1,720,991 research outputs found
A design-based view of species richness estimation in environmental surveys
Full text linkIn this work, the estimation of species richness is approached from a design-based perspective, considering
the probabilistic sampling of species and checking the performance of the estimators automated in the SPADE
software. As shown theoretically and by a simulation study, these estimators are affected by a massive
negative bias. To reduce the underestimation of species richness, data integration is attempted, by
exploiting the list of rare species compiled by purposive surveys. Richness estimation is then performed on
the residual community of species not in the list, and a bootstrap mean squared error estimator is applied.
A simulation study and the application to four case studies produce encouraging results
Model-Assisted Procedure for Spatially Explicit Maps with an Application to Wild Boar Rooting Impact
No full textSpatially explicit mapping depicting the distribution of an interest attribute is approached in a model-assisted framework using the inverse distance weighting (IDW) interpolator. The procedure also allows for the inclusion of wall-to-wall auxiliary variables. Moreover, a pseudo-population bootstrap procedure to estimate the precision of the resulting map is adopted. This methodology is applied to the Acquerino-Cantagallo Nature Reserve in central Italy to produce the wild boar rooting impact map and the corresponding precision map
General anesthesia impairs muscle microvascular compliance
No full textIntroduction Drugs used to induce and maintain general
anesthesia have deep effects on the cardiovascular system. To our knowledge there are no studies investigating microvascular
compliance during general anesthesia with a noninvasive approach
based on near-infrared spectroscopy (NIRS) technology.
Methods We randomized 36 healthy subjects undergoing maxillofacial surgery to receive general anesthesia with a sevofluorane–remifentanil (Group S) or a propofol–remifentanil association (Group P). We collected noninvasive measures of hemoglobin concentration from the gastrocnemius muscle of the subjects using a NIRS device (NIMO, NIROX srl, Italy), which performs quantitative assessments of the [HbO2] and [Hb] exploiting precise absorption measurements close to the absorption peak of the water. Data were collected during a series of venous occlusions at different cuff pressures, before and after 30 minutes from induction of general anesthesia. The muscle blood volume and microvascular compliance were obtained with a process previously described elsewhere [1]. Data were analyzed with a one-way analysis of variance test.
Results Demographic data of the 36 subjects were similar in both
Groups S and P. General anesthesia reduced the heart rate and
mean arterial pressure and increased the total muscle blood
volume in both groups (Group S: from 2.4 ± 0.9 to 3.2 ± 1.2 ml/
100 ml; Group P: from 2.4 ± 1.2 to 3.5 ± 1.8 ml/100 ml;
P < 0.05). During general anesthesia, despite no differences in
muscle blood volume between the two groups, sevofluorane–
remifentanil significantly decreased microvascular compliance
(from 0.15 ± 0.08 to 0.09 ± 0.04 ml/mmHg/100 ml; P = 0.001)
whereas propofol–remifentanil did not (from 0.15 ± 0.08 to
0.16 ± 0.11 ml/mmHg/100 ml; P = 0.39)
Design-based mapping of land use/land cover classes with bootstrap estimation of precision by nearest-neighbour interpolation
Full text linkLand use/land cover mapping is usually performed by classifying satellite imagery (e.g., Landsat, Sentinel) for the whole survey region using classification algorithms implemented with training data. Subsequently, probabilistic samples are usually implemented with the main purpose of assessing the accuracy of these maps by comparing the map class and the ground condition determined for the sampled units. The main proposal of this paper is to directly exploit these probabilistic samples to estimate the land use/land cover class at any location of the survey region in a design-based framework by the well-known nearest-neighbour interpolator. For the first time, the design-based consistency of nearest-neighbour maps (i.e., categorical variables) is theoretically proven and a pseudo-population bootstrap estimator of their precision is proposed and discussed. These nearest-neighbour maps provide the ability to place mapping within a rigorous design-based inference framework, in contrast to most traditional mapping approaches which often are implemented with no inferential basis or by necessity (due to lack of a probabilistic sample) model-based inference. A simulation study is performed on an estimated land use map in Southern Tuscany (Italy)—taken as the true map—to check the finite-sample performance of the proposal as well as the matching of the area coverage estimates arising from the map with those achieved by traditional estimators. The Italian land use map arising from the IUTI surveys and the U.S. land cover map arising from the LCMAP program are considered as case studies. © Institute of Mathematical Statistics, 2023
Estimation of plant species richness exploiting probabilistic sampling and purposive lists: Empirical evidence and practical proposal for forest inventories
No full textForest surveys, especially national forest inventories, are evolving towards multipurpose resource strategies, expanding their scope in several directions including biodiversity assessment. This article focuses on the estimation of plant species richness that constitutes one of the most relevant biodiversity indicators in forest ecosystems. In forest inventories, surveys are usually performed by locating plots in the forest area of interest according to a probabilistic scheme, so that the estimation of plant species richness can be approached from a probabilistic perspective by (i) recording a matrix of species presence/absence called incidence data, and then (ii) adopting a class of widely used, automated estimators called nonparametric estimators. The purpose of this article is to raise awareness within the forestry community of the recent findings of Di Biase et al. (2025) who show, both theoretically and through simulations, the inadequacy of nonparametric estimators affected by massive negative bias due to the difficulty of sampling rare species, at the same time highlighting the appeal of a data integration approach that consists in exploiting lists of rare species compiled by purposive surveys to improve the sample-based estimates. A case study performed in the Nature Reserve of Poggio all'Olmo (Central Italy) is considered to confirm the failure of nonparametric estimators and the suitability of the data integration in estimating plant species richness. This approach paves the way for the practical integration of species richness assessment into forest inventories, thus meeting the technical requests to support modern multipurpose forestry
A probabilistic sampling strategy for estimating plant density in Posidonia oceanica meadows
Full text linkMarine and coastal ecosystems, such as seagrasses, mangroves, and coral reefs, provide a range of essential provisioning, regulating and cultural ecosystem services. Recent United Nations guidelines on ecosystem accounting (SEEA EA) emphasise the need for biophysical data as the foundation for compiling ecosystem accounts and conducting economic evaluations for developing indicators and informing policies and interventions. However, data availability on marine ecosystems is limited with respect to terrestrial ones. Moreover, the collection of biophysical data on marine ecosystem extent and condition required for ecosystem accounting (EA) is often not aligned with existing habitat monitoring strategies. This study aims to address the scarcity of spatial data on marine ecosystems and facilitate the integration of current monitoring strategies with the scope of EA. We propose the application of design-based inference for the estimation, mapping, and monitoring of key ecological attributes of marine ecosystems. We focus on the habitat of Posidonia oceanica, an endemic seagrass of the Mediterranean Sea, but the proposed strategy is adaptable to other ecosystems. The benefits of appropriate probabilistic sampling schemes for assessing P. oceanica are explored via simulation testing. The performance of different sample schemes in artificial populations reveals that reliable estimates of density (as well as their precision) can be obtained even with low sample sizes. The empirical viability of our methodology is exemplified using data collected on a meadow located in an Italian Marine Protected Area (Puglia region, Southern Italy)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Per-Pixel Forest Attribute Mapping and Error Estimation: The Google Earth Engine and R dataDriven Tool
Full text linkRemote sensing products are typically assessed using a single accuracy estimate for the entire map, despite significant variations in accuracy across different map areas or classes. Estimating per-pixel uncertainty is a major challenge for enhancing the usability and potential of remote sensing products. This paper introduces the dataDriven open access tool, a novel statistical design-based approach that specifically addresses this issue by estimating per-pixel uncertainty through a bootstrap resampling procedure. Leveraging Sentinel-2 remote sensing data as auxiliary information, the capabilities of the Google Earth Engine cloud computing platform, and the R programming language, dataDriven can be applied in any world region and variables of interest. In this study, the dataDriven tool was tested in the Rincine forest estate study area-eastern Tuscany, Italy-focusing on volume density as the variable of interest. The average volume density was 0.042, corresponding to 420 m3 per hectare. The estimated pixel errors ranged between 93 m3 and 979 m3 per hectare and were 285 m3 per hectare on average. The ability to produce error estimates for each pixel in the map is a novel aspect in the context of the current advances in remote sensing and forest monitoring and assessment. It constitutes a significant support in forest management applications and also a powerful communication tool since it informs users about areas where map estimates are unreliable, at the same time highlighting the areas where the information provided via the map is more trustworthy. In light of this, the dataDriven tool aims to support researchers and practitioners in the spatially exhaustive use of remote sensing-derived products and map validation
- …
