An exploratory classification of ecological incubator environments in Wales

School of Managemen

Bayesian analysis of the multivariate dependence of three transition water ecosystem classifications

The Water Framework Directive (WFD) recognizes benthic macroinvertebrates as a good biological quality element for transitional waters as they are the most exposed to natural variability patterns characteristic of these ecosystems, due to their life cycles and space-use behavior. Here, we address the ecological status classification issue for three lagoons in Apulia, using benthic macroinvertebrates and three proposed multimetric indices (namely M-AMBI, BITS and ISS), likely to respond differently to different sources of stress and natural variability. Lagoon classification is based on discretization by standard classification boundaries with only partial consideration of the natural variability of ecosystem properties and possible inaccuracies of the classification procedures. In order to investigate the possible contrasting behavior of the three classifications, we propose Bayesian hierarchical models in which the multimetric indices and their discrete counterparts are jointly modeled as function of abiotic covariates, external anthropogenic pressures indicators and spatio-temporal effects

Speciation of chilean Rhinocryptidae (Avian) based on their behaviour

The current classification of the chilean representatives of the passerine family Rhinocryptidae includes eight species. Three of them contain subspecies that don't exhibit clear differences. Moreover, differences among two lineages of _Scytalopus_ genera and two species of _Pteroptochos_ are cryptic. We propose a new methodology based on ecological and behavioural patterns in order to understand the concept of speciation in this group of birds. According to our results, we postulate that there is not a cut criteria to establish differences among three sister lineages of current classification. This way the methodology developed by us does not allow to establish divergence for a given common ancestor. Our methodology allows to establish comparison among previously determined phylogenetic lineages. Our results show how when integrating behaviour and ecological terms as biological traits next to morphological characters of the plumage, it allows us to conclude that there is decrease of the distances among sister lineages in the cluster tree

Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hopkinson, B. M., King, A. C., Owen, D. P., Johnson-Roberson, M., Long, M. H., & Bhandarkar, S. M. Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks. PLoS One, 15(3), (2020): e0230671, doi: 10.1371/journal.pone.0230671.Coral reefs are biologically diverse and structurally complex ecosystems, which have been severally affected by human actions. Consequently, there is a need for rapid ecological assessment of coral reefs, but current approaches require time consuming manual analysis, either during a dive survey or on images collected during a survey. Reef structural complexity is essential for ecological function but is challenging to measure and often relegated to simple metrics such as rugosity. Recent advances in computer vision and machine learning offer the potential to alleviate some of these limitations. We developed an approach to automatically classify 3D reconstructions of reef sections and assessed the accuracy of this approach. 3D reconstructions of reef sections were generated using commercial Structure-from-Motion software with images extracted from video surveys. To generate a 3D classified map, locations on the 3D reconstruction were mapped back into the original images to extract multiple views of the location. Several approaches were tested to merge information from multiple views of a point into a single classification, all of which used convolutional neural networks to classify or extract features from the images, but differ in the strategy employed for merging information. Approaches to merging information entailed voting, probability averaging, and a learned neural-network layer. All approaches performed similarly achieving overall classification accuracies of ~96% and >90% accuracy on most classes. With this high classification accuracy, these approaches are suitable for many ecological applications.This study was funded by grants from the Alfred P. Sloan Foundation (BMH, BR2014-049; https://sloan.org), and the National Science Foundation (MHL, OCE-1657727; https://www.nsf.gov). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript

A regulation-based classification system for marine protected areas: A response to Dudley et al. [9]

Dudley et al. [9] commented on our paper [11], arguing that the current IUCN objective-based categorization of protected areas, which is also used in marine protected areas (MPAs), should not be abandoned and replaced by the new regulation-based classification system [11]. Here we clarify that we do not advocate replacing the current IUCN categories, but highlight the benefits of using both the objective-based IUCN categories and the new regulation-based classification when applied to MPAs. With an increasing number of MPA types being implemented, most of them multiple-use areas zoned for various purposes, assessing ecological and socio-economic benefits is key for advancing conservation targets and policy objectives. Although the IUCN categories can be used both in terrestrial and marine systems, they were not designed to follow a gradient of impacts and there is often a mismatch between stated objectives and implemented regulations. The new regulation-based classification system addresses these problems by linking impacts of activities in marine systems with MPA and zone classes in a simple and globally applicable way. Applying both the IUCN categories and the regulation based classes will increase transparency when assessing marine conservation goals.ERA-Net BiodivERsA project "BUFFER Partially protected areas as buffers to increase the linked social ecological resilience"; national funders ANR (France); FCT (Portugal); FOR-MAS (Sweden); SEPA (Sweden); RCN (Norway); project BUFFER; Fernand Braudel IFER fellowship (Fondation Maison des Sciences de l'Homme); Fundacao para a Ciencia e a Tecnologia (FCT) [UID/MAR/04292/2013

Use of habitat suitability modeling in the integrated urban water system modeling of the Drava River (Varazdin, Croatia)

The development of practical tools for providing accurate ecological assessment of rivers and species conditions is necessary to preserve habitats and species, stop degradation and restore water quality. An understanding of the causal mechanisms and processes that affect the ecological water quality and shape macroinvertebrate communities at a local scale has important implications for conservation management and river restoration. This study used the integration of wastewater treatment, river water quality and ecological assessment models to study the effect of upgrading a wastewater treatment plant (WWTP) and their ecological effects for the receiving river. The WWTP and the water quality and quantity of the Drava river in Croatia were modelled in the software WEST. For the ecological modeling, the approach followed was to build habitat suitability and ecological assessment models based on classification trees. This technique allows predicting the biological water quality in terms of the occurrence of macroinvertebrates and the river status according to ecological water quality indices. The ecological models developed were satisfactory, and showed a good predictive performance and good discrimination capacity. Using the integrated ecological model for the Drava river, three scenarios were run and evaluated. The scenario assessment showed that it is necessary an integrated approach for the water management of the Drava river, which considers an upgrading of the WWTP with Nitrogen and Phosphorous removal and the treatment of other diffuse pollution and point sources (including the overflow of the WWTP). Additionally, if an increase in the minimum instream flow after the dams is considered, a higher dilution capacity and a higher self-cleaning capability could be obtained. The results proved that integrated models like the one presented here have an added value for decision support in water management. This kind of integrated approach is useful to get insight in aquatic ecosystems, for assessing investments in sanitation infrastructure of urban wastewater systems considering both, the fulfilling of legal physical chemical emission limits and the ecological state of the receiving waters

Evaluation of Minnesota Geographic Classifications Based on Caddisfly (Trichoptera) Data

The ability to partition the variation of faunal assemblages into homogenous units valuable for biomonitoring is referred to as classification strength (CS). In this study, the CSs of three types of geographic classifications: watershed basin, ecological region, and caddisfly region, were compared based on 248 light trap samples of adult caddisflies collected in Minnesota during 1999–2001. The effect on CS of three different levels of taxonomic resolution: family, genus, and species, was also assessed. Primary (broadest possible) a priori classification by watershed basin and ecological region had a lower CS than did secondary classification by these regions. Caddisfly region, an a posteriori classification based directly on caddisfly distribution data, had nearly twice the CS of any a priori classification. CS decreased approximately 20% with a decrease in taxonomic resolution from species to genus, and from genus to family. These results suggest that geographic classification, spatial scale, and taxonomic resolution are all important factors to consider when sampling aquatic insects, and that widely used a priori geographic classifications are not the ideal units for sampling the aquatic biota

A Survey of Economic-Ecological Models

This paper reports on a survey of economic-ecological models conducted by a research team from the Institute for Environmental Studies, Free University, Amsterdam (IvM) in cooperation with and supported by the International Institute for Applied Systems Analysis (IIASA), Laxenburg Austria. The paper attempts to describe the state-of-the-art in economic-ecological modeling as derived from this survey. Various classifications have been used to this end. These are: -- Classification of economic-ecological policy issues -- Classification of phases in economic-ecological policy analysis -- Classification of mathematical models used in the interface between economics and ecology -- Classification concerning the internal structure of mathematical models -- Classification concerning the relationships between economic and ecological models. A combination of these classifications provides a framework for evaluating economic-ecological modeling. Special attention is paid to problems that economic-ecological modeling is still facing today, and some remarks are made on the perspectives of economic-ecological modeling. In an extensive Appendix a catalogue of model summaries is presented. These summaries give a non-mathematical description of the model structure, model properties and the policy issue for each of the documented survey models. References to the model documentation are included. The essence of this report will appear shortly in a state of the art book, edited by the authors of this paper. In addition, it will include introductions to economic, ecological and environmental modeling and analysis of integration techniques between economic and ecological submodels. The book will further contain a number of chapters presenting evaluations of models considered representative for those used in various fields of policy and management. The book has been designed to present a coherent picture of the origins, state and future of economic-ecological modeling

Mapping cultivated area in West Africa using modis imagery and agroecological stratification

To predict and respond to famine and other forms of food insecurity, different early warning systems are using remote analyses of crop condition and agricultural production, using satellite-based information. To improve these predictions, a reliable estimation of the cultivated area at national scale must be carried out. In this study, we develop a methodology for extracting cultivated domain based on their temporal behaviour as captured in time-series of moderate resolution remote sensing MODIS images. We also used higher resolution SPOT and LANDSAT images for identifying cultivated areas used in training. We tested this methodology in Senegal and Mali at national scale. Both studied areas were stratified in homogeneous areas from an ecological and a remote sensing point of view, to reduce the land surface reflectance variability in the dataset in order to improve the classification efficiency. A spatiotemporal (K-means) classification was finally made on the MODIS NDVI time series, inside each of the agro-ecological regions For Senegal, we obtained an updated map of crop area with a better resolution than the USAID map (which is 1 km resolution) and with a nomenclature more specific of the Senegal region than suggested in the POSTEL map. For Mali, the results showed that MODIS data set can provide a completely satisfactory representation of the cultivated domain in one FEWS zone, in combination with external data. Results at national scale are being processed and will be presented at the conference. (Résumé d'auteur