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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.

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.

The Asiatic garden beetle, Maladera formosae Brenske (AGB), has become a significant pest of commercial mint fields in northern Indiana. Larval feeding on mint roots can cause stunted growth and plant death when densities are high. Sampling approaches that provide reliable estimates of larval densities in mint have not been established, leaving farmers without the knowledge necessary to implement integrated pest management (IPM) strategies. To address this knowledge gap, we evaluated strategies for estimating AGB larval densities and plant performance in commercial mint systems. We used 2 sampling methods to collect larval density and plant performance data from 3 mint fields and conducted simulations to optimize sampling intensity (accuracy and precision) and sampling scheme (random vs. systematic) using these data. Additionally, we examined the sensitivity and efficiency of each sampling method. Compared to the cup-cutter method, the quadrat method provided the most accurate and precise estimates of larval density and plant performance, with ≤ 7 samples required per 0.2 ha. Quadrat excavation was also more sensitive, increasing the probability of detecting AGB larvae within a 32 m2 plot by 76.7%, and requiring significantly less time to survey an equivalent volume of soil for AGB larvae. When the quadrat method was employed, random sampling schemes provided below-ground biomass estimates that were significantly closer to the true mean of the sampling area. The results of this research will facilitate the development of IPM decision-making tools for farmers and support future research for AGB and other soil insect pests affecting mint production.

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.

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: 1–6m2/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.

Monitoring the occupancy and abundance of wildlife populations is key to evaluate their conservation status and trends. However, estimating these parameters often involves time and resource-intensive techniques, which are logistically challenging or even unfeasible for rare and elusive species that occur patchily and in small numbers. Hence, surveys based on field identification of signs (e.g. faeces, footprints) have long been considered a cost-effective alternative in wildlife monitoring, provided they produce reliable detectability and meaningful indices of population abundance. We tested the use of sign surveys for monitoring rare and otherwise elusive small mammals, focusing on the Cabrera vole (Microtus cabrerae) in Portugal. We asked how sampling intensity affects true positive detection of the species, and whether sign abundance is related to population size. We surveyed Cabrera voles’ latrines in 20 habitat patches known to be occupied, and estimated ‘true’ population size at each patch using DNA-based capture-recapture techniques. We found that a searching rate of ca. 3 min/250m2 of habitat based on adaptive guided transects was sufficient to provide true positive detection probabilities > 0.85. Sign-based abundance indices were at best moderately correlated with estimates of ‘true’ population size, and even so only for searching rates > 12 min/250m2. Our study suggests that surveys based on field identification of signs should provide a reliable option to estimate occupancy of Cabrera voles, and possibly for other rare or elusive small mammals, but cautions should be exercised when using this approach to infer population size. In case of practical constraints to the use of more accurate methods, a considerable sampling intensity is needed to reliably index Cabrera voles’ abundance from sign surveys.

1. Frequency of singletons – species represented by single individuals – is anomalously high in most large tropical arthropod surveys (average, 32%). 2. We sampled 5965 adult spiders of 352 species (29% singletons) from 1 ha of lowland tropical moist forest in Guyana. 3. Four common hypotheses (small body size, male-biased sex ratio, cryptic habits, clumped distributions) failed to explain singleton frequency. Singletons are larger than other species, not gender-biased, share no particular lifestyle, and are not clumped at 0·25–1 ha scales. 4. Monte Carlo simulation of the best-fit lognormal community shows that the observed data fit a random sample from a community of ∼700 species and 1–2 million individuals, implying approximately 4% true singleton frequency. 5. Undersampling causes systematic negative bias of species richness, and should be the default null hypothesis for singleton frequencies. 6. Drastically greater sampling intensity in tropical arthropod inventory studies is required to yield realistic species richness estimates. 7. The lognormal distribution deserves greater consideration as a richness estimator when under-sampling bias is severe.

The use of soil residual herbicides, along with other practices that diversify weed management strategies, have been recommended to improve weed management and deter the progression of herbicide resistance. Although soil characteristics influence recommended application rates for these herbicides, the common practice is to apply a uniform dose of soil residual herbicides across fields with variable soil characteristics. Mapping fields for soil characteristics that dictate the optimal dose of soil residual herbicides could improve the efficiency and effectiveness of these herbicides, as well as improve environmental stewardship. The objectives of this research were to develop and quantify the accuracy of management zone classifications for variable-rate residual herbicide applications using multiple soil data sources and soil sampling intensities. The maps were created from soil data that included (i) Soil Survey Geographic database (SSURGO), (ii) soil samples (SS), (iii) soil samples regressed onto soil electrical conductivity (EC) measurements (SSEC), (iv) soil samples with organic matter (OM) data from SmartFirmer® (SF) sensors (SSSF), and (v) soil samples regressed onto EC measurements plus OM data from SmartFirmer® sensor (SSECSF). A modified Monte Carlo cross validation method was used on ten commercial Indiana fields to generate 36,000 maps across all sources of spatial soil data, sampling density, and three representative herbicides (pyroxasulfone, s-metolachlor, and metribuzin). Maps developed from SSEC data were most frequently ranked with the highest management zone classification accuracy compared to maps developed from SS data. However, SS and SSEC maps concurrently had the highest management zone classification accuracy of 34% among maps developed across all fields, herbicides, and sampling intensities. One soil sample per hectare was the most reliable sampling intensity to generate herbicide application management zones compared to one soil sample for every 2 or 4 hectares. In conclusion, soil sampling with ECa data should be used for defining the management zones for variable-rate (VR) residual herbicide applications.

This study addresses the use of geostatistics to ensure sampling representativeness in a continuous forest inventory (CFI). A database of 89 permanent plots was used. Dominant height was employed for stratification by ordinary kriging. The correlation between the values estimated by kriging was calculated for all measurement occasions to define the earliest age for stable stratification. Growth estimates were obtained by simple random sampling (SRS) and poststratification. Mean volume and volume growth values were computed along with their sampling errors for the four growth intervals. The impact of decreased sampling intensity on volume growth precision was based on the poststratification. Site index modeling considered the reduced and full databases. The earliest age for reliable stratification was 3.1 years. Poststratification resulted in greater precision in volume growth estimates. A 40% decrease in sampling intensity did not result in significant losses in precision. Site index modeling with the reduced database had the same precision when the full database was used. Geostatistics improved the precision and reliability of the CFI statistics, because it allows the segregation of different forest sites and ensures CFI representativeness while improving the precision of the estimates and allowing decreased sampling intensity.

Key messageA random sampling between 30 and 50 trees is sufficient for forest-level wood density estimates.Wood density (WD) is a key trait used to determine forest biomass and carbon stocks, but determining WD accurately is logistically demanding and expensive. These challenges also hamper comparisons across studies and different forest types, because sampling intensity within forests and within individual trees often vary across studies. We aimed to evaluate the relationship between WD and forest type using a standardized protocol and to simulate the number of samples required to obtain a representative estimation of WD of trees belonging to different tropical vegetation types representing an increasing order of aridity: rain forest, semideciduous forest, evergreen dry forest, savannah woodland, and seasonally deciduous forest. We measured WD at five vertical profiles along the trunks of 1,671 trees representing 349 species. Using bootstrapping analyses, we modeled WD as a function of the different combinations of samples extracted at the five sampling heights and evaluated the models with the best performance. The lowest and highest mean WD values were found in rain forest and seasonally deciduous forests, respectively, in line with the correspondingly low and high aridity of these habitats. Depending on forest type, sampling approximately 30–60 trees is sufficient for stabilizing the coefficient of variation in WD. Additionally, using samples collected at 25% and 50% height from the base along the vertical profile of each tree is adequate for WD estimations. These insights could be used to develop less destructive methodologies for wood density sampling, and, thus, help to reduce costs of carbon stock inventories in tropical forests.