Geo‐referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the ...usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and data sets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence data sets. sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and data sets. We demonstrate the use of sampbias on a data set of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. sampbias is implemented as a well‐documented, open‐access and user‐friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.
Luna et al. (2022) concluded that the environment contributes to explaining specialisation in open plant–pollinator networks. When reproducing their study, we instead found that network size alone ...largely explained the variation in their specialisation metrics. Thus, we question whether empirical network specialisation is driven by the environment.
Luna et al. (2022) concluded that the environment contributes to explaining specialisation in open plant–pollinator networks. When reproducing their study, we instead found that network size alone largely explained the variation in their specialisation metrics. Thus, we question whether empirical network specialisation is driven by the environment.
•Sampling intensity, stand age and spatial heterogeneity have greater influence on the reliability for total volume estimates than subsampling intensity and measurement error.•Stand age and levels of ...spatial heterogeneity can be regarded as useful tools for forest inventory planning.•For a constant total number of measurement trees, adding sample plots is suggested rather than adding measurement trees on plots in order to reduce the variability for total volume estimates.
Assessing current conditions and projecting future forest productivity are primary objectives in designing forest inventory for forest management planning. Stand net volume, expressed as sum of all standing trees’ volume on a per-unit area basis, has been widely applied to quantify forest productivity. Total tree height is a principal predictor commonly used to estimate tree volume, weight and biomass. However, measuring tree heights in the field is time consuming and labor intensive. Measuring a small portion of trees on the sample plots, denoted as subsampling heights, is a relatively efficient alternative in many assessments of forest cover. In this study, sampling plans with different levels of subsampling intensity were evaluated. Four 37.63-ha loblolly pine plantations with varying degrees of spatial heterogeneity at four inventory entry points (ages 10, 15, 20 and 25) were simulated. The impact of tree height measurement error was assessed using data from 210 standing and felled trees measurements across southeastern US and incorporated in the simulations.
Results indicate that sampling intensity, stand age, and spatial heterogeneity have greater influence on the reliability for total volume estimation than height subsampling intensity and measurement error. Precision of the total volume estimates decreased with increasing degrees of spatial heterogeneity and stand age. Adding sample plots is a more effective way to reduce error for total volume estimation than adding measurement trees on plots. The plantations and repeated samples simulated in this study provide important information about the behavior of total volume and related estimates under various scenarios, which provides guidance for ecologists and resource managers to design efficient subsampling strategies for height measurements.
Due to the influence of sampling strategy, the resulting probability of landslides using logistic regression (LR) can deviate considerably from the actual areal percentage of coseismic landslides. ...This study used the landslides data of the 2013 Lushan, China earthquake to further address this issue. Based on the Bayesian theory, we proposed a sampling method that selects the sliding samples and non-sliding samples based on the ratio of the stable area to the landslide area. Using this method, we tested 15 values of sampling intensities (1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 500, 1000, and 2000 grid cell km−2) and 12 values of non-sliding/sliding sample ratios (1, 5, 10, 25, 50, 75, 100, 125, 150, 175, 200, and 228) for the analysis of the LR model. Ten factors were considered in this analysis, including elevation, slope gradient, aspect, the topographic wetness index (TWI), peak ground acceleration (PGA), distance to the epicenter, distance to rivers, distance to roads, lithology, and annual precipitation. In terms of these 15 sampling intensities and 12 sample ratios, the samples were trained 200 and 150 times, respectively using the LR model, yielding 4800 predicted pictures of potential landslides in the study area. The results show that different sampling intensities have a certain effect on the total predicted landslide area – the higher the intensity of the samples, the more stable the prediction results. Especially when the sampling intensity reaches 1000 grid cell km−2, the total area of the predictive model is about 17.1 km2, which is close to the real area with 17.16 km2, with a difference within 2%. Different ratios of non-slide/slide sample greatly affect the occurrence probability of coseismic landslides. When the ratio is 1:1, the predicted landslide area (Ap) is between 1265 and 1290 km2, with an average of 1280 km2, which is 75 times the actual landslide area. The functional relationship between the ratio of predicted area to real area (Rpa) and the ratio of non-sliding samples to sliding samples (Rns) is Rpa=99.156Rns−0.826. It implies that the non-slide/slide sample ratio determined by the ratio of stable area to the landslide area permits us to construct real probability models to predict the areal percentages of landslides, and based on such models, the predicted probability is largely consistent with the actual areal percentage of coseismic landslides.
•Real occurrence probability modeling of co-seismic landslides•The effect of sampling intensity on the resulting probability of landslides•The non-slide/slide sample ratio greatly influences landslide probability
Stem volume is a key attribute of Eucalyptus forest plantations upon which decision-making is based at diverse levels of planning. Quantifying volume through remote sensing can support a proper ...management of forests. Because of limitations on spaceborne optical and synthetic aperture radar sensors, this study integrated both types of datasets assembled using support vector regression (SVR) to retrieve the stand volume of Eucalyptus plantations. We assessed different combinations of sensors and a minimum number of plots to develop an SVR model. Finally, the best SVR performance was compared with other analytical methods already tested and in the literature: multilinear regression, artificial neural networks (ANN), and random forest (RF). Here, we introduce a test for comparative analysis of the performance of different methods. We found that SVR accurately predicted stem volume of Brazilian fast-growing Eucalyptus forest plantations. Gaussian radial basis was the most suitable kernel function. Integrating the optical and L-band backscatter data increased the predictive accuracy compared to a single sensor model. Combining NIR-band data from ALOS AVNIR-2 and backscatter of L-band horizontal emitted and vertical received (HV) electric fields from ALOS PALSAR produced the most accurate SVR model (with an R2 of 0.926 and root mean square error of 11.007 m3/ha). The number of field plots sufficient for model development with non-redundant explanatory variables was 77. Under this condition, SVR performed similarly to ANN and outperformed the multiple linear regression and random forest methods.
To what extent is the relative biodiversity of some flagship conservation sites a result of differential attention? Knysna estuarine bay is the topmost ranked South African estuary for conservation ...importance and biodiversity. It is also one of the most intensively studied, and hence differential sampling effort could partly be responsible for its apparent relative richness. To assess the extent to which this might be true, identical sampling area, effort and methodology were employed to compare the benthic macrofauna of one specific major Knysna habitat (
Zostera capensis
seagrass beds) with equivalent ones in two nearby lesser-studied estuaries, the Keurbooms/Bitou and Swartvlei. Investigation showed all three localities to share a common species pool, but different elements of it dominated the shared habitat type in each. The seagrass and adjacent sandflat macrobenthos proved just as biodiverse in unprotected Keurbooms/Bitou as in the Protected Area of Knysna, but that in Swartvlei (also a Protected Area) was impoverished in comparison, presumably consequent on mouth closure and the prevailing lower salinity. Despite marked geomorphological and hydrological differences, all three estuaries share a suite of unusual faunal elements and such particularly close faunal similarity suggests the importance of historical biogeographic processes. The analysis emphasises the need for caution when assessing the relative conservation importance or other merits of different individual systems in a data-limited environment.
Abstract Precise assessment of bark stripping damage is of high economic importance, since bark stripping makes wood unusable for saw timber and it is important for compensation payments for game ...damage. Bark stripping is clustered and decreases with increasing tree diameter, so that common forest inventories, optimized for assessing timber production variables such as standing timber volume, do not provide adequately precise estimates of bark stripping damage. In this study we analysed different sampling designs (random sampling, systematic sampling), tree selection methods (fixed radius plot, angle count sampling) and number of plots and plot sizes (plot radius: 2–20 m; basal area factor: 1–6m 2 /ha) for bark stripping assessment. The analysis is based on simulation studies in 9 fully censused stands (9026 trees). Simulations were done for actually assessed damage and randomly distributed damage and each scenario was repeated 100 times with different random points or different random grid locations. Systematic sampling was considerably more precise than random sampling in both scenarios. Sampling intensities to attain a standard error of 10% ranged between 12 and 18% dependent on the plot size. For a given sampling intensity, precision increased with decreasing plot size or increasing basal area factor. This implies, however, a large number of plots to be measured, which is expensive, when travel costs are high. Differences between tree selection by fixed radius plots or angle count sampling were minor. For bark stripping damage, we recommend sampling with fixed radius plots with a radius of 4–6 m and the measurement of approximately 230 or 150 plots, respectively.
•Sampling intensity could influence the estimation of FD (functional diversity) indices.•The accuracy and precision of FD indices estimates increased with sampling intensity.•Functional richness and ...divergence indices were less sensitive than evenness indices.•The effects showed an obvious seasonal variability due to fish migration.
Functional diversity has emerged as a key component of biodiversity in recent two decades, which has been extensively used to assess the health of aquatic ecosystems and predict the responses of fish communities to disturbances. However, sampling intensity may affect the estimation of functional diversity indices and thus lower the validity of functional diversity indices in monitoring long-term changes in ecosystem status. In this study, we used a simulation study to investigate the effect of sampling intensity on the estimation of functional diversity indices of fish communities in the Haizhou Bay, China, based on thirteen fish functional traits that reflect the characteristics of feeding, habitat use, swimming behavior, ecological adaptation, reproduction and life history. In general, sampling intensity influenced the estimation of functional diversity indices, and the accuracy and precision of the estimation of functional diversity indices increased with the sampling intensity. Functional richness and functional divergence indices were less sensitive to sampling intensity than functional evenness indices. The effects of sampling intensity on the estimation of functional diversity indices also showed an obvious seasonal variability, reflecting seasonal differences in trait distribution of composite fish species. This study indicates that it is essential to evaluate and identify the seasonal sampling intensity required in monitoring programs, especially for long-term monitoring program of fisheries ecosystems.
Phylogenetic diversity has been widely used to explore diversity patterns and assess processes governing the species composition in community. The estimates of many metrics depend on high-quality ...data collected from well-designed sampling surveys. However, knowledge of impacts of sampling design on estimation of phylogenetic diversity metrics remains unclear. This study is aim to evaluate the influence of sampling design on phylogenetic diversity metrics estimation of fish community. Simple random sampling (SRS), systematic sampling (SS) and stratified random sampling (StRS) with different sampling intensities were chosen and mean pairwise distances (MPD), mean nearest taxon distance (MNTD), phylogenetic diversity (PD), phylogenetic species variability (PSV), phylogenetic species evenness (PSE) and phylogenetic species richness (PSR) were selected. SRS and StRS showed similar impact on phylogenetic diversity indices estimation and performed relatively well for collecting data to estimate phylogenetic diversity. The accuracy and precision of the estimation increased with sampling intensity under SRS and StRS except SS. MNTD was the only metric not underestimated in four seasons. Metrics strongly influenced by species richness were underestimated when sampling intensity was insufficient. MPD, PSV and PSE showed an obvious seasonal change, which was due to the seasonal differences in fish species composition. In cases where under-sampling is suspected or logistically unavoidable, phylogenetic diversity metrics that are relatively insensitive to sampling design (e.g., MPD and PSV) should be prioritized, especially for exploring the temporal variation in fish community. This study reveals it is indispensable to evaluate sampling design when estimating phylogenetic diversity metrics, especially those indices susceptible to species richness.
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