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 difference 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 reflect the winter conditions affecting 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.

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 20172 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.

Camera traps have become popular for monitoring biodiversity and animal populations. Artificial intelligence is increasingly used to automatically classify large image data sets produced by camera traps and many tools that incorporate machine-learning models for automatic image classification have been developed over the last years. However, it is still challenging to combine tools for automatic classification with other tools for processing camera trap images and to adapt these tools to a specific study. Therefore, we propose a semi-automatic workflow for processing camera trap images in R. The workflow includes managing raw images, automatic image classification, a quality check of automatic image labels as well as the possibilities to retrain the model with new images and to manually review subsets of images to correct image labels. We illustrate the workflow with a case-study from the small mammal monitoring program of the Climate-ecological Observatory for Arctic Tundra. We first trained a classification model for small mammals and then transferred the model to new images, including images from newly established camera traps. We could show that retraining the original model with a small number of new images increased model performance and therefore highlight the importance of verifying automatic image labels when a model was transferred to new images. Furthermore, retraining the original model also decreased the time needed for manually reviewing images and correcting image labels substantially. Thus, the proposed workflow results in a data set with high accuracy and minimizes time needed for labeling images manually. This is especially useful for long-term monitoring where new images have to be processed continuously and methods have to be adapted over time. We provide all R scripts and the classification model for small mammals to make the workflow accessible to other ecologists.

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.

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.

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-graysided 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-markrecapture (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 speciesspecific photo counts over different time windows, as a function of CMRabundance 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.

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The arctic tundra is most sensitive to climate change. The change can be quantified from observations of the fauna, flora and weather conditions. To do observations at sufficient spatial and temporal resolution, ground-based observation nodes with sensors are needed.However, the arctic tundra is resource-limited with regards to energy, data networks, and humans. There are also regulatory and practical obstacles. Consequently, observation nodes must be small and unobtrusive, have a year or longer operational lifetime from small batteries, and be able to report results and receive software updates over scarce back-haul networks.We describe the architecture, design, and implementation of prototype observation nodes deployed to the arctic tundra for the periods August 2019 to July 2020 and August 2020 to July 2021.For the 2019 deployment, ten nodes were each placed inside ten existing camera traps. A camera trap is a box with a wildlife camera taking pictures of rodents when they enter the box from tunnels under snow and ice. For the 2020 deployment, eight nodes were located pairwise inside four camera traps.Each node measures carbon dioxide level and temperature inside the camera trap during the winter season. A node reports its state and observational data each night over a commercial low power IoT telecom back-haul network, if available.We report on the issues encountered doing actual deployments of the prototype nodes. For each issue, we describe the reason for why it happened, relate it to the architecture, design and implementation, and explain what we did about it.