Biology subjects

Soininen, E. M.

Publications and source records attributed to Soininen, E. M..

3 recordsLinked to original sources

bioRxiv2022

A semi-automatic workflow to process camera trap images in R

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.

ecology↗

bioRxiv2022

Novel frontier in wildlife monitoring: identification of small rodent species from faecal pellets using Near-Infrared Reflectance Spectroscopy (NIRS)

Small rodents are prevalent and functionally important across worlds biomes, making their monitoring salient for ecosystem management, conservation, forestry and agriculture. Yet, there is a dearth of cost-effective and non-invasive methods for large-scale, intensive sampling. As one such method, fecal pellet counts readily provide relative abundance indices. Given available analytical methods, feces could also allow for determination of multiple ecological and physiological variables, including community composition. 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 co-existing subarctic microtine rodent species. We show that sample exposure to weathering did not reduce accuracy, indicating suitability of the method 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. While regional calibration models predicted poorly samples from another region, calibration models including samples from two regions provided a good prediction accuracy for both regions. We propose fNIRS as a fast and cost-efficient high-throughoutput method for rodent taxonomic determination, and highlight its potential for cross-regional calibrations and use on field-collected samples. FNIRS can facilitate rodent population censuses at larger spatial extent than before deemed feasible, if combined with pellet-count based abundance indices. Given the versatility of fNIRS analytics, developing such monitoring schemes can support ecosystem- and interaction-based approaches to monitoring.

ecology↗

bioRxiv2020

A dynamic and hierarchical spatial occupancy model for interacting species

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 models 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.

ecology↗