Taking stock of nature: Essential biodiversity variables explained

In 2013, the Group on Earth Observations Biodiversity Observation Network (GEO BON) developed the framework of Essential Biodiversity Variables (EBVs), inspired by the Essential Climate Variables (ECVs). The EBV framework was developed to distill the complexity of biodiversity into a manageable list of priorities and to bring a more coordinated approach to observing biodiversity on a global scale. However, efforts to address the scientific challenges associated with this task have been hindered by diverse interpretations of the definition of an EBV. Here, the authors define an EBV as a critical biological variable that characterizes an aspect of biodiversity, functioning as the interface between raw data and indicators. This relationship is clarified through a multi-faceted stock market analogy, drawing from relevant examples of biodiversity indicators that use EBVs, such as the Living Planet Index and the UK Spring Index. Through this analogy, the authors seek to make the EBV concept accessible to a wider audience, especially to non-specialists and those in the policy sector, and to more clearly define the roles of EBVs and their relationship with biodiversity indicators. From this we expect to support advancement towards globally coordinated measurements of biodiversity

Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions

Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS-EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS-EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS-EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long-term coordinated actions

Temporal relationship between instantaneous pressure gradients and peak‐to‐peak systolic ejection gradient in congenital aortic stenosis

ObjectiveWe sought to identify a time during cardiac ejection when the instantaneous pressure gradient (IPG) correlated best, and near unity, with peak‐to‐peak systolic ejection gradient (PPSG) in patients with congenital aortic stenosis. Noninvasive echocardiographic measurement of IPG has limited correlation with cardiac catheterization measured PPSG across the spectrum of disease severity of congenital aortic stenosis. A major contributor is the observation that these measures are inherently different with a variable relationship dependent on the degree of stenosis.DesignHemodynamic data from cardiac catheterizations utilizing simultaneous pressure measurements from the left ventricle (LV) and ascending aorta (AAo) in patients with congenital valvar aortic stenosis was retrospectively reviewed over the past 5 years. The cardiac cycle was standardized for all patients using the percentage of total LV ejection time (ET). Instantaneous gradient at 5% intervals of ET were compared to PPSG using linear regression and Bland‐Altman analysis.ResultsA total of 22 patients underwent catheterization at a median age of 13.7 years (interquartile range [IQR] 10.3‐18.0) and median weight of 51.1 kg (IQR 34.2‐71.6). The PPSG was 46.5 ± 12.6 mm Hg (mean ± SD) and correlated suboptimally with the maximum and mean IPG. The midsystolic IPG (occurring at 50% of ET) had the strongest correlation with the PPSG (PPSG = 0.97(IPG50%)–1.12, R2 = 0.88), while the IPG at 55% of ET was closest to unity (PPSG = 0.997(IPG55%)–1.17, R2 = 0.87).ConclusionsThe commonly measured maximum and mean IPG are suboptimal estimates of the PPSG in congenital aortic stenosis. Using catheter‐based data, IPG at 50%–55% of ejection correlates well with PPSG. This may allow for a more accurate estimation of PPSG via noninvasive assessment of IPG.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140042/1/chd12514.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140042/2/chd12514_am.pd

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

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

Time-lapsing biodiversity: an open source method for measuring diversity changes by remote sensing

Understanding biodiversity changes in time is crucial to promptly provide management practices against diversity loss. This is overall true when considering global scales, since human-induced global change is expected to make significant changes on the Earth's biota. Biodiversity management and planning is mainly based on field observations related to community diversity, considering different taxa. However, such methods are time and cost demanding and do not allow in most cases to get temporal replicates. In this view, remote sensing can provide a wide data coverage in a short period of time. Recently, the use of Rao's Q diversity as a measure of spectral diversity has been proposed in order to explicitly take into account differences in a neighbourhood considering abundance and relative distance among pixels. The aim of this paper was to extend such a measure over the temporal dimension and to present an innovative approach to calculate remotely sensed temporal diversity. We demonstrated that temporal beta-diversity (spectral turnover) can be calculated pixel-wise in terms of both slope and coefficient of variation and further plotted over the whole matrix / image. From an ecological and operational point of view, for prioritisation practices in biodiversity protection, temporal variability could be beneficial in order to plan more efficient conservation practices starting from spectral diversity hotspots in space and time. In this paper, we delivered a highly reproducible approach to calculate spatio-temporal diversity in a robust and straightforward manner. Since it is based on open source code, we expect that our method will be further used by several researchers and landscape managers

ORTHORECTIFICATION OF A LARGE DATASET OF HISTORICAL AERIAL IMAGES: PROCEDURE AND PRECISION ASSESSMENT IN AN OPEN SOURCE ENVIRONMENT

The availability of data time series spanning a long period is crucial for landscape change analysis. A suitable dataset, both in terms of time span and information content, must be available for the use with a GIS.In Italy, one of the most important historical source of land cover analysis is the GAI (Gruppo Aereo Italiano) photogrammetric survey (“Volo GAI”) commissioned in 1954 by the Italian national mapping agency, Istituto Geografico Militare Italiano (IGMI).The survey covers the whole Italy, but so far only some Regions, namely Lombardia and Veneto, have carried out the image rectification and the successive analyses to map land cover and use.This work describes the process of image orthorectification of the Volo GAI images for the Province of Trento (Provincia Autonoma di Trento).Image orthorectification must be performed to transform the images in maps available for analysis. This procedure corrects the geometry according to the terrain surface described by a Digital Terrain Model (DTM) to create an image compatible with the cartographic projection in use.To this end, the orthorectification modules available in GRASS GIS have been used, with the advantage of using the same GIS environment which will be used for the landscape analysis. The dataset covering the whole Province contains almost 100 images, this paper presents the preliminary results of the orthorectification of a quarter of the images. A reduced dataset has been used to test the results obtained using different settings with respect to: digital image resolution, DTM resolution and number of Ground Control Points (GCPs) used for the external orientation.These preliminary tests show that for the average quality of the Volo GAI images scan resolution beyond 600 DPI and DTM resolution above 10 m do not provide significant improvements for orthorectification images. The minimum number of GCPs to guarantee the requested accuracy can vary from image to image, depending on the image quality and recognizable features position, but it is usually in the 15–20 points range