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This study investigated the relationships between benthic macroinvertebrates and environmental variables in the Vam Co River system in southern Vietnam. Field surveys were conducted four times from May to November 2023 to evaluate seasonal variations in macroinvertebrate diversity and water quality. The river, characterized by moderate pollution levels, elevated nutrient concentrations, and substrates mainly composed of fine and coarse sand, significantly influences the distribution and abundance of benthic macroinvertebrates. A total of 32 species were identified, with bivalves and crustaceans being the most prevalent groups during the sampling period. Benthic macroinvertebrate densities ranged from 28 to 167 individuals per square meter, reflecting habitat quality linked to the substrate type. Biodiversity, assessed using the Shannon–Wiener index (H’), varied from 1.51 to 2.94, whereas the average tolerance score per individual (ATSPI) ranged from 37 to 51, indicating moderate-to-good ecological health. Species richness was positively associated with pH, total suspended solids (TSS), and dissolved oxygen (DO), suggesting that these factors support diverse communities. Conversely, ATSPI scores, which indicate pollution tolerance, were positively correlated with biochemical oxygen demand (BOD5), total nitrogen (T_N), and total phosphorus (T_P) and negatively correlated with pH, total suspended solids (TSS), and DO. These findings demonstrate the importance of benthic macroinvertebrates as bioindicators of river health and underscore the need for ongoing integrated monitoring and adaptive management to promote the sustainable conservation of the Vam Co River system.

The rapid and accurate assessment of regional soil salinity is crucial for effective salinization management. This study proposes an enhanced remote sensing inversion method by integrating both driving and response environmental variables to address lag effects and incomplete factor consideration in existing models. The Yellow River Delta, a coastal saline–alkaline region, was selected as the study area, where soil salinity-sensitive spectral parameters were derived from Sentinel-2 MSI imagery. Six environmental variables, including precipitation, distance from the sea, and soil moisture, were analyzed. Four scenarios were constructed: (1) using only spectral parameters; (2) spectral parameters with driving variables; (3) spectral parameters with response variables; and (4) combining both types. Four modeling methods were employed to assess inversion accuracy. The results show that incorporating either driving or response variables improved accuracy, with validation R2 increasing by up to 0.149 and RMSE decreasing by up to 0.097 when both were used. The suitable model, integrating soil moisture, distance from the sea, and chlorophyll content, achieved a calibration R2 of 0.813 and validation R2 of 0.722. These findings demonstrate that combining both driving and response variables enhances model performance and provides valuable insights for soil salinization management.

The lack of organisation in urban spaces plays a decisive role in the level of integration, communication and social bonds of the residents, impacting the citizens’ feelings of trust and security. Different personal variables and contextual characteristics have been associated with the fear of crime (FOC). The main objective of this study is to analyse how individual and social/environmental variables, and incivilities, predict crime against people and property, crime that has either happened or is feared to happen. Five hundred and fifty‐four residents (M = 43.82; SD = 18.38) in the Historic Centre of Porto (HCP), Portugal, answered 61 items of the Diagnosis of Local Security (DLS) Questionnaire. The results of this study show that in the most frequent crime category, 72% of occurrences represent crime against property. In the feared crime category, there is a preponderance of crime against people (61%). Age of the respondents predicted the most frequent and feared crime, while sex predicted the most feared crime only. Social/environmental variables, as well as incivilities, also predict the frequent and feared crime in two typologies, i.e., crime against people and crime against property. Practical implications to reduce FOC and areas for further investigation are discussed.

Aim: Species distribution modelling, a family of statistical methods that predicts species distributions from a set of occurrences and environmental predictors, is now routinely applied in many macroecological studies. However, the reliability of evaluation metrics usually employed to validate these models remains questioned. Moreover, the emergence of online databases of environmental variables with global coverage, especially climatic, has favoured the use of the same set of standard predictors. Unfortunately, the selection of variables is too rarely based on a careful examination of the species' ecology. In this context, our aim was to highlight the importance of selecting ad hoc variables in species distribution models, and to assess the ability of classical evaluation statistics to identify models with no biological realism. Innovation: First, we reviewed the current practices in the field of species distribution modelling in terms of variable selection and model evaluation. Then, we computed distribution models of 509 European species using pseudo-predictors derived from paintings or using a real set of climatic and topographic predictors. We calculated model performance based on the area under the receiver operating curve (AUC) and true skill statistics (TSS), partitioning occurrences into training and test data with different levels of spatial independence. Most models computed from pseudo-predictors were classified as good and sometimes were even better evaluated than models computed using real environmental variables. However, on average they were better discriminated when the partitioning of occurrences allowed testing for model transferability. Main conclusions: These findings confirm the crucial importance of variable selection and the inability of current evaluation metrics to assess the biological significance of distribution models. We recommend that researchers carefully select variables according to the species' ecology and evaluate models only according to their capacity to be transfered in distant areas. Nevertheless, statistics of model evaluations must still be interpreted with great caution.

Phenological studies are fundamental for species management and conservation and support research into genetic improvement by providing insights into plant life cycles and their relationship with phenophases and environmental conditions. This study aimed to determine the environmental variables and their influence on the vegetative and reproductive phenological patterns of Hymenaea martiana Hayne. Field observations were carried out in five areas located in the municipality of Areia, Paraíba State, Brazil. Between May 2021 and April 2023, the phenological events of budding, senescence, flower bud, flowering, and fruiting were assessed every 30 days. Statistical analyses were circular to verify the occurrence of the phenophase periods, their concentration, seasonality, and duration, and Spearman correlations were used to test the relationships between meteorological variables during the period and the phenophases. The patterns of budding, senescence, flower bud, and flowering phenophases of H. martiana were seasonal, with an annual periodicity and a continuous fruiting pattern. The average dates of occurrence of the budding phenophase were August (area 2), October (areas 1, 4, and 5), and November (area 3). The average date for senescence was July (area 2) and August (areas 1 and 3–5). The average date for the flower bud phase was November (area 5) and December (areas 1–4). Flowering occurred in January (areas 3 and 4), March (area 2), and December (areas 1 and 5). Rainfall and temperature had a significant impact on the period and occurrence of vegetative and reproductive phenological events in H. martiana. Under highland marsh conditions, H. martiana fruit should be collected in October, November, and December.

This article aims to demonstrate that environmental variables can assume differentiated values over a given period and associate themselves to form configurations of different contexts. Knowing the possible configurations of the organizational environment, we are able to identify which indicators are most appropriate to measure innovation, thus meeting the basic condition to manage innovation: to measure accurately the phenomenon under analysis. Thus, with the empirical data analysis from Brazil and the states of Sao Paulo, Parana and Sergipe, we are able to highlight and characterize the different environmental configurations and their reflexes for the innovation measurement process. It should be emphasized that the environmental configuration appears as a relevant factor that must be considered in the process of measurement and management of innovation aiming at competitiveness.

Aim: To synthesize the species distribution modelling (SDM) literature to inform which variables have been used in MaxEnt models for different taxa and to quantify how frequently they have been important for species' distributions. Location: Global. Methods: We conducted a quantitative synthesis analysing the contribution of over 400 distinct environmental variables to 2040 MaxEnt SDMs for nearly 1900 species representing over 300 families. Environmental variables were grouped into 24 related factors and results were analysed by examining the frequency with which variables were found to be most important, the mean contribution of each variable (at various taxonomic levels), and using TrueSkill™, a Bayesian skill rating system. Results: Precipitation, temperature, bathymetry, distance to water and habitat patch characteristics were the most important variables overall. Precipitation and temperature were analysed most frequently and one of these variables was often the most important predictor in the model (nearly 80% of models, when tested). Notably, distance to water was the most important variable in the highest proportion of models in which it was tested (42% of 225 models). For terrestrial species, precipitation, temperature and distance to water had the highest overall contributions, whereas for aquatic species, bathymetry, precipitation and temperature were most important. Main conclusions: Over all MaxEnt models published, the ability to discriminate occurrence from reference sites was high (average AUC = 0.92). Much of this discriminatory ability was due to temperature and precipitation variables. Further, variability (temperature) and extremes (minimum precipitation) were the most predictive. More generally, the most commonly tested variables were not always the most predictive, with, for instance, 'distance to water' infrequently tested, but found to be very important when it was. Thus, the results from this study summarize the MaxEnt SDM literature, and can aid in variable selection by identifying underutilized, but potentially important variables, which could be incorporated in future modelling efforts.

Yellowfin tuna, Thunnus albacares, are the most important ecological and economic fishes in the Persian Gulf. In recent decades, their populations have faced overfishing, environmental problems and climate change. In this study, using some environmental variables affecting the habitat of tuna fish, i.e. sea surface temperature at night and day, reflection of 645 nm wavelength as a water turbidity, angstrom view of aerosol 443 to 965 nm, aerosol optic thickness at 869 nm, organic and inorganic particle carbon, photosynthetic active radiation, absorption by phytoplankton at 443 nm and chlorophyll-a concentration from 2002 to 2018, on the spatial distribution of yellow-fin tuna has been modelled by fuzzy rule-based classification. Over the years, the variables had different degrees of importance in the models. There was a great variation in the spatial distribution of the species from year to year.

Phytoplankton are diverse photosynthetic organisms in estuarine ecosystems and sensitive indicators of environmental changes. This study employed Generalized Additive Model (GAM) to explore the impact of environmental variables on the abundance of six dominant phytoplankton species in the tropical Karanja estuary, India. Data were collected from five sampling stations between January 2022 and March 2023. The GAM model explained ≥ 55% of the variability in species distribution, predicting that the Karanja estuary provides a suitable habitat for these phytoplankton. Spatiotemporal predictions revealed higher abundances of Asterionellopsis glacialis , Coscinodiscus sp., Pseudo-nitzschia pungens , Skeletonema costatum and Thalassionema frauenfeldii during post-monsoon and lower in pre-monsoon. Conversely, Odontella sinensis thrived during pre-monsoon. Optimal growth conditions included a water temperature range of 24–32 °C for O. sinensis , P. pungens and S. costatum , while chlorophyll-b concentrations between 1 and 20 mg/m 3 favored A. glacialis , O. sinensis , P. pungens , S. costatum and T. frauenfeldii. P. pungens , S. costatum and T. frauenfeldii exhibited broad salinity tolerance ranging from 10–40‰. Additionally, Coscinodiscus sp., P. pungens and T. frauenfeldii were found in environments with nitrate content of 0.1–1 mg/L, while O. sinensis preferred silicate levels of 1–10 mg/L. Notably, these species serve as indicators of eutrophication, providing insights into ecosystem health. This study represents the first application of GAM for exploring phytoplankton-environment interactions in India, offering critical data for water quality management, pollution control and food web preservation. Policymakers can use these findings to develop evidence-based regulations that support proactive coastal management, enhance estuarine resilience to climate stressors and ensure sustainable resource conservation.

Although several studies have examined the functional diversity of freshwater macroinvertebrates, the variety of methodologies combined with the absence of a synthetic review make our understanding of this field incomplete. Therefore, we reviewed the current methodology for assessing functional diversity in freshwater macroinvertebrate research. Our review showed that most papers quantified functional diversity using biological traits, among which feeding habits were the most common traits probably due to the assumed links between feeding and ecosystem functions. A large number of diversity measures have been applied for quantifying functional diversity of freshwater macroinvertebrate assemblages, among which Rao’s quadratic entropy looks like the most frequent. In most papers, functional diversity was positively related to taxon richness, and functional redundancy was a key concept in explaining this correlation. Most studies detected strong influence of the environmental factors as well as human impact on functional diversity. Finally, our review revealed that functional diversity research is biased towards European running waters and is hindered by yet insufficient information on the autecology of macroinvertebrates.