The wild turkeys of Mississippi do not have secrets for the 'machine learning' | Innovation

Have you ever searched for a house? If you were a wild turkey, you would be in constant movement. Always hunting for better pastures, across the face of East America, but without actually migrating. Locating human populations is relatively simple, starting from the gazillion records that characterize the Orwellian era in which we live. Turkeys – and other wild animals – do not register, nor do they carry a smartphone, nor do they make card payments.

The ability to predict the directions taken by wild species in their wandering is key to the study of their behavior and, in cases of threatened species, to establish more effective protection measures. To make these forecasts, it is necessary to monitor and quantify the weight of the variables that lead the animal to establish the limits of its habitat in one way or another: availability of resources, meteorology, changes in the environment … Therefore, what we are going to tell you it is not turkey moque (wink, wink).

  • What is the machine learning and find them

How does this work? Guessing the whereabouts of wild turkeys is possible thanks to combinations of machine learning algorithms like those used by six researchers from the University of Mississippi, the University of Georgia, the University of New York, Jackson's Wildlife, Fisheries and Parks Department ( Mississippi) and the Forest Association of Mississippi. "Studies of the spatio-temporal distribution of resources that maintain organisms are essential to understand the dynamics of animal populations, including avian species, through space and time," they explain in the study resulting from their experiment.

The machine learning is a promising tool for modeling distributions of species

To determine the course of the turkeys is taken into consideration what is known as the index of sustainability of the habitat, "the probability of a species occupying or using said habitat", or what is the same, the appetizing that will result the environment, taking into account the conditions that converge in it. "Habitat sustainability prediction models predict the likelihood of animals appearing in a spatial location, using environmental variables." In this way, the conditions that can lead to the arrival of certain species are quantified. "Animals choose habitat based on their ecological and physiological needs and the availability of resources," the researchers say.

"The machine learning It is a promising tool for the modeling of species distributions, "the American researchers point out, and its use gains impact when working with very large dimensions or endangered species, of which there is even less information. of algorithms machine learning could facilitate exploratory studies of the effects that environmental factors have on the spatial distribution and sustainability of wildlife habitats. "

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