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BookDOI

Camera Traps in Animal Ecology

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The article was published on 2011-01-01. It has received 430 citations till now. The article focuses on the topics: Animal ecology.

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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

TL;DR: In this paper, the authors train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset.
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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

TL;DR: The ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences is investigated.
Journal ArticleDOI

Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna

TL;DR: This work deployed 225 camera traps across Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics and classified the images via the citizen-science website www.snapshotsereNGeti.org, yielding a final classification for each image and a measure of agreement among individual answers.
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Big cats in our backyards: Persistence of large carnivores in a human dominated landscape in India

TL;DR: This study used photographic capture recapture analysis to assess the density of large carnivores in a human-dominated agricultural landscape with density >300 people/km2 in western Maharashtra, India and found evidence of a wide suite of wild carnivores inhabiting a cropland landscape devoid of wilderness and wild herbivore prey.
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Fear of large carnivores causes a trophic cascade.

TL;DR: It is suggested that the results reinforce the need to conserve large carnivores given the significant “ecosystem service” the fear of them provides.
References
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Book

Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
Journal ArticleDOI

Random-effects models for longitudinal data

Nan M. Laird, +1 more
- 01 Dec 1982 - 
TL;DR: In this article, a unified approach to fitting two-stage random-effects models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed.
Journal ArticleDOI

Program MARK: survival estimation from populations of marked animals

TL;DR: Mark as discussed by the authors provides parameter estimates from marked animals when they are re-encountered at a later time as dead recoveries, or live recaptures or re-sightings.
Journal ArticleDOI

Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy

TL;DR: The bootstrap is extended to other measures of statistical accuracy such as bias and prediction error, and to complicated data structures such as time series, censored data, and regression models.
Journal ArticleDOI

Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness

TL;DR: A series of common pitfalls in quantifying and comparing taxon richness are surveyed, including category‐subcategory ratios (species-to-genus and species-toindividual ratios) and rarefaction methods, which allow for meaningful standardization and comparison of datasets.
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