A guide to pre-processing high-throughput animal tracking data

1. Modern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animal’s step size, leading to mis-estimation of key movement metrics such as speed. ‘Cleaning’ the data to reduce location errors prior to analyses is one of the main ways movement ecologists deal with noisy data, and has the advantage of being more scalable to massive datasets than more complex methods. Though data cleaning is widely recommended, and ecologists routinely consider cleaned data to be the ground-truth, inclusive uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is still rather scarce. 2. A pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficient signal vs. noise screening, in which location errors are rejected without discarding valid animal movements. Another useful feature of a pre-processing pipeline is efficiently segmenting and clustering location data for statistical methods, while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time consuming, and to boost reproducibility, a robust pre-processing pipeline must be automated. 3. In this article we provide guidance on building pipelines for pre-processing high-throughput animal tracking data in order to prepare it for subsequent analysis. Our recommended pipeline, consisting of removing outliers, smoothing the filtered result, and thinning it to a uniform sampling interval, is applicable to many massive tracking datasets. We apply this pipeline to simulated movement data with location errors, and also show a case study of how large volumes of cleaned data can be transformed into biologically meaningful ‘residence patches’, for quick biological inference on animal space use. We use calibration data to illustrate how pre-processing improves its quality, and to verify that the residence patch synthesis accurately captures animal space use. Finally, turning to tracking data from Egyptian fruit bats (Rousettus aegyptiacus), we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. 4. To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data-volume combined with knowledge of the tracked individuals’ movement capacity can be used to reduce location errors. The atlastools function is easy to use for beginners, while providing a template for further development. The use of common pre-processing steps that are simple yet robust promotes standardised methods in the field of movement ecology and leads to better inferences from data

Exploration speed in captivity predicts foraging tactics and diet in free-living red knots

1. Variation in foraging tactics and diet is usually attributed to differences in morphology, experience and prey availability. Recently, consistent individual differences in behaviour (personality) have been shown to be associated with foraging strategies. Bolder or more exploratory individuals are predicted to have a faster pace‐of‐life and offset the costs of moving more or in risky areas, with higher energetic gains by encountering profitable foraging opportunities and prey. However, the relationship between personality, foraging and diet is poorly understood. 2. We investigated how exploratory behaviour in red knots Calidris canutus is associated with foraging tactics and diet by combining laboratory experiments, field observations and stable isotope analysis. First, we developed a mobile experimental arena to measure exploration speed in controlled settings. We validated the method by repeated testing of individuals over time and contexts. This setup allowed us to measure exploratory personality at the field site, eliminating the need to bring birds into captivity for long periods of time. After releasing birds within days of their capture, we asked whether exploration speed was associated with differences in foraging tactics and diet in the wild. 3. We found that tactile foraging red knots mainly caught hard‐shelled prey that are buried in the sediment, whereas visual foraging knots only captured soft preys located close to or on the surface. We also found that faster explorers showed a higher percentage of visual foraging than slower explorers. By contrast, morphology (bill length and gizzard size) had no significant effect on foraging tactics. Diet analysis based on δ(15)N and δ(13)C stable isotope values of plasma and red blood cells confirmed our field observations with slower explorers mainly consumed hard‐shelled prey while faster explorers consumed more soft than hard‐shelled prey. 4. Our results show that foraging tactics and diet are associated with a personality trait, independent of morphological differences. We discuss how consistent behaviour might develop early in life through positive feedbacks between foraging tactics, prey type and foraging efficiency

Quantification of marine benthic communities with metabarcoding

DNA metabarcoding methods have been implemented in studies aimed at detecting and quantifying marine benthic biodiversity. In such surveys, universal barcodes are amplified and sequenced from environmental DNA. To quantify biodiversity with DNA metabarcoding, a relation between the number of DNA sequences of a species and its biomass and/or the abundance is required. However, this relationship is complicated by many factors, and it is often unknown. In this study, we validate estimates of biomass and abundance from molecular approaches with those from the traditional morphological approach. Abundance and biomass were quantified from 126 samples of benthic intertidal mudflat using traditional morphological approaches and compared with frequency of occurrence and relative read abundance estimates from a molecular approach. A relationship between biomass and relative read abundance was found for two widely dispersed annelid taxa (Pygospio and Scoloplos). None of the other taxons, however, showed such a relationship. We discuss how quantification of abundance and biomass using molecular approaches are hampered by the ecology of DNA i.e. all the processes that determine the amount of DNA in the environment, including the ecology of the benthic species as well as the compositional nature of sequencing data

Landscape-scale experiment demonstrates that Wadden Sea intertidal flats are used to capacity by molluscivore migrant shorebirds

P>1. Whether intertidal areas are used to capacity by shorebirds can best be answered by large-scale manipulation of foraging areas. The recent overexploitation of benthic resources in the western Dutch Wadden Sea offers such an 'experimental' setting. 2. We review the effects of declining food abundances on red knot Calidris canutus islandica numbers, based on a yearly large-scale benthic mapping effort, long-term colour-ringing and regular bird-counts from 1996 to 2005. We focus on the three-way relationships between suitable foraging area, the spatial predictability of food and red knot survival. 3. For each benthic sampling position, red knot intake rate (mg AFDM s-1) was predicted by a multiple prey species functional response model, based on digestive rate maximization (this model explained diet and intake rate in earlier studies on red knots). This enabled us to derive the spatial distribution of the suitable foraging area, which in each of the 10 years was analysed with a measure of autocorrelation, i.e. Moran's I. 4. Over the 10 years, when accounting for a threshold value to meet energetic demands, red knots lost 55% of their suitable foraging area. This ran parallel to a decrease in red knot numbers by 42%. Although there was also a decrease in patchiness (i.e. less information about the location of the suitable feeding sites), this did not yet lead to additional loss of birds. 5. To cope with these landscape-scale declines in food stocks, an increase in the capacity for instantaneous food processing would be required. Although we show that red knots indeed enlarged their muscular gizzards, the increase in gizzard size was not enough to compensate for the decreased feeding area. 6. Survival of islandica knots in the western Dutch Wadden Sea, based on colour-ring resightings, declined from 89% in the first half of our study period to 82% in the second half of our study period and could account for almost half of the decline in red knot numbers; the rest must have moved elsewhere in winter. 7. Densities of red knots per unit suitable foraging area remained constant at 10 knots ha-1 between 1996 and 2005, which suggests that red knots have been using the Dutch Wadden Sea to full capacity

Predictive performance of deep-learning-enhanced remote-sensing data for ecological variables of tidal flats over time

Tidal flat systems with a diverse benthic community (e.g., bivalves, polychaetes and crustaceans) is important in the food chain for migratory birds and fish. The geographical distribution of macrozoobenthos depends on physical factors, among which sediment characteristics are key aspects. Although high-resolution and high-frequency mapping of benthic indices (i.e., sediment composition and benthic fauna) of these coastal systems are essential to coastal management plans, it is challenging to gather such information on tidal flats through in-situ measurements. The Synoptic Intertidal Benthic Survey (SIBES) database provides this field information for a 500m grid annual for the Dutch Wadden Sea, but continuous coverage and seasonal dynamics are still lacking. Remote sensing may be the only feasible monitoring method to fill in this gap, but it is hampered by the lack of spectral contrast and variation in this environment. In this study, we used a deep-learning model to enhance the information extraction from remote-sensing images for the prediction of environmental and ecological variables of the tidal flats of the Dutch Wadden Sea. A Variational Auto Encoder (VAE) deep-learning model was trained with Sentinel-2 satellite images with four bands (blue, green, red and near-infrared) over three years (2018, 2019 and 2020) of the tidal flats of the Dutch Wadden Sea. The model was trained to derive important characteristics of the tidal flats as image features by reproducing the input image. These features contain representative information from the four input bands, like spatial texture and band ratios, to complement the low-contrast spectral signatures. The VAE features, the spectral bands and the field-collected samples together were used to train a random forest model to predict the sediment characteristics: median grain size and silt content, and macrozoobenthic biomass and species richness. The prediction was done on the tidal flats of Pinkegat and Zoutkamperlaag of the Dutch Wadden sea. The encoded features consistently increased the accuracy of the predictive model. Compared to a model trained with just the spectral bands, the use of encoded features improved the prediction (coefficient of determination, R2) by 10-15% points for 2018, 2019 and 2020. Our approach improves the available techniques for mapping and monitoring of sediment and macrozoobenthic properties of tidal flat systems and thereby contribute towards their sustainable management

Enhancing the predictive performance of remote sensing for ecological variables of tidal flats using encoded features from a deep learning model

Tidal flats are among the ecologically richest areas of the world where sediment composition (e.g. median grain size and silt content) and the macrozoobenthic presence play an important role in the health of the ecosystem. Regular monitoring of environmental and ecological variables is essential for sustainable management of the area. While monitoring based on field sampling is very time-consuming, the predictive performance of these variables using satellite images is low due to the spectral homogeneity over these regions. We tested a novel approach that uses features from a variational autoencoder (VAE) model to enhance the predictive performance of remote sensing images for environmental and ecological variables of tidal flats. The model was trained using the Sentinel-2 spectral bands to reproduce the input images, and during this process, the VAE model represents important information on the tidal flats within its layer structure. The information in the layers of the trained model was extracted to form features with identical spatial coverage to the spectral bands. The features and the spectral bands together form the input to random forest models to predict field observations of the sediment characteristics such as median grain size and silt content, as well as the macrozoobenthic biomass and species richness. The maximum prediction accuracy of feature-based maps was close to 62% for the sediment characteristics and 37% for benthic fauna indices. The encoded features improved the prediction accuracy of the random forest regressor model by 15% points on average in comparison to using just the spectral bands. Our method enhances the predictive performance of remote sensing, in particular the spatiotemporal dynamics in median grain size and silt content of the sediment thereby contributing to better-informed management of coastal ecosystems

Publisher correction:Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites

[This corrects the article DOI: 10.1186/s40462-018-0142-4.].</p

Connecting foraging and roosting areas reveals how food stocks explain shorebird numbers

Shorebird populations, especially those feeding on shellfish, have strongly declined in recent decades and identifying the drivers of these declines is important for conservation. Changing food stocks are thought to be a key driver of these declines and may also explain why trends have not been uniform across Europe's largest estuary. We therefore investigated how winter population trends of Eurasian oystercatchers (Haematopus ostralegus) were linked to food availability in the Dutch Wadden Sea. Our analysis incorporated two spatial scales, a smaller scale focused on roost counting areas and food available to birds in these areas and a larger spatial scale of tidal basins. A novelty in our study is that we quantify the connectivity between roosting and foraging areas, identified from GPS tracking data. This allowed us to estimate food available to roosting birds and thus how food availability may explain local population trends. At the smaller spatial scale of roost counting areas, there was no clear relationship between available food and the number of roosting oystercatchers, indicating that other factors may drive population fluctuations at finer spatial scales. At the scale of tidal basins, however, there was a significant relationship between population trends and available food, especially cockle Cerastoderma edule,. Mortality and recruitment alone could not account for the large fluctuations in bird counts, suggesting that the site choice of wintering migratory oystercatchers may primarily drive these large fluctuations. Furthermore, the relationship between oystercatcher abundance and benthic food stocks, suggests winter shorebird counts could act as ecological indicators of ecosystem health, informing about the winter status of food stocks at a spatial scale of tidal basins