Using spectral diversity and heterogeneity measures to map habitat mosaics: An example from the Classical Karst

Questions: Can we map complex habitat mosaics from remote-­sensing data? In doing this, are measures of spectral heterogeneity useful to improve image classification performance? Which measures are the most important? How can multitemporal data be integrated in a robust framework? Location: Classical Karst (NE Italy). Methods: First, a habitat map was produced from field surveys. Then, a collection of 12 monthly Sentinel-­2 images was retrieved. Vegetation and spectral heterogeneity (SH) indices were computed and aggregated in four combinations: (1) monthly layers of vegetation and SH indices; (2) seasonal layers of vegetation and SH indices; (3) yearly layers of SH indices computed across the months; and (4) yearly layers of SH indices computed across the seasons. For each combination, a Random Forest clas- sification was performed, first with the complete set of input layers and then with a subset obtained by recursive feature elimination. Training and validation points were independently extracted from field data. Results: The maximum overall accuracy (0.72) was achieved by using seasonally ag- gregated vegetation and SH indices, after the number of vegetation types was re- duced by aggregation from 26 to 11. The use of SH measures significantly increased the overall accuracy of the classification. The spectral β-­diversity was the most im- portant variable in most cases, while the spectral α-­diversity and Rao's Q had a low relative importance, possibly because some habitat patches were small compared to the window used to compute the indices. Conclusions: The results are promising and suggest that image classification frame- works could benefit from the inclusion of SH measures, rarely included before. Habitat mapping in complex landscapes can thus be improved in a cost-­and time-­effective way, suitable for monitoring applications

Sampling strategy matters to accurately estimate response curves' parameters in species distribution models

Aim: Assessing how different sampling strategies affect the accuracy and precision of species response curves estimated by parametric species distribution models.Major Taxa Studied: Virtual plant species.Location: Abruzzo (Italy).Time Period: Timeless (simulated data).Methods: We simulated the occurrence of two virtual species with different ecology (generalist vs specialist) and distribution extent. We sampled their occurrence following different sampling strategies: random, stratified, systematic, topographic, uniform within the environmental space (hereafter, uniform) and close to roads. For each sampling design and species, we ran 500 simulations at increasing sampling efforts (total: 42,000 replicates). For each replicate, we fitted a binomial generalised linear model, extracted model coefficients for precipitation and temperature, and compared them with true coefficients from the known species' equation. We evaluated the quality of the estimated response curves by computing bias, variance and root mean squared error (RMSE). Additionally, we (i) assessed the impact of missing covariates on the performance of the sampling approaches and (ii) evaluated the effect of incompletely sampling the environmental space on the uniform approach.Results: For the generalist species, we found the lowest RMSE when uniformly sampling the environmental space, while sampling occurrence data close to roads provided the worst performance. For the specialist species, all sampling designs showed comparable outcomes. Excluding important predictors similarly affected all sampling strategies. Sampling limited portions of the environmental space reduced the performance of the uniform approach, regardless of the portion surveyed.Main Conclusions: Our results suggest that a proper estimate of the species response curve can be obtained when the choice of the sampling strategy is guided by the species' ecology. Overall, uniformly sampling the environmental space seems more efficient for species with wide environmental tolerances. The advantage of seeking the most appropriate sampling strategy vanishes when modelling species with narrow realised niches

Mapping the recreational value of coppices’ management systems in Tuscany

In recent decades the growing interest in forested areas has led to a higher level of appreciation and consideration regarding the various benefits and services provided by forests. Despite this, when it comes to acknowledging their economic value and their capacity to produce income, the production of timber seems to be the main or even the only function that is considered. However, by adopting a sustainable forest management approach, the value related to non-market forest functions could also be considered. The present paper aims to quantify the potential income related to the recreational value of coppice forest by considering three different management systems: traditional coppice, active conversion to high forest and the natural evolution of forest. In order to do so, a contingent valuation method was used, and 248 forest users were surveyed in the region of Tuscany, Italy. The surveys included a revised price-list method, and the results obtained showed the existence of willingness to pay (WTP) for the maintenance of forests. Users showed a strong preference for conversion to high forest, while natural evolution was the least preferred management option. People’s perception on this matter was also assessed based on their specific location, by georeferencing all of the respondents’ answers: considering this, it was observed that belonging to a municipality located in or close to the mountains (i.e., mountain and natural municipalities) influenced the users’ WTP to maintain natural evolution

Double down on remote sensing for biodiversity estimation. A biological mindset

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the “spectral species”—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept

Under the mantra: ‘Make use of colorblind friendly graphs’

Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In this paper, we propose good practices and coding solutions developed in the R Free and Open Source Software to (i) simulate colorblindness, (ii) develop colorblind friendly color palettes and (iii) provide the tools for converting a noncolorblind friendly graph into a new image with improved colors

Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns

Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present the cblindplot R package and its founding function - cblind.plot() - which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colour blind friendly ramp palette. The function described in this paper, on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette

Impact of Vitamin C on Endothelial Function and Exercise Capacity in Patients with a Fontan Circulation

Objective.  To evaluate the impact of antioxidant therapy on functional health status in Fontan‐palliated patients. Design.  Prospective, randomized, double‐blind, placebo‐controlled trial. Patients.  Fifty‐three generally asymptomatic Fontan patients. Interventions.  Patients were randomized to receive either high‐dose ascorbic acid (vitamin C) or placebo for 4 weeks. Outcome Measures.  Peripheral vascular function, as measured with endothelium‐dependent digital pulse amplitude testing (EndoPAT), and exercise capacity were assessed before and after study drug treatment. Primary outcome measures included the EndoPAT index and peripheral arterial tonometry (PAT) ratio, both validated markers of vascular function. Secondary outcome measures included peak oxygen consumption and work. Results.  Twenty‐three vitamin C‐ and 21 placebo‐assigned subjects completed the protocol (83%). Median age and time from Fontan completion were 15 (interquartile range [IQR] 11.7–18.2) and 11.9 years (IQR 9.0–15.7), respectively. Right ventricular morphology was dominant in 30 (57%). Outcome measures were similar between groups at baseline. Among all subjects, vitamin C therapy was not associated with a statistical improvement in either primary or secondary outcome measures. In subjects with abnormal vascular function at baseline, compared with placebo, vitamin C therapy more frequently resulted in normalization of the EndoPAT index (45% vs. 17%) and PAT ratio (38% vs. 13%). Conclusions.  Short‐term therapy with vitamin C does not alter endothelial function or exercise capacity in an asymptomatic Fontan population overall. Vitamin C may provide benefit to a subset of Fontan patients with abnormal vascular function.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92126/1/j.1747-0803.2011.00605.x.pd

Effects of spatial scales and vegetation types on Observer bias: practical implications for long term monitoring programs

Global changes mainly due to habitat fragmentation and climate variation, are rapidly influencing terrestrial and aquatic ecosystems. Long-term monitoring programs based on periodic reliefs represent important tools to understand ecosystem changes in time and in space. Under this perspective, it is crucial to understand the amount of variation in species inventories due to the observer: in long-term monitoring programs, it is often impossible to maintain the same teams of observers over the years and this variation through years can results in a major impact on the data quality and consistency; biased data can result into changes in time due to systematic differences among observers instead of true variations. Non-sampling errors (both within and between observer) can be classified in: 1) overlooking errors, when a species is not recorded when it is present; 2) misidentification errors, occurring when the species is not correctly identified; 3) estimation errors, when species abundances are not accurately estimated. This work aims to: i) investigate the role of observer subjectivity in sampling vegetation in forest monitoring plots in relation to different parameters such as vegetation complexity, observer expertise and the spatial scale of observation and ii) suggest ideas to reduce the observer bias for reliable and repeatable monitoring programs over long periods. We analyzed the observers’ influence on vegetation records using data collected in six forest areas in Tuscany (Central Italy): 10 nested multi-scale plots (three plot measures: 1 m2, 10 m2 and 100 m2) were sampled in spring/summer 2009 by three different teams of botanists with different level of knowledge of the vegetation in the areas. We analyzed the observers’ influence on vegetation data using different analytic methods such as comparisons among field notebooks and permutation analysis of variance (PERMANOVA). We observed that most of the divergence in species records are related with different characteristics of the sampled area, therefore ecologically and structurally complex sites increase observer bias due to the difficulty in species detection. Furthermore, we highlighted the importance of training for new observers to level off their experience with the other more-trained members of the monitoring team

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.Research contributing to this study was funded by the project “Development of a National Plan for Biodiversity Monitoring” (Italian National Institute for Environmental Protection and Research – ISPRA). BIOME Group was partially supported by the H2020 SHOWCASE (Grant agreement No 862480) and by the H2020 COST Action CA17134 ‘Optical synergies for spatiotemporal sensing of scalable ecophysiological traits (SENECO)’