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

Issues of under-representation in quantitative DNA metabarcoding weaken the inference about diet of the tundra vole Microtus oeconomus

During the last decade, methods based on high-throughput sequencing such as DNA metabarcoding have opened up for a range of new questions in animal dietary studies. One of the major advantages of dietary metabarcoding resides in the potential to infer a quantitative relationship between sequence read proportions and biomass of ingested food. However, this relationship’s robustness is highly dependent on the system under study, calling for case-specific assessments. Herbivorous small rodents often play important roles in the ecosystem, and the use of DNA metabarcoding for analyses of rodent diets is increasing. However, there has been no direct validation of the quantitative reliability of DNA metabarcoding for small rodents. Therefore, we used an experimental approach to assess the relationship between input plant biomass and sequence reads proportions from DNA metabarcoding in the tundra vole Microtus oeconomus. We found a weakly positive relationship between the number of high-throughput DNA sequences and the expected biomass proportions of food plants. The weak relationship was possibly caused by a systematic under-amplification of one of the three plant taxa fed. Generally, our results add to the growing evidence that case-specific validation studies are required to reliably make use of sequence read abundance as a proxy of relative food proportions in the diet

Not only mosses: lemming winter diets as described by DNA metabarcoding

This is a post-peer-review, pre-copyedit version of an article published in Polar Biology. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00300-017-2114-3. The temporal dynamics of most tundra food webs are shaped by the cyclic population dynamics of lemmings. While processes during winter may be behind the recent disruptions of lemming cycles, lemming winter ecology is poorly known. We present here the first DNA metabarcoding data on the winter diet of Norwegian lemmings (Lemmus lemmus), based on feces collected after a winter of population increase. Prostrate willows, mosses, and graminoids dominated the species winter diet, indicating that the conventional idea of lemmings as moss‐specialists should be revised. The behavior of lemming‐plant models in theoretical studies is conditional on the assumptions of mosses being their main winter food item. As shrubs have been excluded from the framework of these models, incorporating them in future modeling studies should nuance our understanding on how plants affect lemmings. We also sampled diet of a few individuals found dead on top of the snow. These individuals had relatively empty stomachs and had, prior to death, relied heavily on mosses. This apparent lack of abundant good quality indicates spatial heterogeneity in local food availability during the population increase phase

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

High Arctic ecosystem states : Conceptual models of vegetation change to guide long-term monitoring and research

Acknowledgements Open Access funding provided by Norwegian Polar Institute. We are grateful to our colleagues in COAT—Climate-ecological Observatory for Arctic Tundra for discussions, and to the editor Niels Martin Schmidt and anonymous reviewers for their thoughtful and constructive comments on earlier versions of this paper.Peer reviewedPublisher PD

What are the effects of herbivore diversity on tundra ecosystems? : A systematic review protocol

Funding Information: The project was funded by the Icelandic Research Fund (Grant Nr. 217754) and the European Union’s Horizon 2020 programme (CHARTER project, Grant Agreement Nr. 869471). Funding for open access publication was provided by the Agricultural University of Iceland. The funding bodies had no influence in the design of the study and collection, analysis and interpretation of data. Funding Information: This study is a contribution of the Herbivory Network (http://herbivory.lbhi.is), a UArctic Thematic Network. Publisher Copyright: © 2022, The Author(s).Background: Changes in the diversity of herbivore communities can strongly influence the functioning of northern ecosystems. Different herbivores have different impacts on ecosystems because of differences in their diets, behaviour and energy requirements. The combined effects of different herbivores can in some cases compensate each other but lead to stronger directional changes elsewhere. However, the diversity of herbivore assemblages has until recently been a largely overlooked dimension of plant–herbivore interactions. Given the ongoing environmental changes in tundra ecosystems, with increased influx of boreal species and changes in the distribution and abundance of arctic herbivores, a better understanding of the consequences of changes in the diversity of herbivore assemblages is needed. This protocol presents the methodology that will be used in a systematic review on the effects of herbivore diversity on different processes, functions and properties of tundra ecosystems. Methods: This systematic review builds on an earlier systematic map on herbivory studies in the Arctic that identified a relatively large number of studies assessing the effects of multiple herbivores. The systematic review will include primary field studies retrieved from databases, search engines and specialist websites, that compare responses of tundra ecosystems to different levels of herbivore diversity, including both vertebrate and invertebrate herbivores. We will use species richness of herbivores or the richness of functional groups of herbivores as a measure of the diversity of the herbivore assemblages. Studies will be screened in three stages: title, abstract and full text, and inclusion will follow clearly identified eligibility criteria, based on their target population, exposure, comparator and study design. The review will cover terrestrial Arctic ecosystems including the forest-tundra ecotone. Potential outcomes will include multiple processes, functions and properties of tundra ecosystems related to primary productivity, nutrient cycling, accumulation and dynamics of nutrient pools, as well as the impacts of herbivores on other organisms. Studies will be critically appraised for validity, and where studies report similar outcomes, meta-analysis will be performed.Peer reviewe

Within and between breeding-season changes in contaminant occurrence and body condition in the Antarctic breeding south polar skua

The Antarctic ecosystem represents a remote region far from point sources of pollution. Still, Antarctic marine predators, such as seabirds, are exposed to organohalogen contaminants (OHCs) which may induce adverse health effects. With increasing restrictions and regulations on OHCs, the levels and exposure are expected to decrease over time. We studied south polar skua (Catharacta maccormicki), a top predator seabird, to compare OHC concentrations measured in whole blood from 2001/2002 and 2013/2014 in Dronning Maud Land. As a previous study found increasing organochlorine concentrations with sampling day during the 2001/2002 breeding season, suggesting dietary changes, we investigated if this increase was repeated in the 2013/2014 breeding season. In addition to organochlorines, we analyzed hydroxy-metabolites, brominated contaminants and per- and polyfluoroalkyl substances (PFAS) in 2013/2014, as well as dietary descriptors of stable isotopes of carbon and nitrogen, to assess potential changes in diet during breeding. Lipid normalized concentrations of individual OHCs were 63%, 87% and 105% higher for hexachlorobenzene (HCB), 1,1-dichloro-2,2-bis (p-chlorophenyl)ethylene (p,p'-DDE), and ∑Polychlorinated biphenyls (PCBs), respectively, in 2013/2014 compared to 2001/2002. South polar skuas males in 2013/2014 were in poorer body condition than in 2001/2002, and with higher pollutant levels. Poorer body condition may cause the remobilization of contaminants from stored body reserves, and continued exposure to legacy contaminants at overwintering areas may explain the unexpected higher OHC concentrations in 2013/2014 than 2001/2002. Concentrations of protein-associated PFAS increased with sampling day during the 2013/2014 breeding season, whereas the lipid-soluble chlorinated pesticides, PCBs and polybrominated diphenyl ether (PBDEs) showed no change. OHC occurrence was not correlated with stable isotopes. The PFAS biomagnification through the local food web at the colony should be investigated further

Will borealization of Arctic tundra herbivore communities be driven by climate warming or vegetation change?

Poleward shifts in species distributions are expected and frequently observed with a warming climate. In Arctic ecosystems, the strong warming trends are associated with increasing greenness and shrubification. Vertebrate herbivores have the potential to limit greening and shrub advance and expansion on the tundra, posing the question of whether changes in herbivore communities could partly mediate the impacts of climate warming on Arctic tundra. Therefore, future changes in the herbivore community in the Arctic tundra will depend on whether the community tracks the changing climates directly (i.e. occurs in response to temperature) or indirectly, in response to vegetation changes (which can be modified by trophic interactions). In this study, we used biogeographic and remotely sensed data to quantify spatial variation in vertebrate herbivore communities across the boreal forest and Arctic tundra biomes. We then tested whether present-day herbivore community structure is determined primarily by temperature or vegetation. We demonstrate that vertebrate herbivore communities are significantly more diverse in the boreal forest than in the Arctic tundra in terms of species richness, phylogenetic diversity and functional diversity. A clear shift in community structure was observed at the biome boundary, with stronger northward declines in diversity in the Arctic tundra. Interestingly, important functional traits characterizing the role of herbivores in limiting tundra vegetation change, such as body mass and woody plant feeding, did not show threshold changes across the biome boundary. Temperature was a more important determinant of herbivore community structure across these biomes than vegetation productivity or woody plant cover. Thus, our study does not support the premise that herbivore-driven limitation of Arctic tundra shrubification or greening would limit herbivore community change in the tundra. Instead, borealization of tundra herbivore communities is likely to result from the direct effect of climate warming

Novel frontier in wildlife monitoring: Identification of small rodent species from fecal pellets using near-infrared reflectance spectroscopy (NIRS)

Small rodents are prevalent and functionally important across the world's biomes, making their monitoring salient for ecosystem management, conservation, forestry, and agriculture. There is a growing need for cost-effective and noninvasive methods for large-scale, intensive sampling. Fecal pellet counts readily provide relative abundance indices, and given suitable analytical methods, feces could also allow for the determination of multiple ecological and physiological variables, including community composition. In this context, we developed calibration models for rodent taxonomic determination using fecal near-infrared reflectance spectroscopy (fNIRS). Our results demonstrate fNIRS as an accurate and robust method for predicting genus and species identity of five coexisting subarctic microtine rodent species. We show that sample exposure to weathering increases the method's accuracy, indicating its suitability for samples collected from the field. Diet was not a major determinant of species prediction accuracy in our samples, as diet exhibited large variation and overlap between species. fNIRS could also be applied across regions, as calibration models including samples from two regions provided a good prediction accuracy for both regions. We show fNIRS as a fast and cost-efficient high-throughput method for rodent taxonomic determination, with the potential for cross-regional calibrations and the use on field-collected samples. Importantly, appeal lies in the versatility of fNIRS. In addition to rodent population censuses, fNIRS can provide information on demography, fecal nutrients, stress hormones, and even disease. Given the development of such calibration models, fNIRS analytics could complement novel genetic methods and greatly support ecosystem- and interaction-based approaches to monitoring

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