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Dream team!
\"Boys and girls will love this exciting full-color storybook featuring Swift, Penny, Rod, and Brody, the high-flying cadets from Nickelodeon's Top Wing! In this high-flying adventure, the cadets must work together to earn their copilot badges\"--Amazon.com.
Book
Aeroecology meets aviation safety: early warning systems in Europe and the Middle East prevent collisions between birds and aircraft
The aerosphere is utilized by billions of birds, moving for different reasons and from short to great distances spanning tens of thousands of kilometres. The aerosphere, however, is also utilized by aviation which leads to increasing conflicts in and around airfields as well as en‐route. Collisions between birds and aircraft cost billions of euros annually and, in some cases, result in the loss of human lives. Simultaneously, aviation has diverse negative impacts on wildlife. During avian migration, due to the sheer numbers of birds in the air, the risk of bird strikes becomes particularly acute for low‐flying aircraft, especially during military training flights. Over the last few decades, air forces across Europe and the Middle East have been developing solutions that integrate ecological research and aviation policy to reduce mutual negative interactions between birds and aircraft. In this paper we 1) provide a brief overview of the systems currently used in military aviation to monitor bird migration movements in the aerosphere, 2) provide a brief overview of the impact of bird strikes on military low‐level operations, and 3) estimate the effectiveness of migration monitoring systems in bird strike avoidance. We compare systems from the Netherlands, Belgium, Germany, Poland and Israel, which are all areas that Palearctic migrants cross twice a year in huge numbers. We show that the en‐route bird strikes have decreased considerably in countries where avoidance systems have been implemented, and that consequently bird strikes are on average 45% less frequent in countries with implemented avoidance systems in place. We conclude by showing the roles of operational weather radar networks, forecast models and international and interdisciplinary collaboration to create safer skies for aviation and birds.
Journal Article
A general deep learning model for bird detection in high‐resolution airborne imagery
Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human‐labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine‐tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.
Journal Article
Penny to the rescue!
\"Boys and girls will love this full-color storybook featuring the high-flying cadets from Nickelodeon's Top Wing! When Commodore Smurkturkski gets trapped in Shipwreck Cove, it's up to Penny to save him--and a lost treasure hidden there! This book includes an exciting lenticular cover that makes it appear as though Penny is actually swimming to the rescue!\"--Amazon.com
Book
Flying stimulates the antioxidant system and protects against oxidative damage in a migratory songbird, yet diet quality has little effect
Ecologically relevant factors such as exercise and diet quality can directly influence how multifaceted physiological systems work; however, little is known about how such factors directly and interactively affect key components of the antioxidant system in multiple tissues of migratory songbirds. We tested 3 main hypotheses across three tissues in European starlings fed diets with more or less antioxidants (anthocyanins) and long‐chain omega‐6 polyunsaturated fats (18:2n6) while being flight‐trained in a wind tunnel. Stimulatory effect of flight: flight‐training stimulated the antioxidant system in that 1) plasma oxidative damage (dROMs) was reduced during a given acute flight, and contrary to our predictions, 2) antioxidant capacity (OXY or ORAC) and oxidative damage in plasma (dROMs), flight‐muscle, and liver (LPO) of flight‐trained birds were similar to that of untrained birds (i.e. not flown in a wind tunnel). Flight‐trained birds that expended more energy per unit time (kJ min−1) during their longest, final flight decreased antioxidant capacity (OXY) the most during the final flight. Dietary fat quality effect: contrary to our predictions, dietary 18:2n‐6 did not influence oxidative status even after flight training. Dietary antioxidant effect: flight‐trained birds supplemented with dietary anthocyanins did not have higher antioxidant capacity in plasma (OXY), or liver and flight‐muscle (ORAC) compared to untrained birds. Counterintuitively, oxidative damage (dROMs) was higher in flight‐trained supplemented birds compared to unsupplemented birds after an acute flight. In sum, the antioxidant system of songbirds flexibly responded to changes in availability of dietary antioxidants as well as increased flight time and effort, and such condition‐dependent, individual‐level, tissue‐specific responses to the oxidative costs of long‐duration flights apparently requires recovery periods for maintaining oxidative balance during migration.
Journal Article
Predicting fisheries from albatross movements requires accounting for individual variability in interaction
Fisheries have major ecological impacts including bycatch of foraging seabirds, but it is often difficult to obtain comprehensive information on the presence of fishing vessels. Automatic Identification System (AIS) data can be used to monitor fisheries and their interactions with GPS-tracked seabirds, but not all vessels have their AIS operational. Bird-tied radar detectors can overcome this limit and complement monitoring, but the technology is recent and costly. We used both methods combined as a training dataset for classification algorithms, to extend the identification of interactions to GPS tracks without radar detectors nor AIS. We studied over 3 years wandering albatrosses from the French Southern Territories, interacting with toothfish and tuna longliners. We used 196 GPS tracks combined with radar detectors, to calculate different movement variables over various scales (time spent in an area, sinuosity, speed) and used a Random Forest to distinguish behaviour in presence or absence of fishing vessels. Our model reached high classification accuracy (ca. 85%) for individual birds included in the training dataset. However, we lost predictive power (around 72% of accuracy, with a drop of specificity from 76 to 59%) when predicting on individuals not included in the training dataset. Our results emphasize the importance of documenting and accounting for individual variations to use animals as sentinels. We discuss the pros and cons of different research avenues (data sampling, classification model, bird species, etc.) to eventually get to predict fisheries from bird movements only.
Journal Article
Citizen science for predicting spatio-temporal patterns in seabird abundance during migration
Pelagic seabirds are elusive species which are difficult to observe, thus determining their spatial distribution during the migration period is a difficult task. Here we undertook the first long-term study on the distribution of migrating shearwaters from data gathered within the framework of citizen science projects. Specifically, we collected daily abundance (only abundance given presence) of Balearic shearwaters from 2005 to 2017 from the online databases Trektellen and eBird. We applied machine-learning techniques, specifically Random Forest regression models, to predict shearwater abundance during migration using 15 environmental predictors. We built separated models for pre-breeding and post-breeding migration. When evaluated for the total data sample, the models explained more than 52% of the variation in shearwater abundance. The models also showed good ability to predict shearwater distributions for both migration periods (correlation between observed and predicted abundance was about 70%). However, relative variable importance and variation among the models built with different training data subsamples differed between migration periods. Our results showed that data gathered in citizen science initiatives together with recently available high-resolution satellite imagery, can be successfully applied to describe the migratory spatio-temporal patterns of seabird species accurately. We show that a predictive modelling approach may offer a powerful and cost-effective tool for the long-term monitoring of the migratory patterns in sensitive marine species, as well as to identify at sea areas relevant for their protection. Modelling approaches can also be essential tools to detect the impacts of climate and other global changes in this and other species within the range of the training data.
Journal Article
Beyond knowing nature: Contact, emotion, compassion, meaning, and beauty are pathways to nature connection
Feeling connected to nature has been shown to be beneficial to wellbeing and pro-environmental behaviour. General nature contact and knowledge based activities are often used in an attempt to engage people with nature. However the specific routes to nature connectedness have not been examined systematically. Two online surveys (total n = 321) of engagement with, and value of, nature activities structured around the nine values of the Biophila Hypothesis were conducted. Contact, emotion, meaning, and compassion, with the latter mediated by engagement with natural beauty, were predictors of connection with nature, yet knowledge based activities were not. In a third study (n = 72), a walking intervention with activities operationalising the identified predictors, was found to significantly increase connection to nature when compared to walking in nature alone or walking in and engaging with the built environment. The findings indicate that contact, emotion, meaning, compassion, and beauty are pathways for improving nature connectedness. The pathways also provide alternative values and frames to the traditional knowledge and identification routes often used by organisations when engaging the public with nature.
Journal Article
Deep inference of seabird dives from GPS-only records: Performance and generalization properties
At-sea behaviour of seabirds have received significant attention in ecology over the last decades as it is a key process in the ecology and fate of these populations. It is also, through the position of top predator that these species often occupy, a relevant and integrative indicator of the dynamics of the marine ecosystems they rely on. Seabird trajectories are recorded through the deployment of GPS, and a variety of statistical approaches have been tested to infer probable behaviours from these location data. Recently, deep learning tools have shown promising results for the segmentation and classification of animal behaviour from trajectory data. Yet, these approaches have not been widely used and investigation is still needed to identify optimal network architecture and to demonstrate their generalization properties. From a database of about 300 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has benchmarked deep neural network architectures trained in a supervised manner for the prediction of dives from trajectory data. It first confirms that deep learning allows better dive prediction than usual methods such as Hidden Markov Models. It also demonstrates the generalization properties of the trained networks for inferring dives distribution for seabirds from other colonies and ecosystems. In particular, convolutional networks trained on Peruvian boobies from a specific colony show great ability to predict dives of boobies from other colonies and from distinct ecosystems. We further investigate accross-species generalization using a transfer learning strategy known as ‘fine-tuning’. Starting from a convolutional network pre-trained on Guanay cormorant data reduced by two the size of the dataset needed to accurately predict dives in a tropical booby from Brazil. We believe that the networks trained in this study will provide relevant starting point for future fine-tuning works for seabird trajectory segmentation.
Journal Article