Likelihood of changes in forest species suitability,distribution, and diversity under future climate: The case of Southern Europe

Forest conservation strategies and plans can be unsuccessful if the new habitat conditions determined by climate change are not considered. Our work aims at investigating the likelihood of future suitability, distribution and diversity for some common European forest species under the projected changes in climate, focusing on Southern Europe. We combine an Ensemble Platform for Species Distribution Models (SDMs) to five Global Circulation Models (GCMs) driven by two Representative Concentration Pathways (RCPs), to produce maps of future climate‐driven habitat suitability for ten categories of forest species and two time horizons. For each forest category and time horizon, ten maps of future distribution (5 GCMs by 2 RCPs) are thus combined in a single suitability map supplied with information about the “likelihood” adopting the IPCC terminology based on consensus among projections. Then, the statistical significance of spatially aggregated changes in forest composition at local and regional level is analyzed. Finally, we discuss the importance, among SDMs, that environmental predictors seem to have in influencing forest distribution. Future impacts of climate change appear to be diversified across forest categories. A strong change in forest regional distribution and local diversity is projected to take place, as some forest categories will find more suitable conditions in previously unsuitable locations, while for other categories the same new conditions will become less suited. A decrease in species diversity is projected in most of the area, with Alpine region showing the potentiality to become a refuge for species migration.     

Cognitive predictors of balance in Parkinson’s disease

Postural instability is one of the most incapacitating symptoms of Parkinson’s disease (PD) and appears to be related to cognitive deficits. This study aims to determine the cognitive factors that can predict deficits in static and dynamic balance in individuals with PD. A sociodemographic questionnaire characterized 52 individuals with PD for this work. The Trail Making Test, Rule Shift Cards Test, and Digit Span Test assessed the executive functions. The static balance was assessed using a plantar pressure platform, and dynamic balance was based on the Timed Up and Go Test. The results were statistically analysed using SPSS Statistics software through linear regression analysis. The results show that a statistically significant model based on cognitive outcomes was able to explain the variance of motor variables. Also, the explanatory value of the model tended to increase with the addition of individual and clinical variables, although the resulting model was not statistically significant The model explained 25–29% of the variability of the Timed Up and Go Test, while for the anteroposterior displacement it was 23–34%, and for the mediolateral displacement it was 24–39%. From the findings, we conclude that the cognitive performance, especially the executive functions, is a predictor of balance deficit in individuals with PD.     

Diversity of European habitat types is correlated with geography more than climate and human pressure

Habitat richness, that is, the diversity of ecosystem types, is a complex, spatially explicit aspect of biodiversity, which is affected by bioclimatic, geographic, and anthropogenic variables. The distribution of habitat types is a key component for understanding broad‐scale biodiversity and for developing conservation strategies. We used data on the distribution of European Union (EU) habitats to answer the following questions: (i) how do bioclimatic, geographic, and anthropogenic variables affect habitat richness? (ii) Which of those factors is the most important? (iii) How do interactions among these variables influence habitat richness and which combinations produce the strongest interactions? The distribution maps of 222 terrestrial habitat types as defined by the Natura 2000 network were used to calculate habitat richness for the 10 km × 10 km EU grid map. We then investigated how environmental variables affect habitat richness, using generalized linear models, generalized additive models, and boosted regression trees. The main factors associated with habitat richness were geographic variables, with negative relationships observed for both latitude and longitude, and a positive relationship for terrain ruggedness. Bioclimatic variables played a secondary role, with habitat richness increasing slightly with annual mean temperature and overall annual precipitation. We also found an interaction between anthropogenic variables, with the combination of increased landscape fragmentation and increased population density strongly decreasing habitat richness. This is the first attempt to disentangle spatial patterns of habitat richness at the continental scale, as a key tool for protecting biodiversity. The number of European habitats is related to geography more than climate and human pressure, reflecting a major component of biogeographical patterns similar to the drivers observed at the species level. The interaction between anthropogenic variables highlights the need for coordinated, continental‐scale management plans for biodiversity conservation.     

Register-based predictors of violations of animal welfare legislation in dairy herds

The assessment of animal welfare can include resource-based or animal-based measures. Official animal welfare inspections in Denmark primarily control compliance with animal welfare legislation based on resource measures (e.g. housing system) and usually do not regard animal response parameters (e.g. clinical and behavioural observations). Herds selected for welfare inspections are sampled by a risk-based strategy based on existing register data. The aim of the present study was to evaluate register data variables as predictors of dairy herds with violations of the animal welfare legislation (VoAWL) defined as occurrence of at least one of the two most frequently violated measures found at recent inspections in Denmark, namely (a) presence of injured animals not separated from the rest of the group and/or (b) animals in a condition warranting euthanasia still being present in the herd. A total of 25 variables were extracted from the Danish Cattle Database and assessed as predictors using a multivariable logistic analysis of a data set including 73 Danish dairy herds, which all had more than 100 cows and cubicle loose-housing systems. Univariable screening was used to identify variables associated with VoAWL at a P-value<0.2 for the inclusion in a multivariable logistic regression analysis. Backward selection procedures identified the following variables for the final model predictive of VoAWL: increasing standard deviation of milk yield for first lactation cows, high bulk tank somatic cell count (⩾250 000 cells/ml) and suspiciously low number of recorded veterinary treatments (⩽25 treatments/100 cow years). The identified predictors may be explained by underlying management factors leading to impaired animal welfare in the herd, such as poor hygiene, feeding and management of dry or calving cows and sick animals. However, further investigations are required for causal inferences to be established.     

Resting Sinus Heart Rate and First Degree AV block: Modifiable Risk Predictors or Epiphenomena?

Simple and cost-effective tools that identify patients at increased risk for adverse cardiovascular events are actively sought. High resting sinus heart rate and first degree AV block are easily recognized and commonly encountered findings in a cardiology practice. A growing body of epidemiological and clinical evidence has been shown them to be independent predictors of all-cause and cardiovascular mortality, both in the general population and in patients with structural heart disease. This paper reviews the important role of heart rate and first degree AV block in predicting cardiovascular outcomes, examines the pathophysiological mechanisms underlying this increased risk, and discusses the effectiveness of available therapies to favorably modify these risk factors.     

Fish condition in introduced tilapias of Ugandan crater lakes in relation to deforestation and fishing pressure

This study identifies environmental predictors of the condition of two introduced tilapia species (Oreochromis leucostictus and Tilapia zillii) that are known to have divergent trophic niches (planktivore and herbivore, respectively) in 17 crater lakes in western Uganda. We asked whether fish condition differs among lakes characterized by differences in fishing pressure and catchment deforestation; and we related relative condition factor to gradients of environmental variation across lakes. Lakes characterized by severe catchment deforestation tended to be lakes with high fishing pressure, so it was difficult to explore independent and interactive effects. However, mean relative condition factor was higher in populations with high fishing pressure compared to populations with low fishing pressure for both O. leucostictus and T. zillii. The condition of O. leucostictus populations was higher in lakes with severely deforested catchments; but mean relative condition factor of T. zillii did not differ between deforestation categories. Principal components analysis (PCA) was used to describe the major environmental gradients of variation among the lakes; and PCA factor scores were regressed against relative fish condition. The association between fish condition and environmental gradients was stronger for O. leucostictus than for T. zillii. For O. leucostictus, fish condition was related to PC1 (43% of the variance) and factors that loaded most heavily included Chl-a, water transparency, lake area and depth, suggesting higher condition in lakes characterized by higher primary productivity and smaller size. For T. zillii, PC3 (11%) was the only axis related to fish condition; and factors that loaded most heavily included lake area (positive), and conductivity and total nitrogen (negative). Some of the larger lakes are characterized by higher availability of macrophytes that may positively affect the food base for T. zillii.     

Comparison of complex modeling strategies for prediction of a binary outcome based on a few,highly correlated predictors

Motivated by a clinical prediction problem, a simulation study was performed to compare different approaches for building risk prediction models. Robust prediction models for hospital survival in patients with acute heart failure were to be derived from three highly correlated blood parameters measured up to four times, with predictive ability having explicit priority over interpretability. Methods that relied only on the original predictors were compared with methods using an expanded predictor space including transformations and interactions. Predictors were simulated as transformations and combinations of multivariate normal variables which were fitted to the partly skewed and bimodally distributed original data in such a way that the simulated data mimicked the original covariate structure. Different penalized versions of logistic regression as well as random forests and generalized additive models were investigated using classical logistic regression as a benchmark. Their performance was assessed based on measures of predictive accuracy, model discrimination, and model calibration. Three different scenarios using different subsets of the original data with different numbers of observations and events per variable were investigated. In the investigated setting, where a risk prediction model should be based on a small set of highly correlated and interconnected predictors, Elastic Net and also Ridge logistic regression showed good performance compared to their competitors, while other methods did not lead to substantial improvements or even performed worse than standard logistic regression. Our work demonstrates how simulation studies that mimic relevant features of a specific data set can support the choice of a good modeling strategy.     

Estimating and controlling for spatial structure in the study of ecological communities

Aim Variation partitioning based on canonical analysis is the most commonly used analysis to investigate community patterns according to environmental and spatial predictors. Ecologists use this method in order to understand the pure contribution of the environment independent of space, and vice versa, as well as to control for inflated type I error in assessing the environmental component under spatial autocorrelation. Our goal is to use numerical simulations to compare how different spatial predictors and model selection procedures perform in assessing the importance of the spatial component and in controlling for type I error while testing environmental predictors. Innovation We determine for the first time how the ability of commonly used (polynomial regressors) and novel methods based on eigenvector maps compare in the realm of spatial variation partitioning. We introduce a novel forward selection procedure to select spatial regressors for community analysis. Finally, we point out a number of issues that have not been previously considered about the joint explained variation between environment and space, which should be taken into account when reporting and testing the unique contributions of environment and space in patterning ecological communities. Main conclusions In tests of species‐environment relationships, spatial autocorrelation is known to inflate the level of type I error and make the tests of significance invalid. First, one must determine if the spatial component is significant using all spatial predictors (Moran's eigenvector maps). If it is, consider a model selection for the set of spatial predictors (an individual‐species forward selection procedure is to be preferred) and use the environmental and selected spatial predictors in a partial regression or partial canonical analysis scheme. This is an effective way of controlling for type I error in such tests. Polynomial regressors do not provide tests with a correct level of type I error.     

Truffle biogeography—A case study revealing ecological niche separation of different Tuber species

Ecology of hypogeic mycorrhizal fungi, such as truffles, remains largely unknown, both in terms of their geographical distribution and their environmental niches. Occurrence of true truffles (Tuber spp.) was therefore screened using specific polymerase chain reaction (PCR) assays and subsequent PCR amplicon sequencing in tree roots collected at 322 field sites across the Czech Republic. These sites spanned a wide range of climatic and soil conditions. The sampling was a priori restricted to areas thought to be suitable for Tuber spp. inasmuch as they were characterized by weakly acidic to alkaline soils, warmer climate, and with tree species previously known to host true truffles. Eight operational taxonomic units (OTUs) corresponding to Tuber aestivum, T. borchii, T. foetidum, T. rufum, T. indicum, T. huidongense, T. dryophilum, and T. oligospermum were detected. Among these, T. borchii was the OTU encountered most frequently. It was detected at nearly 19% of the sites. Soil pH was the most important predictor of Tuber spp. distribution. Tuber borchii preferred weakly acidic soils, T. foetidum and T. rufum were most abundant in neutral soils, and T. huidongense was restricted to alkaline soils. Distribution of T. aestivum was mainly dictated by climate, with its range restricted to the warmest sites. Host preferences of the individual Tuber spp. were weak compared to soil and climatic predictors, with the notable exception that T. foetidum appeared to avoid oak trees. Our results open the way to better understanding truffle ecology and, through this new knowledge, also to better‐informed trufficulture.