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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.

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.

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.

Larvae of Culex (Melanoconion) pilosus were collected during February–April 2014 in temporary pools in “Bosques de Ezeiza,” a large forested park, near Buenos Aires city, Argentina. This is the first record in Buenos Aires Province, extending the distribution of this species 380 km to the south. Regarding habitat use, Cx. (Mel.) pilosus is a generalist, although a slight association of larval abundances with pools of lower pH and higher vegetation cover was observed. The comparison of larval instars of Cx. (Mel.) pilosus with those of other genera suggests a life-history strategy similar to that of floodwater mosquitoes.

The present study analysed the distribution of subfossil chironomid larval head caspules in a suite of surface sediment samples from a large deep subtropical lake, i.e. Lugu lake, located in southwest China. In order to identify the relationships between environmental variables and the chironomid assemblages ordination methods were used. A total of 41 chironomid taxa were found across the 46 samples, 21 of which had a minimum abundance >1% and were present in more than one site. The samples were dominated by 12 taxa, which together accounted for 97% of the fauna. Redundancy analysis (RDA) revealed that the predominant drivers of chironomid distribution within Lugu lake were bottom water temperature, water depth, loss-on-ignition (LOI) and water total phosphorus (TP) concentration. Abrupt changes in chironomid assemblages occurred at 10 m of water depth, which is closely related to the macrophyte distribution and the position of the thermocline. The chironomid assemblages became uniform below a water depth of ca. 15 m, and the anthropogenic impact on the chironomid fauna was then limited to the edge of Lugu lake. The depth-related faunal shifts primarily reflect the dominant controls of temperature and macrophyte distribution on the chironomids in Lugu lake. This is the first within-lake subfossil chironomid study from this region and the understanding of key environmental influences on contemporary faunas within the lake will aid interpretations of palaeolimnological datasets to reconstruct past trends and magnitude of environmental change over a range of timescales.

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.

Environmental variation is a crucial driver of ecological pattern, and spatial layers representing this variation are key to understanding and predicting important ecosystem distributions and processes. A national, standardised collection of different environmental gradients has the potential to support a variety of large-scale research questions, but to date these data sets have been limited and difficult to obtain. Here we describe the New Zealand Environmental Data Stack (NZEnvDS), a comprehensive set of 72 environmental layers quantifying spatial patterns of climate, soil, topography and terrain, as well as geographical distance at 100 m resolution, covering New Zealand’s three main islands and surrounding inshore islands. NZEnvDS includes layers from the Land Environments of New Zealand (LENZ), additional layers generated for LENZ but never publicly released, and several additional layers generated more recently. We also include an analysis of correlation between variables. All final NZEnvDS layers, their original source layers, and the R-code used to generate them are available publicly for download at https://doi.org/10.7931/m6rm-vz40.

Urban riverfront space has diversified ecosystem services, but due to excessive changes in the geographical environment, such as drastic changes in land use, people gain social value at a great ecological cost. Obtaining benefits from the ecosystem in this way is not sustainable. Therefore, this paper uses the SolVES model to evaluate the social value of ecosystem services on the east bank of the Fenghe River, while also studying the contribution of different environmental variables to social value. The main results are as follows. (1) Environmental variables affect the spatial distribution characteristics of social value. The distance to water (DTW) means the social value was distributed in strips, and the distance to road (DTR) concentrated the social value along the road. The landscape type (LT) means the social value was concentrated in the landscape space. (2) When DTW, DTR, and LT were collectively used as environmental variables, the distribution characteristics of various social values were similar to when LT was used as the only environmental variable. (3) The results of MaxEnt show that LT made a greater contribution to the aesthetic, recreation, therapeutic, and historic values, and was the largest contribution factor to the aesthetic, therapeutic, and historic values, with contribution rates of 47.6, 50.5, and 80.0%, respectively. DTW is the factor that contributed the most to recreation, with a contribution rate of 43.1%. Improving social value based on the influence and contribution of environmental variables can reduce the damage to the ecological environment caused by changes in geographic factors. This is sustainable for both the ecosystem and the services it provides to mankind.