishedVersio

Using camera traps to monitor cyclic vole populations

Camera traps have become popular labor-efficient and non-invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or animals with identifiable features, with less attention being paid to small mammals, including rodents. Here we investigate the suitability of camera-trap-based abundance indices to monitor population dynamics in two species of voles with key functions in boreal and Arctic ecosystems, known for their high-amplitude population cycles. The targeted species—gray-sided vole (Myodes rufocanus) and tundra vole (Microtus oeconomus)—differ with respect to habitat use and spatial-social organization, which allow us to assess whether such species traits influence the accuracy of the abundance indices. For both species, multiple live-trapping grids yielding capture-mark-recapture (CMR) abundance estimates were matched with single tunnel-based camera traps (CT) continuously recording passing animals. The sampling encompassed 3 years with contrasting abundances and phases of the population cycles. We used linear regressions to calibrate CT indices, based on species-specific photo counts over different time windows, as a function of CMR-abundance estimates. We then performed inverse regression to predict CMR abundances from CT indices and assess prediction accuracy. We found that CT indices (for windows maximizing goodness-of-fit of the calibration models) predicted adequately the CMR-based estimates for the gray-sided vole, but performed poorly for the tundra vole. However, spatially aggregating CT indices over nearby camera traps enabled reliable abundance indices also for the tundra vole. Such species differences imply that the design of camera trap studies of rodent population dynamics should be adapted to the species in focus, and adequate spatial replication must be considered. Overall, tunnel-based camera traps yield much more temporally resolved abundance metrics than alternative methods, with a large potential for revealing new aspects of the multi-annual population cycles of voles and other small mammal species they interact with

Using machine learning to provide automatic image annotation for wildlife camera traps in the Arctic

Source at https://hdl.handle.net/10037/26504.The arctic tundra is considered the terrestrial biome expected to be most impacted by climate change, with temperatures projected to increase as much as 10 °C by the turn of the century. The Climate-ecological Observatory for Arctic Tundra (COAT) project monitors the climate and ecosystems using several sensor types. We report on results from projects that automate image annotations from two of the camera traps used by COAT: an artificial tunnel under the snow for capturing information about small mammals, and an open-air camera trap using bait that captures information of a range of larger sized birds and mammals. These traps currently produce over two million pictures per year. We have developed and trained several Convolutional Neural Network (CNN) models to automate annotation of images from these camera traps. Results show that we get a high accuracy: 97.84% for tunnel traps, and 94.1% for bait traps. This exceeds previous state of the art in animal identification on camera trap images, and is at a level where we can already relieve experts from manual annotation of images

Predictive state-space modelling of lemming population outbreaks on the Fennoscandian tundra: Are determinants of spatial variation in outbreak amplitude temporally consistent?

Lemmings are famous for their spectacular population cycles that causes waves of biomass through the arctic tundra. Both climate variability and the interaction with the sympatric grey-sided vole have been shown to effect lemming outbreaks. However little is known about the transferability of these effects between peaks. I analyzed the spatial variability using snap-trapping data from two consecutive lemming outbreaks, sampled at 98-109 sites on the Fennoscandian tundra in the period from 2004 to 2013. I estimated the interaction between lemming and grey-sided vole, and the sensitivity of lemmings to climate variability as well as the temporal consistency of these effects. Effects were estimated using hierarchical state-space models, where the observation error was modeled using a removal model. My results suggested a positive effect of altitude on lemming abundance in 3 out of 4 seasons. In line with an earlier study, a mutualistic interaction between lemmings and the sympatric grey-sided vole was indicated for the winter of 2006/07, an effect likely driven indirectly by shared predators. However, I found that this interaction was neither consistent between seasons (winter and summer) nor between the two consecutive peaks. Therefore, determinants of lemming peaks, especially the interaction with grey-sided vole, have poor temporal transferability. I propose this to be due to the large temporal variability in snow properties in addition to the temporally long spanning arctic winter where little is known about both the predator and the lemmings. I also discuss how monitoring data could be improved to provide better efficiency of statistical models aimed at estimating predictors of lemming population dynamics. Key word: Lemmus lemmus; Myodes rufocanus; population dynamics; apparent interactions; temporal transferability; detection probability

Population cycles in small rodents seen through the lens of a wildlife camera

Population cycles in small rodents have attracted attention from ecologists for more than a century. This spectacular phenomenon is crucial for the functioning of many northern food-webs and has intrigued ecologist because of its lessons for general ecology. Knowledge about the rodent cycle has, however, been hampered by the lack of reliable monitoring methods both for rodents and some of their assumed interactants (e.g. the small mustelids). In resent decades, camera traps have become widely used in ecology as they provide a cost-efficient and non-invasive method for wildlife monitoring. In this thesis, consisting of four studies, I will investigate how camera trap tunnels tailored for small mammals can enhance rodent monitoring. First, in study I, I together with colleagues conducted the first large scale assessment of the applicability of tunnel-based camera traps to estimate population parameters in a small mammal community, including during a long Arctic winter. We showed that the camera trap provide estimates of rodent occupancy also under the snow during winter. Further we give recommendation on micro-scale placement of the traps to maximize technical functionality in order to avoid loss of data. Then, in study II, we expand on dynamic occupancy models for interacting species by including two nested spatial scales. This allows for camera trap-based investigation of the rodent-mustelid interaction on both a local and a landscape scale. Features of this interaction are assumed to be a key to understand the cause(s) of the rodent population cycles. In study III, we apply the statistical framework developed in study II to a dataset derived from the long-term monitoring program Climate-ecological Observatory for Arctic Tundra (COAT). Our results show that presence of mustelids increased the extinction probability of rodents on both a local and a landscape scale. Furthermore, we demonstrate a clear habitat dependence and indications of a season-dependency in the rodent-mustelid interaction strength. Finally, in study IV, we assess whether camera trap-based abundance indices can be used to study population dynamics of two rodent species (gray-sided vole (Myodes rufocanus) and tundra vole (Microtus oeconomus)). This was done by comparing camera trap-based abundance indices to abundance estimated from capture-mark-recapture (CMR). For gray-sided voles a single camera trap provided reliable abundance indices with camera trap counts aggregated over 5-days. For tundra voles counts from four spatially replicated camera traps from a single day within the same local population needed to be aggregated to obtain a good correspondence to the abundance estimated from CMR. Such species-differences imply that the design of camera trap studies should be adapted to the species in focus. This study further highlight that camera traps yield much more temporally resolved abundance metrics than alternative methods. To conclude, the work presented in this thesis demonstrates how camera trap-based rodent monitoring provides multiple improvements compared to previous methods. Camera traps are non-invasive avoiding the ethical issues kill-trapping are fraught with. Further, camera traps provide data year round - including from under the snow- on a fine temporal scale. In addition camera traps are not species specific and provide data on the whole small mammals community including small rodents, shrews and small mustelids. In addition, camera traps provide a reliable abundance index at least for two of the most ecologically important rodents species in northern Fennoscandia. Furthermore, this thesis present a statistical framework for investigating mustelid-rodent interactions based camera trap data and exemplify how this framework can improve on the knowledge on one of the longest standing mysteries in ecology

Using subnivean camera traps to study arctic small mammal community dynamics during winter

Small rodents are a key indicator to understand the effect of rapidly changing winter climate on Arctic tundra ecosystems. However, monitoring rodent populations through the long Arctic winter by means of conventional traps has, until now, been hampered by snow cover and harsh ambient conditions. Here, we conduct the first extensive assessment of the utility of a newly developed camera trap to study the winter dynamics of small mammals in the Low Arctic tundra of northern Norway. Forty functional cameras were motion-triggered 20 172 times between September 2014 and July 2015, mainly by grey-sided voles (Myodes rufocanus (Sundevall, 1846)), tundra voles (Microtus oeconomus (Pallas, 1776)), Norwegian lemmings (Lemmus lemmus (Linnaeus, 1758)) and shrews (Sorex spp.). These data proved to be suitable for dynamical modelling of species-specific site occupancy rates. The occupancy rates of all recorded species declined sharply and synchronously at the onset of the winter. This decline happened concurrently with changes in the ambient conditions recorded by time-lapse images of snow and water. Our study demonstrates the potential of subnivean camera traps for elucidating novel aspects of year-round dynamics of Arctic small mammal communities

Seasonal difference in temporal transferability of an ecological model: near-term predictions of lemming outbreak abundances

Ecological models have been criticized for a lack of validation of their temporal transferability. Here we answer this call by investigating the temporal transferability of a dynamic state-space model developed to estimate season-dependent biotic and climatic predictors of spatial variability in outbreak abundance of the Norwegian lemming. Modelled summer and winter dynamics parametrized by spatial trapping data from one cyclic outbreak were validated with data from a subsequent outbreak. There was a distinct diference in model transferability between seasons. Summer dynamics had good temporal transferability, displaying ecological models’ potential to be temporally transferable. However, the winter dynamics transferred poorly. This discrepancy is likely due to a temporal inconsistency in the ability of the climate predictor (i.e. elevation) to refect the winter conditions afecting lemmings both directly and indirectly. We conclude that there is an urgent need for data and models that yield better predictions of winter processes, in particular in face of the expected rapid climate change in the Arctic

A dynamic occupancy model for interacting species with two spatial scales

Occupancy models have been developed independently to account for multiple spatial scales and species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant in models of interacting species. Here we bridge these two model frameworks by developing a multi-scale two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities - including probabilities conditional to the other species’ presence. With a simulation study, we demonstrate that the model is able to estimate parameters without bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities. We further show the model’s ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator-prey system. The field study illustrates that the model allows estimation of species interaction effects on colonization and extinction probabilities at two spatial scales. This creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasted movement ranges with camera traps

Using camera traps to monitor cyclic vole populations

Abstract Camera traps have become popular labor‐efficient and non‐invasive tools to study animal populations. The use of camera trap methods has largely focused on large animals and/or animals with identifiable features, with less attention being paid to small mammals, including rodents. Here we investigate the suitability of camera‐trap‐based abundance indices to monitor population dynamics in two species of voles with key functions in boreal and Arctic ecosystems, known for their high‐amplitude population cycles. The targeted species—gray‐sided vole (Myodes rufocanus) and tundra vole (Microtus oeconomus)—differ with respect to habitat use and spatial‐social organization, which allow us to assess whether such species traits influence the accuracy of the abundance indices. For both species, multiple live‐trapping grids yielding capture‐mark‐recapture (CMR) abundance estimates were matched with single tunnel‐based camera traps (CT) continuously recording passing animals. The sampling encompassed 3 years with contrasting abundances and phases of the population cycles. We used linear regressions to calibrate CT indices, based on species‐specific photo counts over different time windows, as a function of CMR‐abundance estimates. We then performed inverse regression to predict CMR abundances from CT indices and assess prediction accuracy. We found that CT indices (for windows maximizing goodness‐of‐fit of the calibration models) predicted adequately the CMR‐based estimates for the gray‐sided vole, but performed poorly for the tundra vole. However, spatially aggregating CT indices over nearby camera traps enabled reliable abundance indices also for the tundra vole. Such species differences imply that the design of camera trap studies of rodent population dynamics should be adapted to the species in focus, and adequate spatial replication must be considered. Overall, tunnel‐based camera traps yield much more temporally resolved abundance metrics than alternative methods, with a large potential for revealing new aspects of the multi‐annual population cycles of voles and other small mammal species they interact with