Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios

The impact of climate change on the habitat suitability for large brown trout (Salmo trutta L.) was studied in a segment of the Cabriel River (Iberian Peninsula). The future flow and water temperature patterns were simulated at a daily time step with M5 models' trees (NSE of 0.78 and 0.97 respectively) for two short-term scenarios (2011 2040) under the representative concentration pathways (RCP 4.5 and 8.5). An ensemble of five strongly regularized machine learning techniques (generalized additive models, multilayer perceptron ensembles, random forests, support vector machines and fuzzy rule base systems) was used to model the microhabitat suitability (depth, velocity and substrate) during summertime and to evaluate several flows simulated with River2D©. The simulated flow rate and water temperature were combined with the microhabitat assessment to infer bivariate habitat duration curves (BHDCs) under historical conditions and climate change scenarios using either the weighted usable area (WUA) or the Boolean-based suitable area (SA). The forecasts for both scenarios jointly predicted a significant reduction in the flow rate and an increase in water temperature (mean rate of change of ca. −25% and +4% respectively). The five techniques converged on the modelled suitability and habitat preferences; large brown trout selected relatively high flow velocity, large depth and coarse substrate. However, the model developed with support vector machines presented a significantly trimmed output range (max.: 0.38), and thus its predictions were banned from the WUA-based analyses. The BHDCs based on the WUA and the SA broadly matched, indicating an increase in the number of days with less suitable habitat available (WUA and SA) and/or with higher water temperature (trout will endure impoverished environmental conditions ca. 82% of the days). Finally, our results suggested the potential extirpation of the species from the study site during short time spans.The study has been partially funded by the IMPADAPT project (CGL2013-48424-C2-1-R) - Spanish MINECO (Ministerio de Economia y Competitividad) - and FEDER funds and by the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment). We are grateful to the colleagues who worked in the field and in the preliminary data analyses, especially Juan Diego Alcaraz-Henandez, David Argibay, Aina Hernandez and Marta Bargay. Thanks to Matthew J. Cashman for the academic review of English. Finally, the authors would also to thank the Direccion General del Agua and INFRAECO for the cession of the trout data. The authors thank AEMET and UC by the data provided for this work (dataset Spain02).Muñoz Mas, R.; López Nicolás, AF.; Martinez-Capel, F.; Pulido-Velazquez, M. (2016). Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios. Science of the Total Environment. 544:686-700. https://doi.org/10.1016/j.scitotenv.2015.11.14768670054

Using machine learning to estimate herbage production and nutrient uptake on Irish dairy farms

peer-reviewedNutrient management on grazed grasslands is of critical importance to maintain productivity levels, as grass is the cheapest feed for ruminants and underpins these meat and milk production systems. Many attempts have been made to model the relationships between controllable (crop and soil fertility management) and noncontrollable influencing factors (weather, soil drainage) and nutrient/productivity levels. However, to the best of our knowledge not much research has been performed on modeling the interconnections between the influencing factors on one hand and nutrient uptake/herbage production on the other hand, by using data-driven modeling techniques. Our paper proposes to use predictive clustering trees (PCT) learned for building models on data from dairy farms in the Republic of Ireland. The PCT models show good accuracy in estimating herbage production and nutrient uptake. They are also interpretable and are found to embody knowledge that is in accordance with existing theoretical understanding of the task at hand. Moreover, if we combine more PCT into an ensemble of PCT (random forest of PCT), we can achieve improved accuracy of the estimates. In practical terms, the number of grazings, which is related proportionally with soil drainage class, is one of the most important factors that moderates the herbage production potential and nutrient uptake. Furthermore, we found the nutrient (N, P, and K) uptake and herbage nutrient concentration to be conservative in fields that had medium yield potential (11 t of dry matter per hectare on average), whereas nutrient uptake was more variable and potentially limiting in fields that had higher and lower herbage production. Our models also show that phosphorus is the most limiting nutrient for herbage production across the fields on these Irish dairy farms, followed by nitrogen and potassium

Ensemble Methods in Environmental Data Mining

Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an individual model. The study presented in this chapter focuses on several ensemble strategies in addition to the standard single classifiers such as decision tree, naive Bayes, support vector machine, and k-nearest neighbor (KNN), popularly used in literature. This is the first study that compares four ensemble strategies for environmental data mining: (i) bagging, (ii) bagging combined with random feature subset selection (the random forest algorithm), (iii) boosting (the AdaBoost algorithm), and (iv) voting of different algorithms. In the experimental studies, ensemble methods are tested on different real-world environmental datasets in various subjects such as air, ecology, rainfall, and soil

Muestreo de pseudo-ausencias en modelos de distribución de especies y transferibilidad en condiciones de cambio climático

186 p.Los Modelos de Distribución de Especies (SDMs), son herramientas estadísticas utilizadas para la generación de predicciones probabilísticas de la presencia de poblaciones de especies en el espacio geográfico (mapas de idoneidad de hábitat). Dada la amenaza que supone el cambio climático, una aplicación popular de estos modelos es la proyección futura de las distribuciones potenciales de las especies con el fin de evaluar temas claves en la conservación del medio ambiente. Sin embargo, hay fuentes importantes de incertidumbre que afectan la credibilidad de las predicciones. Entre ellas, en esta Tesis se destacan dos, la elección del algoritmo de modelización y la utilización de datos de pseudo-ausencia. Para ello se analiza el muestreo de pseudo-ausencias como un factor determinante para caracterizar la estabilidad y transferibilidad de los SDMs en condiciones de cambio climático, mediante la evaluación de la incertidumbre en conjuntos de predicciones futuras. Además, se ha desarrollado una herramienta de modelización que implementa diferentes técnicas para generar datos de pseudo-ausencia y analizar la incertidumbre de las predicciones, dirigidos a producir estimaciones óptimas de la idoneidad de hábitats futuros y facilitar el acceso y preparación de datos climáticos

Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004)

The invasion of freshwater ecosystems is a particularly alarming phenomenon in the Iberian Peninsula. Habitat suitability modelling is a proficient approach to extract knowledge about species ecology and to guide adequate management actions. Decision-trees are an interpretable modelling technique widely used in ecology, able to handle strongly nonlinear relationships with high order interactions and diverse variable types. Decision-trees recursively split the input space into two parts maximising child node homogeneity. This recursive partitioning is typically performed with axis-parallel splits in a top-down fashion. However, recent developments of the R packages oblique.tree, which allows the development of oblique split-based decision-trees, and evtree, which performs globally optimal searches with evolutionary algorithms to do so, seem to outperform the standard axis-parallel top-down algorithms; CART and C5.0. To evaluate their possible use in ecology, the two new partitioning algorithms were compared with the two well-known, standard axis-parallel algorithms. The entire process was performed in R by simultaneously tuning the decision-tree parameters and the variables subset with a genetic algorithm and modelling the presence-absence of the Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004), an invasive fish species that has spread across the Iberian Peninsula. The accuracy and complexity of the trees, the modelled patterns of mesohabitat selection and the variables importance were compared. None of the new R packages, namely oblique.tree and evtree, outperformed the C5.0 algorithm. They rendered almost the same decision-trees as the CART algorithm, although they were completely interpretable they performed from four to eight partitions in comparison with C5.0, which resulted in a more complex structure with 17 partitions. Oblique.tree proved to be affected by prevalence and it does not include the possibility of weighting the observations, which potentially discourage its actual use. Although the use of evtree did not suggest a major improvement compared with the remaining packages, it allowed the development of regression trees which may be informative for additional modelling tasks such as abundance estimation. Looking at the resulting decision-trees, the optimal habitats for the Iberian gudgeon were large pools in lowland river segments with depositional areas and aquatic vegetation present, which typically appeared in the form of scattered macrophytes clumps. Furthermore, Iberian gudgeon seem to avoid habitats characterised by scouring phenomena and limited vegetated cover availability. Accordingly, we can assume that river regulation and artificial impoundment would have favoured the spread of the Iberian gudgeon across the entire peninsula.The study has been partially funded by the national Research project IMPADAPT (CGL2013-48424-C2-1-R) with MINECO (Spanish Ministry of Economy) and Feder funds and by the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment). This study was also supported in part by the University Research Administration Center of the Tokyo University of Agriculture and Technology. Finally, we are grateful to the colleagues who worked in the field data collection, especially Juan Diego Alcaraz-Henandez, Rui M. S. Costa and Aina Hernandez.Muñoz Mas, R.; Fukuda, S.; Vezza, P.; Martinez-Capel, F. (2016). Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004). Ecological Informatics. 34:22-34. https://doi.org/10.1016/j.ecoinf.2016.04.011S22343

Spatializing the Soil-Ecological Factorial: Data Driven Integrated Land Management Tools

Soils form the dynamic interface of many processes key to the function of terrestrial ecosystems. Many soil properties both influence and are influenced by activity of flora and fauna. Interactions between soils, biota, and climate determine the potential ecosystem services that a given unique ecological site (ES) can support, and how resilient a site is to various pressures and disturbances. Soil data are needed to fully understand how these factors interact, but because this data is difficult to obtain, existing soil maps are sometimes not detailed enough to fully explore relationships. Environmental raster GIS data layers were used to increase the detail of maps by representing soil forming factors and associated ecological pedomemory legacies important to understanding ecological potential. This dissertation presents methods and tools to help create these new soil maps at appropriate resolution and theme for field scale assessment of ecological sites that enable land managers to plan and implement appropriate management decisions.;USDA-NRCS soil surveys were disaggregated to higher resolution maps using a semi-automated expert training routine to implement a random forest classification model. This transformed soil map polygons of variable thematic and spatial resolution (soil map unit concepts) to a consistent 30-meter raster grid of unified theme (soil taxa). Disaggregated maps (DM) showed highly variable accuracy (25--75% overall validation accuracy) that mirrored that of the original soil surveys evaluated in Arizona (AZ) and West Virginia (WV). However, disaggregated maps expressed the soil data at a much more detailed spatial scale with a more interpretable legend. The WV surveys exhibited much lower accuracy than the AZ survey evaluated. This lower accuracy in WV is likely due to the forested setting and highly dissected landscape, two factors that create more intrinsic soil variability that is harder to explain with spatial covariates.;Ecological site descriptions (ESD) document soil-ecosystem groups that produce unique amounts and types of biological constituents and respond similarly to disturbance and environmental variation. ESD are linked to soil map unit components in USDA-NRCS soil surveys and are used as the basis for land management planning on rangelands and forestlands. The component level connection makes DM a good way to spatialize ESD because both are spatially represented at the same thematic level, whereas conventional soil maps have polygons that often have multiple components linked to a delineation.;However, in the evaluation of mapping ESD via DM, the DM turned out not to document the key difference in spodic soil properties that distinguished the important ecotone between northern hardwood and alpine red spruce conifer ESDs in Pocahontas and Randolph counties, WV. So, to adjust, spodic soil properties were mapped directly using digital soil mapping approaches. A strong spatial model of spodic soil morphology presence was developed from a random forest probability model and showed correspondence to red spruce and hemlock occurrences in local historic land deed witness trees from records between 1752 and 1899. From this result, areas with spodic soil properties were assumed to be associated with historic red spruce communities, although 68% of those areas in the WV study area are currently under hardwood cover. This would seem to indicate that hardwoods have encroached on the historic extent of spruce, which is consistent with other recent studies. O-horizon thickness was also observed to be one cm thicker for every 10% greater importance value of red spruce or hemlock versus that of hardwood species at field sites. From these observations, it was calculated conservatively that at least 3.74-6.62 Tg of C have likely been lost from red spruce influenced ecological sites in WV due to historic disturbance related conversions of forest to hardwood composition. These results highlight the value of working within a soil-ecological factorial framework (e.g. an ESD) to contextualize land management options and potential derived services or negative consequences of each available action