Enabling the public to easily recognize water birds has a positive effect on wetland bird conservation. However, classifying water birds requires advanced ornithological knowledge, which makes it very difficult for the public to recognize water bird species in daily life. To break the knowledge barrier of water bird recognition for the public, we construct a water bird recognition system (Eyebirds) by using deep learning, which is implemented as a smartphone app. Eyebirds consists of three main modules: (1) a water bird image dataset; (2) an attention mechanism-based deep convolution neural network for water bird recognition (AM-CNN); (3) an app for smartphone users. The waterbird image dataset currently covers 48 families, 203 genera and 548 species of water birds worldwide, which is used to train our water bird recognition model. The AM-CNN model employs attention mechanism to enhance the shallow features of bird images for boosting image classification performance. Experimental results on the North American bird dataset (CUB200-2011) show that the AM-CNN model achieves an average classification accuracy of 85%. On our self-built water bird image dataset, the AM-CNN model also works well with classification accuracies of 94.0%, 93.6% and 86.4% at three levels: family, genus and species, respectively. The user-side app is a WeChat applet deployed in smartphones. With the app, users can easily recognize water birds in expeditions, camping, sightseeing, or even daily life. In summary, our system can bring not only fun, but also water bird knowledge to the public, thus inspiring their interests and further promoting their participation in bird ecological conservation.
Background:
In rural China, many natural water bodies and farmlands have been converted to fish farming
ponds as an economic development strategy. There is still a limited understanding of how the diversity and structure of
microbial communities change in natural and managed fish pond ecosystems.
Objective:
We aimed to identify the changes of the diversity and structure of microbial community and driving
mechanism in pond ecosystems.
Methods:
The datasets of 16S rRNA amplicon sequencing and the concentrations of N and P fractions were achieved in
water samplers of pond ecosystems. Bioinformatics analysis was used to analyze the diversity and structure of the
microbial communities.
Results:
Our results indicated that the diversity and structure of the microbial communities in the natural ponds were
significantly different from ones in managed fish ponds. The nutrients of N and P and water environmental factors were
responsible for 46.3% and 19.5% of the changes in the structure and diversity of the microbial community, respectively.
Conclusion: The N and P fractions and water environmental factors influenced the microbial community structure and
diversity in pond ecosystems. Fish farming indirectly affected the microbial community by altering the contents of N and
P fractions in water bodies of ponds when a natural pond was converted to a managed fish pond.
Conclusion:
The N and P fractions and water environmental factors influenced the microbial community structure and
diversity in pond ecosystems. Fish farming indirectly affected the microbial community by altering the contents of N and
P fractions in water bodies of ponds when a natural pond was converted to a managed fish pond.
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