Biology subjects

Lahoz-Monfort, J. J.

Publications and source records attributed to Lahoz-Monfort, J. J..

3 recordsLinked to original sources

bioRxiv2025

The Freshwater Sounds Archive

Freshwater ecosystems are full of underwater sounds produced by amphibians, aquatic arthropods, reptiles, plants, fishes, and methane bubbles escaping from the sediment. Although much headway has been made in recent years investigating the overall soundscapes of various freshwater ecosystems around the world, there remains a significant knowledge gap in our collective inability to accurately and reliably link recorded sounds with the species that produced them. Here, we present The Freshwater Sounds Archive, a new global initiative, which seeks to address this knowledge gap by collating species-specific freshwater sound recordings into a publicly available database. By means of metadata collection, we also present a snapshot of the species studied, the recording equipment, and recording parameters used by freshwater ecoacousticians globally. In total, 61 entries were submitted to the archive between the 4th of March 2023 and the 30th of April 2025, representing 16 countries and 6 continents. The most numerous taxonomic group was arthropods (29 entries), followed by fishes (14 entries), amphibians (10 entries), macrophytes (7 entries), and a freshwater mollusk (1 entry). The majority of the submissions were from European countries (27 entries), of which the United Kingdom was the most represented with 14 entries. The next most represented region was North America (11 entries), followed by South America (8 entries), Oceania and Asia (5 entries each), Africa (3 entries), and the Middle East and Central America with 1 entry each. The global south, polar regions, and areas with an elevation >500 m (asl) were underrepresented. The field of freshwater ecoacoustics to date has largely focused on the analysis of sound types due to a current lack of knowledge of species-specific sounds. The Freshwater Sounds Archive presents an opportunity to move beyond the sound type approach, and towards an approach with higher taxonomic resolution, ultimately resulting in species-specific descriptions. Furthermore, The Freshwater Sounds Archive will provide freshwater ecoacousticians with one of the main tools required to start creating annotated training datasets for machine learning models from soundscape recordings by referring to known species sounds present in the archive. In the long-term, this will result in the automatic detection and classification of species-specific freshwater sounds from soundscape recordings, such as indicator, invasive, and endangered species.

ecology↗

bioRxiv2020

Modelling species presence-only data with random forests

O_LIThe Random Forest (RF) algorithm is an ensemble of classification or regression trees, and is a widely used and high-performing machine learning technique. It is increasingly used for species distribution modelling (SDM). Many researchers use implementations of RF in the R programming language with default parameters to analyse species presence-only data together with background samples. However, there is good evidence that RF with default parameters does not perform well with such species "presence-background" data. This is often attributed to the typical disparity between the number of presence and background samples also known as class imbalance, and several solutions have been proposed. C_LIO_LIHere, we first set the context: the background sample should be large enough to represent all environments in the region. We then aim to understand the drivers of poor performance of RF with presence-background data, and explain, test and evaluate suggested solutions. Using simulated and real species data, we compare performance of default RF with other weighting and sampling approaches. C_LIO_LIWe show that class overlap is an important driver of poor performance, alongside class imbalance. The results demonstrate clear evidence of improvement in the performance of RFs when class imbalance is explicitly managed by sampling methods or when the overfitting commonly associated with overlapping classes is avoided by forcing shallow trees. C_LIO_LIPresence-background data is a particular version of class imbalance in which class overlap is highly likely and extreme imbalance exists. Without compromising the environmental representativeness of the sampled background, we show several approaches to fitting RF that ameliorate the effects of imbalance and overlap, and allow excellent predictive performance. Understanding the problems of RF in presence-background data allows new insights into how best to fit models, and should guide future efforts to best deal with such data. C_LI

ecology↗

bioRxiv2018

blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models

O_LIWhen applied to structured data, conventional random cross-validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection.\nC_LIO_LIWe present the R package blockCV, a new toolbox for cross-validation of species distribution modelling.\nC_LIO_LIThe package can generate spatially or environmentally separated folds. It includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data. It also offers interactive graphical capabilities for creating spatial blocks and exploring data folds.\nC_LIO_LIPackage blockCV enables modellers to more easily implement a range of evaluation approaches. It will help the modelling community learn more about the impacts of evaluation approaches on our understanding of predictive performance of species distribution models.\nC_LI

ecology↗