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Wild animals usually respond to different landscape features at different spatial scales. The adoption of multi-scale modeling frameworks in habitat suitability modeling studies have been shown to improve model performance and provide greater insights into relationships between species and habitat components. Although the advantage of multi-scale modeling, the implementation of this framework lagged considerably. In the present study, we used a multi-scale approach to assess the habitat suitability for the globally endangered giant panda (Ailuropoda melanoleuca) in Qionglai mountain range, Sichuan, China in an effort to provide improved species-environment relationships with the aim of informing conservation efforts. The occurrence data collected from the Fourth National Giant Panda Survey and a presence-only, multi-scale Maxent approach were used to model habitat suitability for giant pandas. Our results showed that the optimal scale identified for each environmental variable varied, and most variables were strongly related to giant panda habitat suitability at a relatively fine-scale (≤ 2000 m). Multi-scale models outperformed their analogous single-scale counterparts with respect to discrimination and predictive ability. Additionally, there were significant differences in spatial predictions between multi-scale and single-scale model. This study reveals the multi-scale response of giant pandas to environmental features and confirms the advantage of multi-scale habitat modeling. Therefore, it is necessary and beneficial to take scale dependence into consideration in future habitat suitability modeling for the giant panda.

The datasets and code presented in this article are related to the research article entitled “Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets”1. The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes. Keywords: Bioclimatic variables, Biodiversity indicators, Eco-geographic predictors, Economic plants, Occurrence data

The amino acid content of foods commonly consumed by specific populations is rarely measured systematically, especially if we take into account the different ways that foods are usually processed or cooked. The objective of this study is to evaluate the amino acid profile of the representative foods on the Portuguese diet, with a particular focus on indispensable amino acids. We also aim to assess the amino acid intake of the population and the most common sources of amino acids within the Portuguese diet. To achieve these goals, the amino acid intake of Portuguese adults was assessed combining data of food consumption with the food analysis data from samples collected according to the Total Diet Study methodologies. Results of the amino acid profiles of food groups typically consumed in Portugal are reported in this paper. We found that red meat consumption is the most common source of amino acids of the Portuguese population, followed by white meat and fish. The main contribution of individual portions to the recommended intakes of essential amino acids were: cheese, 88%; red meat, 83%; fatty fish, 81% and seitan with 74%. This data could be used to show alternative amino acid sources within commonly consumed foods. Highlights: Amino acid content of foods included in Portuguese Total Diet Study was analysed; Red meat is the major source of dietary amino acids among Portuguese adults; Fatty fish and white and red meat provide the highest contribution to %RI of EAA. The scientific work was funded by the TDS Project, funded by European Union Horizon 2020 research and innovation programme under grant agreement No 739568. GB Gonzales is a postdoctoral fellow of the Research Foundation Flanders (FWO). We thank the FCT - MCTES for its financial support via the project UID/EMS/00667/2019 (UNIDEMI). info:eu-repo/semantics/publishedVersion

High-quality data are indispensable for research and management in biodiversity conservation. Nevertheless, errors in biodiversity data must be removed before they can be used with confidence. In this study, we have developed a workflow for cleaning occurrence data archived in various biodiversity databases. The workflow allows researchers and practitioners to identify taxonomic and geographic errors in millions of records in an automatic, reproducible, and transparent manner. It also allows users to correct several types of taxonomic and geographic errors. We applied the workflow to clean global tree occurrence records. The results showed that among the 30,242,556 occurrence records of 58,034 species extracted from eight databases, only 8,624,319 (28.5%) records of 22,766 (39.2%) species were classified as high quality after running through the workflow. Inaccurate and non-standard taxon names appeared as a more severe problem than geographical errors that people are most familiar with. The workflow developed in this study can be easily adapted to clean occurrence records of other taxonomic groups, which allows researchers and practitioners to reduce uncertainties in their findings. Keywords: Data quality, Biodiversity, Data cleaning, Tree species, Conservation

We modeled summer and winter habitat suitability of Marco Polo argali in the Pamir Mountains in southeastern Tajikistan using these statistical algorithms: Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, and Multivariate Adaptive Regression Splines. Using sheep occurrence data collected from 2009 to 2015 and a set of selected habitat predictors, we produced summer and winter habitat suitability maps and determined the important habitat suitability predictors for both seasons. Our results demonstrated that argali selected proximity to riparian areas and greenness as the two most relevant variables for summer, and the degree of slope (gentler slopes between 0° to 20°) and Landsat temperature band for winter. The terrain roughness was also among the most important variables in summer and winter models. Aspect was only significant for winter habitat, with argali preferring south-facing mountain slopes. We evaluated various measures of model performance such as the Area Under the Curve (AUC) and the True Skill Statistic (TSS). Comparing the five algorithms, the AUC scored highest for Boosted Regression Tree in summer (AUC = 0.94) and winter model runs (AUC = 0.94). In contrast, Random Forest underperformed in both model runs.

Experimental information on the roles played by climatic factors in determining the ecology and distribution of insect species is scarce. This has stimulated the increasing use of the climatic characteristics of the localities in which the species are observed to derive predictions under different climatic scenarios (the so called species-distribution models or SDMs). This text reviews the main limitations of these correlative models when they are applied to organisms, such as insects, that are characterized by a high degree of collector bias and incompleteness. It is argued that SDMs must rely solely on presence information, rejecting the use of background or pseudoabsences, and that we are not predicting the future distribution of a species but exploring the future location of the climatic conditions in which a species was observed. The scarcity and bias of the available occurrence information in insects as well as our ignorance about the non-climatic factors delimiting species ranges forces us to be extremely careful. It is therefore desirable to avoid the use of central tendency measures reflecting supposed optimum niche conditions because they are particularly dependent on the quantity and biases of the occurrence information. The use of simple algorithms and procedures aimed at extracting information on environmental limits from the available occurrences would be more convenient in this case., This paper was funded by the project CGL2011-25544 of the Spanish Ministry of Economía y Competitividad. Special thanks to the referees and editors for their constructive suggestions.

Despite the recent focus on nitrogenous disinfection byproducts in drinking water, there is limited occurrence data available for many species. This paper analyses the occurrence of seven haloacetonitriles, three haloacetamides, eight halonitromethanes and cyanogen chloride in 20 English drinking water supply systems. It is the first survey of its type to compare bromine substitution factors (BSFs) between the haloacetamides and haloacetonitriles. Concentrations of the dihalogenated haloacetonitriles and haloacetamides were well correlated. Although median concentrations of these two groups were lower in chloraminated than chlorinated surface waters, median BSFs for both in chloraminated samples were approximately double those in chlorinated samples, which is significant because of the higher reported toxicity of the brominated species. Furthermore, median BSFs were moderately higher for the dihalogenated haloacetamides than for the haloacetonitriles. This indicates that, while the dihalogenated haloacetamides were primarily generated from hydrolysis of the corresponding haloacetonitriles, secondary formation pathways also contributed. Median halonitromethane concentrations were remarkably unchanging for the different types of disinfectants and source waters: 0.1 μg·mgTOC(-1) in all cases. Cyanogen chloride only occurred in a limited number of samples, yet when present its concentrations were higher than the other N-DBPs. Concentrations of cyanogen chloride and the sum of the halonitromethanes were not correlated with any other DBPs. The Department for Environment, Food and Rural Affairs (Defra) and the Drinking Water Inspectorate (DWI) for England and Wales are gratefully acknowledged for funding this work, as part of project DWI 70/2/268.

In this paper, we present an approach capable of extracting insights on ecosystem organization from merely occurrence (presence/absence) data. We extrapolate to the collective behavior by encapsulating some simplifying assumptions within a given set of constraints, and then examine their ecological implications. We show that by using the mean occurrence and co-occurrence of species as constraints, one is able to capture detailed statistics of a plant community distributed across a vast semiarid area of the United States. The approach allows us to quantify the species' effective couplings: Their frequencies exhibit a peak at zero and the minimal pairwise model is able to capture about 80% of the ecosystem structure. Our analysis reveals a relatively stronger impact of the species network on uncommon species and underscores the importance of species pairs experiencing positive couplings. Additionally, we study the associations among species and, interestingly, find that the frequencies of groups of different species, which the approach is able to capture. exhibit a power-law-like distribution. (C) 2009 Elsevier Ltd. All rights reserved.