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pro vyhledávání: '"Hartig, Florian"'
Autor:
Hartig, Florian, Abrego, Nerea, Bush, Alex, Chase, Jonathan M., Guillera-Arroita, Gurutzeta, Leibold, Mathew A., Ovaskainen, Otso, Pellissier, Loïc, Pichler, Maximilian, Poggiato, Giovanni, Pollock, Laura, Si-Moussi, Sara, Thuiller, Wilfried, Viana, Duarte S., Warton, David I., Zurell, Damaris, Yu, Douglas W.
Publikováno v:
Trends in Ecology & Evolution, 2024
New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales. The potential of these novel community data to standardize community
Externí odkaz:
http://arxiv.org/abs/2401.10860
Autor:
Pichler, Maximilian, Hartig, Florian
Supervised machine learning (ML) and deep learning (DL) algorithms excel at predictive tasks, but it is commonly assumed that they often do so by exploiting non-causal correlations, which may limit both interpretability and generalizability. Here, we
Externí odkaz:
http://arxiv.org/abs/2306.10551
Deep Neural Networks (DNN) have become a central method in ecology. Most current deep learning (DL) applications rely on one of the major deep learning frameworks, in particular Torch or TensorFlow, to build and train DNN. Using these frameworks, how
Externí odkaz:
http://arxiv.org/abs/2303.09599
Autor:
Hartig, Florian, Barraquand, Frédéric
Publikováno v:
Trends in Ecology & Evolution, 2022
In a recent opinion article, Muff et al. recapitulate well-known objections to the Neyman-Pearson Null-Hypothesis Significance Testing (NHST) framework and call for reforming our practices in statistical reporting. We agree with them on several impor
Externí odkaz:
http://arxiv.org/abs/2205.13488
Autor:
Pichler, Maximilian, Hartig, Florian
Publikováno v:
Methods in Ecology and Evolution, 2023
1. The popularity of Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opaque, and their
Externí odkaz:
http://arxiv.org/abs/2204.05023
Autor:
Käber, Yannek1,2 (AUTHOR) y.kaeber@posteo.de, Hartig, Florian3 (AUTHOR), Bugmann, Harald2 (AUTHOR)
Publikováno v:
Geoscientific Model Development. 2024, Vol. 17 Issue 7, p2727-2753. 27p.
Autor:
Pichler, Maximilian, Hartig, Florian
Publikováno v:
Methods in ecology and evolution (2013), 12(11), 2159-2173
1. Joint Species Distribution models (JSDMs) explain spatial variation in community composition by contributions of the environment, biotic associations, and possibly spatially structured residual covariance. They show great promise as a general anal
Externí odkaz:
http://arxiv.org/abs/2003.05331
Identifying directed interactions between species from time series of their population densities has many uses in ecology. This key statistical task is equivalent to causal time series inference, which connects to the Granger causality (GC) concept:
Externí odkaz:
http://arxiv.org/abs/1909.00731
Autor:
Pichler, Maximilian, Boreux, Virginie, Klein, Alexandra-Maria, Schleuning, Matthias, Hartig, Florian
Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical es
Externí odkaz:
http://arxiv.org/abs/1908.09853
Autor:
Hartig, Florian
Publikováno v:
Oxford Bibliographies in Ecology, 2017
With the rise of computers, simulation models have emerged beside the more traditional statistical and mathematical models as a third pillar for ecological analysis. Broadly speaking, a simulation model is an algorithm, typically implemented as a com
Externí odkaz:
http://arxiv.org/abs/1812.09137