Spatial Sampling Research Articles

Points, cells, or polygons? On the choice of spatial units in forest conservation policy impact evaluation

Abstract A fast-growing literature uses remotely sensed land-cover data along with quasi-experimental statistical methods to assess the efficacy of forest conservation interventions. A critical modeling choice is the spatial unit of analysis—points, grid cells, and polygons are all commonly used. Yet little is known about the implications of this choice for treatment effect estimates and for their interpretation. We demonstrate that point-level data can generate treatment effect estimates substantially different from those based on polygon-level data when (i) a disproportionate share of sample points is drawn from relatively large, treated polygons as a result of random or quasi-random spatial sampling, and (ii) the intervention analyzed has heterogeneous effects that depend on treatment polygon size. Our paper has four parts. First, using real-world data (on the award of timber extraction permits to forest management units in Mexico) that meet the two aforementioned criteria, we demonstrate that point- and polygon-level data generate qualitatively different results, and we propose a simple method for weighting the point-level data to recover the polygon-level results. Second, we conduct a Monte Carlo simulation to clarify the mechanism that causes this phenomenon and to provide reassurance that it is not driven by unobserved confounding factors. Third, we present new evidence (on Mesoamerican and Dominican protected areas) suggesting this phenomenon is not uncommon. Finally, we discuss the implications of our findings for the design and interpretation of spatial evaluations of forest conservation interventions. Although our analysis focuses on point- versus polygon-level data, the mechanism we describe also applies to grid cell- versus polygon-level data.

Acoustic monitoring of anurans and birds in tropical biomes

AbstractPassive acoustic monitoring (PAM) is increasingly popular in ecological research, but recording and analyzing large amounts of data is still a critical bottleneck for the long‐term monitoring of multiple species. We evaluated how temporal and spatial sampling effort affects species diversity estimates using a set of 14,045 1‐min recordings from various neotropical birds and anuran communities. Our goals were to evaluate (i) the daily vocal activity cycle of birds and anurans, (ii) the effect of temporal structure (e.g., number of minutes listened each hour; continuous versus intermittent recordings) on determining the species composition, and (iii) the species–area relationship, and how the number of recorders affects species richness estimates. Based on sampling coverage and completeness, we (iv) evaluate manual inspection schedules for birds and anurans across four biomes of Brazil. We found marked diel variation in vocal activity between taxonomic groups, indicating that birds and anurans are more efficiently detected during early periods of the day and night, respectively. For proper diversity estimates, biomes with higher biodiversity required longer inspecting periods and a larger number of replicates, irrespective of taxa. Although fewer recordings per hour are less informative than full‐hour sampling, species diversity is better estimated when inspected minutes are interspersed over longer periods than inspecting minutes recorded over shorter timespans. Based on our findings, we recommend how to set PAM programs over highly diverse ecosystems.Abstract in Portuguese is available with online material.

Nanoscale Spectroscopic Identification and Characterization of Minerals and Organic Matter in Ryugu Particles

AbstractCarbonaceous asteroids are leftover materials from the early solar system. They did not undergo planetary differentiation processes and thus still retained signatures and clues of the origin and evolution of the solar system. Thus, samples of carbonaceous asteroids provide significant potential for deciphering the origin of the solar system. The Hayabusa2 spacecraft returned the first samples of a carbonaceous asteroid (162173) Ryugu. In this work, we report the nanoscale mineralogy and organic matter content of two Ryugu particles, A0030 and C0034. These were analyzed by highly novel scattering‐type scanning near‐field optical microscopy (s‐SNOM)‐based nanoscale‐Fourier transform infrared (nano‐FTIR) spectroscopy with ∼20 nm spatial resolution. The nano‐FTIR measurements were supported by confocal micro‐Raman imaging and spectroscopy (∼500 nm/pixel spatial sampling). Our investigations show that the two Ryugu particles (a) contain different silicate mineralogies, indicating heterogeneous and incomplete aqueous alteration and (b) are highly rich in organic matter, consisting a variety of molecular functional groups. The spatial distributions of chemical functional groups and their associations based on pseudo‐heterodyne (PsHet) SNOM imaging show that the organic matter is distributed as either diffused or discrete grains. Micro‐Raman spectra show that the Ryugu particles experienced minimal thermal metamorphism, although A0030 was slightly more heated than C0034. The identification of abundant nanoscale organic molecules within the Ryugu grains that could not be identified via micrometer‐scale investigations emphasizes the importance of using nanoscale nondestructive methods for studying primitive solar system materials, such as Ryugu and OSIRIS‐Rex particles and those that will be returned in the future (such as MMX samples).

Well-spread samples with dynamic sample sizes.

A spatial sampling design determines where sample locations are placed in a study area so that population parameters can be estimated with relatively high precision. If the response variable has spatial trends, spatially balanced or well-spread designs give precise results for commonly used estimators. This article proposes a new method that draws well-spread samples over arbitrary auxiliary spaces and can be used for master sampling applications. All we require is a measure of the distance between population units. Numerical results show that the method generates well-spread samples and compares favorably with existing designs. We provide an example application using several auxiliary variables to estimate total aboveground biomass over a large study area in Eastern Amazonia, Brazil. Multipurpose surveys are also considered, where the totals of aboveground biomass, primary production, and clay content (3 responses) are estimated from a single well-spread sample over the auxiliary space.

Six Steps towards a Spatial Design for Large-Scale Pollinator Surveillance Monitoring

Despite the importance of pollinators to ecosystem functioning and human food production, comprehensive pollinator monitoring data are still lacking across most regions of the world. Policy-makers have recently prioritised the development of large-scale monitoring programmes for pollinators to better understand how populations respond to land use, environmental change and restoration measures in the long term. Designing such a monitoring programme is challenging, partly because it requires both ecological knowledge and advanced knowledge in sampling design. This study aims to develop a conceptual framework to facilitate the spatial sampling design of large-scale surveillance monitoring. The system is designed to detect changes in pollinator species abundances and richness, focusing on temperate agroecosystems. The sampling design needs to be scientifically robust to address questions of agri-environmental policy at the scales of interest. To this end, we followed a six-step procedure as follows: (1) defining the spatial sampling units, (2) defining and delimiting the monitoring area, (3) deciding on the general sampling strategy, (4) determining the sample size, (5) specifying the sampling units per sampling interval, and (6) specifying the pollinator survey plots within each sampling unit. As a case study, we apply this framework to the “Wild bee monitoring in agricultural landscapes of Germany” programme. We suggest this six-step procedure as a conceptual guideline for the spatial sampling design of future large-scale pollinator monitoring initiatives.

Technique for enhancing the accuracy of the Rayleigh-Sommerfeld convolutional diffraction through the utilization of independent spatial sampling: publisher's note.

This publisher's note contains a correction to Opt. Lett.49, 1385 (2024)10.1364/OL.509688.

Integrating presence‐only and detection/non‐detection data to estimate distributions and expected abundance of difficult‐to‐monitor species on a landscape‐scale

Abstract Estimating species distribution and abundance is foundational to effective management and conservation. Using an integrated species distribution model that combines presence‐only data from various sources with detection/non‐detection data from structured surveys, we estimated the distribution and expected abundance of three difficult‐to‐monitor mammals of management concern across New York State, namely, coyotes (Canis latrans), bobcats (Lynx rufus) and black bears (Ursus americanus). Three distinct landscape‐scale camera trap surveys provided detection/non‐detection data over 9 years between 2013 and 2021, and we augmented those data with incidental records of our focal species from public repositories. We used an inhomogeneous Poisson point process to construct an integrated model that fit both data types simultaneously. We demonstrate a simple application of spatial point density of all species records in the accessed public databases to inform the thinning process to account for unknown spatial sampling in the presence‐only data, often referred to as the ‘magic covariate’. Using this approach, we examine habitat associations and provide spatially explicit estimates in expected abundance across the entirety of New York State for all three focal species. As expected, coyotes were the most widely distributed and abundant species, with a strong positive association with agricultural land uses. Bobcats exhibited low expected abundance throughout the state and showed positive associations with deciduous forest and forest edge, and a negative association with road density. Finally, we observed considerable spatial variation in abundance of black bears with expected abundance increasing in association with various forest cover and composition covariates and decreasing with crop cover. We present insights into habitat associations and spatial variation in abundance, and provide management implications for each of the species of interest. Synthesis and applications. Our integrated modelling method allows for managers to use citizen sightings combined with detection/non‐detection surveys to estimate robust indices of abundance for both high‐ and low‐density, and wide‐spread versus patchily distributed species. Through comparison with previous studies, we highlight how broad‐scale programmes, such as the statewide efforts to estimate species distributions undertaken here, can benefit substantively from integrated models that leverage additional data (here, incidental records) from a larger region of space, and thus capture more landscape heterogeneity than is plausible within formalized surveys alone.

PersistVerify: Federated model ownership verification with spatial attention and boundary sampling

Federated learning, known for its emphasis on privacy and resource efficiency, has emerged as a transformative paradigm in the fields of artificial intelligence and industrial machine learning. However, with the advancement and widespread adoption of this technology, the concern of model theft in federated learning models becomes increasingly prominent. While current endeavors concentrate on protecting individual deep learning models, limited attention has been dedicated to those involved in federated learning, especially within decentralized environments. To fill this void, we introduce PersistVerify, an innovative approach for verifying model copyright within the federated learning framework. PersistVerify integrates boundary sample selection and spatial attention mechanisms, enhancing the robustness and confidentiality of neural network backdoor watermarks to ensure secure verification of model ownership. PersistVerify constructs a confidential and robust watermark dataset through three sequential steps: latent representation extraction, boundary sample selection, and spatial attention-based trigger embedding. Our research addresses the pervasive challenge of reliable model ownership verification in federated learning, demonstrating the efficacy of PersistVerify. This study provides valuable insights and methodologies to maintain the confidentiality and reliability of neural network backdoor watermarks in the domains of artificial intelligence and federated learning.

Rise and diversification of chondrichthyans in the Paleozoic

Abstract The Paleozoic represents a key time interval in the origins and early diversification of chondrichthyans (cartilaginous fishes), but their diversity and macroevolution are largely obscured by heterogenous spatial and temporal sampling. The predominantly cartilaginous skeletons of chondrichthyans pose an additional limitation on their preservation potential and hence on the quality of their fossil record. Here, we use a newly compiled genus-level dataset and the application of sampling standardization methods to analyze global total-chondrichthyan diversity dynamics through time from their first appearance in the Ordovician through to the end of the Permian. Subsampled estimates of chondrichthyan genus richness were initially low in the Ordovician and Silurian but increased substantially in the Early Devonian. Richness reached its maximum in the middle Carboniferous before dropping across the Carboniferous/Permian boundary and gradually decreasing throughout the Permian. Sampling is higher in both the Devonian and Carboniferous compared with the Silurian and most of the Permian stages. Shark-like scales from the Ordovician are too limited to allow for some of the subsampling techniques. Our results detect two Paleozoic radiations in chondrichthyan diversity: the first in the earliest Devonian, led by acanthodians (stem-group chondrichthyans), which then decline rapidly by the Late Devonian, and the second in the earliest Carboniferous, led by holocephalans, which increase greatly in richness across the Devonian/Carboniferous boundary. Dispersal of chondrichthyans, specifically holocephalans, into deeper-water environments may reflect a niche expansion following the faunal displacement in the aftermath of the Hangenberg extinction event at the end of the Devonian.

DAS microseismic reflection imaging for hydraulic fracture and fault lineament characterization

This study presents a novel workflow designed for migrating reflected S-waves generated by microseismic events, as recorded by downhole Distributed Acoustic Sensing (DAS), to characterize hydraulic fractures in three dimensions. In contrast to existing fracture imaging techniques, which have encountered challenges in accurately representing fracture networks and often rely on simplified models, the proposed imaging technique does not assume that fractures are planar or in a pre-specified orientation. DAS seismic measurements benefit from the large aperture and dense spatial sampling enabled by the kilometers-long fiber and, therefore are able to capture a large number of strong reflections compared to traditional borehole geophones or accelerometers. We treat microseismic events as high-frequency sources and apply prestack Kirchhoff migration to each individual source after wavefield separation. Fracture imaging results for multiple selected events are then stacked to generate a 3D reflectivity volume, revealing subsurface fracture and fault networks in intricate detail. The high-resolution fracture images generated by the developed reflection migrating process illuminate the heart of the stimulated volume of the reservoir, a zone that is often challenging to access using conventional surface arrays or active sources. To validate the effectiveness of the proposed workflow, our study employs a dataset acquired during a multi-well project in the Eagle Ford Shale and Austin Chalk in South Texas. To assess the accuracy and reliability of the results, the reflection imaging output is integrated with both microseismicity distribution and strain measurements from low-frequency DAS for interpretation. The results of reflection imaging improve our understanding of fracture geometry including distal fractures that are away from the monitoring well, allow direct estimation of fracture height and length, and potentially signify the presence of pre-existing fluid-filled fault lineaments.

Ex-vivo drug screening of surgically resected glioma stem cells to replace murine avatars and provide personalise cancer therapy for glioblastoma patients

With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas.

Promising Candidate Prognostic Biomarkers in [18F]FDG PET Images: Evaluation in Independent Cohorts of Non-Small Cell Lung Cancer Patients.

The normalized distances from the hot spot of radiotracer uptake (SUVmax) to the tumor centroid (NHOC) and to the tumor perimeter (NHOP) have recently been suggested as novel PET features reflecting tumor aggressiveness. These biomarkers characterizing the shift of SUVmax toward the lesion edge during tumor progression have been shown to be prognostic factors in breast and non-small cell lung cancer (NSCLC) patients. We assessed the impact of imaging parameters on NHOC and NHOP, their complementarity to conventional PET features, and their prognostic value for advanced-NSCLC patients. Methods: This retrospective study investigated baseline [18F]FDG PET scans: cohort 1 included 99 NSCLC patients with no treatment-related inclusion criteria (robustness study); cohort 2 included 244 NSCLC patients (survival analysis) treated with targeted therapy (93), immunotherapy (63), or immunochemotherapy (88). Although 98% of patients had metastases, radiomic features including SUVs were extracted from the primary tumor only. NHOCs and NHOPs were computed using 2 approaches: the normalized distance from the localization of SUVmax or SUVpeak to the tumor centroid or perimeter. Bland-Altman analyses were performed to investigate the impact of both spatial resolution (comparing PET images with and without gaussian postfiltering) and image sampling (comparing 2 voxel sizes) on feature values. The correlation of NHOCs and NHOPs with other features was studied using Spearman correlation coefficients (r). The ability of NHOCs and NHOPs to predict overall survival (OS) was estimated using the Kaplan-Meier method. Results: In cohort 1, NHOC and NHOP features were more robust to image filtering and to resampling than were SUVs. The correlations were weak between NHOCs and NHOPs (r ≤ 0.45) and between NHOCs or NHOPs and any other radiomic features (r ≤ 0.60). In cohort 2, the patients with short OS demonstrated higher NHOCs and lower NHOPs than those with long OS. NHOCs significantly distinguished 2 survival profiles in patients treated with immunotherapy (log-rank test, P < 0.01), whereas NHOPs stratified patients regarding OS in the targeted therapy (P = 0.02) and immunotherapy (P < 0.01) subcohorts. Conclusion: Our findings suggest that even in advanced NSCLC patients, NHOC and NHOP features pertaining to the primary tumor have prognostic potential. Moreover, these features appeared to be robust with respect to imaging protocol parameters and complementary to other radiomic features and are now available in LIFEx software to be independently tested by others.

Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics

Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.

Grid Turbulence Measurements with an Acoustic Doppler Current Profiler

The motivation for this study is to investigate the abilities and limitations of a Nortek Signature1000 acoustic Doppler current profiler (ADCP) regarding fine-scale turbulence measurements. Current profilers offer the advantage of gaining more coherent measurement data than available with point acoustic measurements, and it is desirable to exploit this property in laboratory and field applications. The ADCP was tested in a towing tank, where turbulence was generated from a grid towed under controlled conditions. Grid-induced turbulence is a well-studied phenomenon and a good approximation for isotropic turbulence. Several previous experiments are available for comparison and there are developed theories within the topic. In the present experiments, a Nortek Vectrino acoustic Doppler velocimeter (ADV), which is an established instrument for turbulence measurements, was applied to validate the ADCP. It was found that the mean flow measured with the ADCP was accurate within 4% of the ADV. The turbulent variance was reasonably well resolved by the ADCP when large grid bars were towed at a high speed, but largely overestimated for lower towing speed and smaller grid bars. The effective cutoff frequency and turbulent eddy size were characterized experimentally, which provides detailed guidelines for when the ADCP data can be trusted and will allow future experimentalists to decide a priori if the Nortek Signature can be used in their setup. We conclude that the ADCP is not suitable for resolving turbulent spectra in a small-scale grid-induced flow due to the intrinsic Doppler noise and the low spatial and temporal sample resolution relative to the turbulent scales.

Direct Analysis of Marine Dissolved Organic Matter Using LC-FT-ICR MS.

Marine dissolved organic matter (DOM) is an important component of the global carbon cycle, yet its intricate composition and the sea salt matrix pose major challenges for chemical analysis. We introduce a direct injection, reversed-phase liquid chromatography ultrahigh resolution mass spectrometry approach to analyze marine DOM without the need for solid-phase extraction. Effective separation of salt and DOM is achieved with a large chromatographic column and an extended isocratic aqueous step. Postcolumn dilution of the sample flow with buffer-free solvents and implementing a counter gradient reduced salt buildup in the ion source and resulted in excellent repeatability. With this method, over 5,500 unique molecular formulas were detected from just 5.5 nmol carbon in 100 μL of filtered Arctic Ocean seawater. We observed a highly linear detector response for variable sample carbon concentrations and a high robustness against the salt matrix. Compared to solid-phase extracted DOM, our direct injection method demonstrated superior sensitivity for heteroatom-containing DOM. The direct analysis of seawater offers fast and simple sample preparation and avoids fractionation introduced by extraction. The method facilitates studies in environments, where only minimal sample volume is available e.g. in marine sediment pore water, ice cores, or permafrost soil solution. The small volume requirement also supports higher spatial (e.g., in soils) or temporal sample resolution (e.g., in culture experiments). Chromatographic separation adds further chemical information to molecular formulas, enhancing our understanding of marine biogeochemistry, chemodiversity, and ecological processes.

Thousand‐Kilometer DAS Array Reveals an Uncatalogued Magnitude‐5 Dynamically Triggered Event After the 2023 Turkey Earthquake

AbstractLarge earthquakes can trigger smaller seismic events, even at significant distances. The process of earthquake triggering offers valuable insights into the evolution of local stress states, deepening our understanding of the mechanisms of earthquake nucleation. However, our ability to detect these triggered events is limited by the quality and spatial density of local seismometers, posing significant challenges if the triggered event is hidden in the signal of a nearby larger earthquake. Distributed acoustic sensing (DAS) has the potential to enhance the monitoring capability of triggered earthquakes through its high spatial sampling and large spatial coverage. Here, we report on an uncatalogued magnitude (M) 5.1 event in northeast Turkey, which was likely dynamically and instantaneously triggered by the 2023 M7.8 earthquake in southeast Turkey, located 400 km away. This event was initially discovered on ∼1,100 km of active DAS recordings that are part of an 1,850‐km linear array. Subsequent validation using local seismometers confirmed the event's precise time, location, and magnitude. Interestingly, this dynamically triggered event exhibited precursory signals preceding its P arrivals on the nearby seismometers. It can be interpreted as the signal from other nearby, uncatalogued, smaller triggered events. Our results highlight the potential of high‐spatial‐density DAS in enhancing the local‐scale detection and the detailed analysis of earthquake triggering.

The PiGas: A low-cost approach to volcanic gas sampling

Instruments which are designed to measure multiple gases are widely used across several industries with a common focus on environmental gas sampling. Reliable instrumentation and gas sensors are therefore vital for the monitoring of air quality in cities and in more challenging scenarios, for example volcanic gas release. However, such instrumentation is commonly expensive and can be difficult to deploy, meaning that large instrument networks - needed to assess spatial air quality differences - is inhibitive. A bespoke do-it-yourself approach can therefore be beneficial. Here, we detail a new instrument, the PiGas, which is modelled on the successful “Multi-GAS” technique (Shinohara, 2005; Aiuppa et al. 2005) for the measurement of carbon dioxide (CO2), sulphur dioxide (SO2), hydrogen sulphide (H2S), and water vapour (H2O). Indeed, CO2 is a common gas of interest yet its measurement at high accuracy can be expensive. We demonstrate a cost reduction to <£500 by adapting parts used from non-air sampling specific industries and through use of a lower-cost and lower-power requirement CO2 sensor (the CozIR A-H). We demonstrate the versatility of the PiGas for monitoring indoor and outdoor air quality as a diffuse and pumped sensor. We also test the instrument in two extreme volcanic environments at the high altitude Alitar and Volcán Lastarria (Chile). Overall, the instrumentation shows potential for more widespread co-ordinated use to improve spatial sampling distributions in low concentration scenarios as a supplement to higher accuracy and precision instruments in volcanic environments.

Technique for enhancing the accuracy of the Rayleigh-Sommerfeld convolutional diffraction through the utilization of independent spatial sampling.

The Rayleigh-Sommerfeld diffraction integral (RSD) is a rigorous solution that precisely satisfies both Maxwell's equations and Helmholtz's equations. It seamlessly integrates Huygens' principle, providing an accurate description of the coherent light propagation within the entire diffraction field. Therefore, the rapid and precise computation of the RSD is crucial for light transport simulation and optical technology applications based on it. However, the current FFT-based Rayleigh-Sommerfeld integral convolution algorithm (CRSD) exhibits poor performance in the near field, thereby limiting its applicability and impeding further development across various fields. The present study proposes, to our knowledge, a novel approach to enhance the accuracy of the Rayleigh-Sommerfeld convolution algorithm by employing independent sampling techniques in both spatial and frequency domains. The crux of this methodology involves segregating the spatial and frequency domains, followed by autonomous sampling within each domain. The proposed method significantly enhances the accuracy of RSD during the short distance while ensuring computational efficiency.

Intensive temporal and spatial sampling of polychaete biodiversity reveals significant implications for impact assessment and marine park design

Abstract Intensive temporal and spatial sampling of benthic infauna have rarely been analysed to improve impact assessment or conservation planning. An impact assessment started in the late 1980s in Jervis Bay (Australia) provided a spatially, temporally and taxonomically comprehensive benthic invertebrate dataset. While the proposed development did not eventuate, the area was later declared a multi‐zone marine park. We specifically used the polychaete data which included nine sampling times (February 1989 to June 1991) in seagrass and unvegetated sediments across multiple sites to identify ecological patterns that could inform current and future conservation management and impact assessment. The polychaete assemblage was diverse with 171 species, many of them rare (40% &lt; 10 individuals). Variation was greatest at small spatial scales (metres) for abundances and diversity, and there was substantial spatio‐temporal variation due to inconsistent site differences. Most species occurred in both unvegetated and seagrass sediments, though several were more abundant or restricted to one habitat (39% and 3% only found in unvegetated or seagrass, respectively), or showed bathymetric differences in abundances (e.g., Eunice cultrifera and Lumbrineris cf. latrelli). The current marine park zoning representatively covered polychaete biodiversity, as all sites were generally similar in terms of abundances and species richness. It also representatively covered seagrass and unvegetated sediments and the bathymetric range of the embayment. These findings reinforce the utility of using habitats as surrogates to representatively zone for biological diversity. The high levels of spatial and spatio‐temporal variation in abundances and diversity only allowed detection of environmental impacts with &gt;70% effect sizes even if numerous sites and times were sampled (i.e., &gt; seven sites and &gt; eight times). This suggests that most univariate measures for polychaetes would only be useful to detect enormous effects, and effective impact assessment might require more sophisticated multivariate approaches.

Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic.

The Arctic ecosystems and their species are exposed to amplified climate warming and, in some regions, to rapidly developing economic activities. This study assesses, models, and maps the geographic patterns of community-level plant species richness in the Western Siberian Arctic and estimates the relative impact of environmental and anthropogenic factors driving these patterns. With our study, we aim at contributing toward conservation efforts for Arctic plant diversity in the Western Siberian Arctic. Western Siberian Arctic, Russia. We investigated the relative importance of environmental and anthropogenic predictors of community-level plant species richness in the Western Siberian Arctic using macroecological models trained with an extensive geobotanical dataset. We included vascular plants, mosses and lichens in our analysis, as non-vascular plants substantially contribute to species richness and ecosystem functions in the Arctic. We found that the mean community-level plant species richness in this vast Arctic region does not decrease with increasing latitude. Instead, we identified an increase in species richness from South-West to North-East, which can be well explained by environmental factors. We found that paleoclimatic factors exhibit higher explained deviance compared to contemporary climate predictors, potentially indicating a lasting impact of ancient climate on tundra plant species richness. We also show that the existing protected areas cover only a small fraction of the regions with highest species richness. Our results reveal complex spatial patterns of community-level species richness in the Western Siberian Arctic. We show that climatic factors such as temperature (including paleotemperature) and precipitation are the main drivers of plant species richness in this area, and the role of relief is clearly secondary. We suggest that while community-level plant species richness is mostly driven by environmental factors, an improved spatial sampling will be needed to robustly and more precisely assess the impact of human activities on community-level species richness patterns. Our approach and results can be used to design conservation strategies and to investigate drivers of plant species richness in other arctic regions.