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Register a new userTrends in bird abundance differ among protected forests but not bird guilds
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Improved monitoring and associated inferential tools to efficiently identify declining bird populations, particularly of rare or sparsely distributed species, is key to informed conservation and management across large spatio-temporal regions. We assess abundance trends for 106 bird species in a network of eight national park forests located within the northeast USA from 2006-2019 using a novel hierarchical model. We develop a multi-species, multi-region removal sampling model that shares information across species and parks to enable inference on rare species and sparsely sampled parks and to evaluate the effects of local forest structure. Trends in bird abundance over time varied widely across parks, but species showed similar trends within parks. Three parks (Acadia, Marsh-Billings-Rockefeller, and Morristown) decreased in bird abundance across all species, while three parks (Saratoga, Roosevelt-Vanderbilt, and Weir-Farm) increased in abundance. Bird abundance peaked at medium levels of basal area and high levels of percent forest and forest regeneration, with percent forest having the largest effect. Variation in these effects across parks could be a result of differences in forest structural stage and diversity. Our novel hierarchical model enables estimates of abundance at the network, park, guild, and species levels. We found large variation in abundance trends across parks but not across bird guilds, suggesting that local forest condition may have a broad and consistent effect on the entire bird community within a given park. Management should target the three parks with overall decreasing trends in bird abundance to further identify what specific factors are driving observed declines across the bird community. Understanding how bird communities respond to local forest structure and other stressors is crucial for informed and lasting management.
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In birds, the link between parental care behaviour and prolactin release during incubation persists after hatching in altricial birds, but has never been precisely studied during the whole rearing period in precocial species, such as ducks. The present study aims to understand how changes in parental care after hatching are related to circulating prolactin levels in mallard hens rearing ducklings. Blood was sampled in hens over at least 13 post-hatching weeks and the behaviour of the hens and the ducklings was recorded daily until fledging. Contacts between hens and the ducklings, leadership of the ducklings and gathering of them steadily decreased over post-hatching time. Conversely, resting, preening and agonistic behaviour of hens towards ducklings increased. Plasma prolactin concentrations remained at high levels after hatching and then fell after week 6 when body mass and structural size of the young were close to those of the hen. Parental care behaviour declined linearly with brood age, showed a disruption of the hen-brood bond at week 6 post-hatching and was related to prolactin concentration according to a sigmoid function. Our results suggest that a definite threshold in circulating prolactin is necessary to promote and/or to maintain post-hatching parental care in ducks.
Whether what you see in Figure 1 is a flamingo or a bird, is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the majority of us non-experts just bird would probably suffice. The real question is therefore -- how can we tailor for different fine-grained definitions under divergent levels of expertise. For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes bird-->Phoenicopteriformes-->Phoenicopteridae-->flamingo. To approach this new problem, we first conduct a comprehensive human study where we confirm that most participants prefer multi-granularity labels, regardless whether they consider themselves experts. We then discover the key intuition that: coarse-level label prediction exacerbates fine-grained feature learning, yet fine-level feature betters the learning of coarse-level classifier. This discovery enables us to design a very simple albeit surprisingly effective solution to our new problem, where we (i) leverage level-specific classification heads to disentangle coarse-level features with fine-grained ones, and (ii) allow finer-grained features to participate in coarser-grained label predictions, which in turn helps with better disentanglement. Experiments show that our method achieves superior performance in the new FGVC setting, and performs better than state-of-the-art on traditional single-label FGVC problem as well. Thanks to its simplicity, our method can be easily implemented on top of any existing FGVC frameworks and is parameter-free.
Bird collisions with high-speed transport modes is a vital topic on vehicle safety and wildlife protection, especially when high-speed trains, with an average speed of 250km/h, have to run across the habitat of an endangered bird species. This paper evaluates the bird-train collision risk associated with a recent high-speed railway project in Qinling Mountains, China, for the crested ibis (Nipponia nippon) and other local bird species. Using line transect surveys and walking monitoring techniques, we surveyed the population abundance, spatial-temporal distributions, and bridge-crossing behaviors of the birds in the study area. The results show that: (1) The crested ibis and the egret were the two most abundant waterfowl species in the study area. The RAI of these two species were about 43.69% and 42.91%, respectively; (2) Crested ibises overall habitat closer to the railway bridge. 91.63% of them were firstly detected within the range of 0m to 25m of the vicinity of the bridge; (3) the ratio between crossing over and under the railway bridge was about 7:3. Crested ibises were found to prefer to fly over the railway bridge (89.29% of the total crossing activities observed for this species). Egrets were more likely to cross the railway below the bridge, and they accounted for 60.27% of the total observations of crossing under the bridge. We recommend that, while the collision risk of crested ibises could be low, barrier-like structures, such as fences, should still be considered to promote the conservation of multiple bird species in the area. This paper provides a practical case for railway ecology studies in China. To our best knowledge, this is the first high-speed railway project that takes protecting crested ibises as one of the top priorities, and exemplifies the recent nationwide initiative towards the construction of eco-civilization in the country.
We evaluated the effectiveness of an automated bird sound identification system in a situation that emulates a realistic, typical application. We trained classification algorithms on a crowd-sourced collection of bird audio recording data and restricted our training methods to be completely free of manual intervention. The approach is hence directly applicable to the analysis of multiple species collections, with labelling provided by crowd-sourced collection. We evaluated the performance of the bird sound recognition system on a realistic number of candidate classes, corresponding to real conditions. We investigated the use of two canonical classification methods, chosen due to their widespread use and ease of interpretation, namely a k Nearest Neighbour (kNN) classifier with histogram-based features and a Support Vector Machine (SVM) with time-summarisation features. We further investigated the use of a certainty measure, derived from the output probabilities of the classifiers, to enhance the interpretability and reliability of the class decisions. Our results demonstrate that both identification methods achieved similar performance, but we argue that the use of the kNN classifier offers somewhat more flexibility. Furthermore, we show that employing an outcome certainty measure provides a valuable and consistent indicator of the reliability of classification results. Our use of generic training data and our investigation of probabilistic classification methodologies that can flexibly address the variable number of candidate species/classes that are expected to be encountered in the field, directly contribute to the development of a practical bird sound identification system with potentially global application. Further, we show that certainty measures associated with identification outcomes can significantly contribute to the practical usability of the overall system.
Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having $O(1)$ global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.
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