scholarly journals Too much of a good thing? A landscape-of-fear analysis for collared peccaries (Pecari tajacu) reveals hikers act as a greater deterrent than thorny or bitter food

2018 ◽  
Vol 96 (4) ◽  
pp. 317-324 ◽  
Author(s):  
Sonny S. Bleicher ◽  
Michael L. Rosenzweig

To study how wildlife perceive recreating humans, we studied the habitat selection of a human commensalist, the collared peccary (Pecari tajacu (Linnaeus, 1758)). We measured peccary activity patterns in an area of high human activity (Tumamoc Hill Desert Laboratory in Tucson, Arizona, USA) using a landscape-of-fear analysis. We examined whether the perception of risk from human activity interacted with the chemical (tannin) and mechanical (thorns) antipredator mechanisms of local plant species. The peccaries avoided food stations near a hiking trail. The population foraged less near houses, i.e., moderate human activity, than in the perceived safety of a small wadi. Plant defence treatments impacted the harvesting of food only in the safe zone, suggesting that risk trumps food selectivity. The strong effect of the hiking trail on habitat selection in this disturbance-loving species is an indicator of a much larger impact on sensitive species in conservation areas.

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0151473 ◽  
Author(s):  
Tianyang Zhang ◽  
Peng Cui ◽  
Chaoming Song ◽  
Wenwu Zhu ◽  
Shiqiang Yang

2014 ◽  
Vol 18 (26) ◽  
pp. 11
Author(s):  
Paula Gómez ◽  
Ellen Yi-Luen Do ◽  
Mario Romero

Computational spatial analyses play an important role in architectural design processes, providing feedback about spatial configurations that may inform design decisions. Current spatial analyses convey geometrical aspects of space, but aspects such as space use are not encompassed within the analyses, although they are fundamental for architectural programming. Through this study, we initiate the discussion of including human activity as an input that will change the focus of current computational spatial analyses toward a detailed understanding of activity patterns in space and time. We envision that the emergent insights will serve as guidelines for future evaluation of design intents motivated by spatial occupancy, since we –designers– mentally constructing a model of the situation and activities on it (Eastman, 2001).


2019 ◽  
Vol 8 (1) ◽  
pp. 45 ◽  
Author(s):  
Caglar Koylu ◽  
Chang Zhao ◽  
Wei Shao

Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the utility of computer vision algorithms for studying large-scale human activity patterns. In this article, we introduce an analytical framework that integrates a computer vision algorithm based on convolutional neural networks (CNN) with kernel density estimation to identify objects, and infer human activity patterns from geo-tagged photographs. To demonstrate our framework, we identify bird images to infer birdwatching activity from approximately 20 million publicly shared images on Flickr, across a three-year period from December 2013 to December 2016. In order to assess the accuracy of object detection, we compared results from the computer vision algorithm to concept-based image retrieval, which is based on keyword search on image metadata such as textual description, tags, and titles of images. We then compared patterns in birding activity generated using Flickr bird photographs with patterns identified using eBird data—an online citizen science bird observation application. The results of our eBird comparison highlight the potential differences and biases in casual and serious birdwatching, and similarities and differences among behaviors of social media and citizen science users. Our analysis results provide valuable insights into assessing the credibility and utility of geo-tagged photographs in studying human activity patterns through object detection and spatial analysis.


2019 ◽  
Vol 23 (4) ◽  
pp. 373-385 ◽  
Author(s):  
J. O. Waterman ◽  
L. A. D. Campbell ◽  
L. Maréchal ◽  
M. Pilot ◽  
B. Majolo

2013 ◽  
Vol 78 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Francesco Bisi ◽  
Mosé Nodari ◽  
Nuno Miguel Dos Santos Oliveira ◽  
Federico Ossi ◽  
Elisa Masseroni ◽  
...  

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