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Como parte de mi doctorado en la Universidad de Córdoba, estoy inmersa en el proyecto DesFutur, financiado por la Fundación Biodiversidad. Mi investigación aborda la dinámica espacio-temporal de la expansión e impacto de especies de flora invasora, un aspecto crítico en el contexto de la desertificación en el sudeste de la Península Ibérica.

In our previous blog post, “MaxEnt Algorithm: Using Machine Learning to Improve Species Distribution Modeling”, we introduced the benefits of employing machine learning algorithms for enhancing the accuracy and precision of species distribution models.

La coexistencia armoniosa entre humanos y animales es un objetivo de suma importancia para la conservación de la biodiversidad. En la búsqueda de este objetivo, los científicos han desarrollado y aplicado una variedad de técnicas y herramientas innovadoras.

At Hub-Terra, we are dedicated to advancing our understanding of species and ecosystems through a diverse array of cutting-edge technologies and methodologies. Our team commits to unearthing new insights and providing actionable information that can drive conservation strategies forward.

The collaborative project with IDAF-Forestry Consulting focuses on mitigating human-wildlife conflict in Limpopo National Park through a comprehensive approach. By combining advanced modeling techniques, robust data collection methods, and collaboration with local agents, we aim to predict the distribution of large herbivores and identify areas at a higher risk of conflict. Furthermore, evaluating existing mitigation strategies will provide valuable insights into their effectiveness, allowing us to refine and develop more successful measures. Through our collaborative efforts, we strive to promote the coexistence of wildlife and human communities, fostering a sustainable balance within this remarkable natural habitat.

For years, scientists have used environmental variables such as temperature, precipitation, and topography to model species distributions. While these variables can provide useful information, they do not capture the full complexity of species-environment interactions.

Habitat suitability and distribution models are increasingly being used to inform conservation efforts, allowing scientists to identify priority areas for conservation and predict the impacts of land use changes on species distributions.

MaxEnt, short for Maximum Entropy, is a machine learning algorithm that has been widely used in species distribution modeling. It is based on the principle of maximum entropy, which states that the probability distribution that best represents our current knowledge about a system is the one that is maximally uncertain, subject to constraints imposed by that knowledge.