Optimal Sampling Design Research Articles

Optimization of sampling design for soil total organic carbon assessment in the precision agriculture framework: Impact of different variogram models and potentiality of ground penetrating radar (GPR) covariate information

Assessing soil organic carbon (SOC) at the field scale is crucial for efficient environmental and agronomic management, especially within the precision agriculture framework. This enables the implementation of crop and soil management strategies to enhance soil quality, increase carbon sequestration, and improve crop yields. However, the process of sampling and assessing SOC is resource-intensive, demanding both in time and labour. This aspect is particularly relevant in agronomic research, where the need to assess the spatial correlation structure of the investigated variables requires the collection of large datasets with georeferenced data and often measurements need to be repeated over time as in the case long-term field experiments − LTE. Methods for minimizing information loss while reducing the sampling scheme, such as spatial simulated annealing (SSA), are particularly valuable in the scope of SOC assessment, especially when faced with budget and time/labour constraints. Within the structure of the SSA method, two critical components can be identified: i) the inclusion of highly informative covariates for the primary variable (SOC); ii) the selection of the most appropriate variogram model for spatial variability assessment. Covariates strongly correlated with SOC, such as those obtained from ground penetrating radar (GPR) that can be collected at a higher spatial density compared to SOC data, along with a well-performing model, can significantly enhance the efficiency of the sampling scheme reduction process. We conducted a study using data from an agronomic field experiment, which included 71 georeferenced sampling locations and, through an iterative downsizing process utilizing SSA, we progressively reduced the number of sampling points by removing 10, 15, and 20 observations. The sampling scheme was refined according to two distinct variogram models: the spherical and Gaussian-Matérn models, both for the primary variable (SOC) and the covariate GPR variable. This process allowed us to identify the optimal variogram model, which has a key role in maximizing the reduction of redundant points while preserving those with valuable information, and to assess the role of the covariate variable both in improving the optimization of the sampling scheme for SOC and in replacing the primary variable to optimize the sampling scheme. Finally, to assess the impact of sampling scheme reduction, a validation process was performed by estimating the dropped points by means of the remaining points using ordinary kriging and regression kriging and analysing the accuracy of such estimation. Our analysis demonstrated that it was possible to reduce the original sampling scheme by approximately 20%, equivalent to eliminating 15 sampling points out of 71, without compromising its predictive capability.

Optimizing Soil Sampling for Accurately Prediction of the Potential Remediation-Effective Area in a Contaminated Agricultural Land.

To achieve food security in a contaminated agricultural land, the remediation areas usually need more samples to obtain accurate contamination information and implement appropriate measures. In this study, we propose an optimal encryption sampling design to instead of the detailed survey, which is determined by the variation of heavy metals and the technology capability of remediation, to guide soil sampling for accurately remediation in the potential remediation-effective areas (PRA). The coefficient of screening variation threshold (CSVT), considering spatial variation, technology capacity and acceptable error of sampling, together with the spatial cyclic statistics method of neighbourhood analysis, is introduced to identify and delineate the PRA. Both of the hypothetical analysis and application case studies are conducted to illustrate the advantages and disadvantages of the optimization. The results show that, compared with the detailed survey, the optimal design shows a lower overall accuracy due to its sparsely sampling at the clean area, but it exhibits a similar effect of accurately prediction in boundary delineation and further classification in the PRA in both simulation and application studies. This work provides an effective method for subsequent accurate remediation at the investigation stage and valuable insights into application combination of technology capacity and contaminated agricultural land investigation.

Exact optimisation of spatiotemporal monitoring networks by p‐splines with applications in groundwater assessment

AbstractThis paper develops methods to optimise the sampling strategy for monitoring networks which have fixed locations with regular sampling but with only a proportion of these locations to be used on each sampling occasion. This creates the need for a dynamic spatiotemporal sampling design which makes optimal choices of the locations to be sampled on each occasion. This is a commonly occurring scenario in many environmental settings where there is an existing network of monitoring stations and sampling can be expensive. The particular context of optimisation of an existing groundwater monitoring network is discussed in the paper. The standard design criteria of integrated variance (IV) and variance of the integral (VI) are adapted to the spatiotemporal setting. p‐spline models are shown to allow exact computation of IV and VI, in the case of the additive errors, and a very good approximation of IV in the case of multiplicative errors. The speed of these exact computations allows the globally optimal sampling design to be identified efficiently. In the standard case of additive errors, the design criteria are able to exploit information across time. The only information needed is the location of sampling points, not the values sampled. This contrasts with the case of multiplicative errors where the design criteria are also influenced by the observed response data. Simulated and real examples are used to illustrate the results throughout.

Optimal two-phase sampling for comparing correlated areas under the ROC curves of two screening tests in the presence of verification bias

ABSTRACT The accuracy of a screening test is often measured by the area under the receiver characteristic (ROC) curve (AUC) of a screening test. Two-phase designs have been widely used in diagnostic studies for estimating one single AUC and comparing two AUCs where the screening test results are measured for a large sample (Phase one sample) while the disease status is only verified for a subset of Phase one sample (Phase two sample) by a gold standard. In this paper, we consider the optimal two-phase sampling design for comparing the performance of two ordinal screening tests in classifying disease status. Specifically, we derive an analytical variance formula for the AUC difference estimator and use it to find the optimal sampling probabilities that minimize the variance formula for the AUC difference estimator. According to the proposed optimal two-phase design, the strata with the levels of two tests far apart from each other should be over-sampled while the strata with the levels of two tests close to each other should be under-sampled. Simulation results indicate that two-phase sampling under optimal allocation (OA) achieves a substantial amount of variance reduction, compared with two-phase sampling under proportional allocation (PA). Furthermore, in comparison with a one-phase random sampling, two-phase sampling under OA or PA has a clear advantage in reducing the variance of AUC difference estimator when the variances of the two screening test results in the disease population differ greatly from their counterparts in non-disease population.

Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies

Abstract Camera traps have transformed the way we monitor wildlife and are now routinely used to address questions from a wide range of ecological and conservation aspects. Sampling design optimization and a better understanding of drivers determining the precision of detection rates (i.e. the number of detections per unit of effort) are important methodological issues. Little attention has been focused on the effect of placing more than one camera on each sampling point (hereafter, clustered design), and/or rotating (i.e. redeploying) the cameras to new placements during the sampling period. We explored the differences in the precision of detection rates between clustered vs. single camera designs when cameras remained in the same location during the study. Furthermore, the effect of keeping the placement of cameras fixed or rotating them (i.e. moving them to new locations during the sampling period), when a limited number of camera devices are available, was also evaluated. We used simulations and field data to test differences in detection rate precision for the different sampling designs. We simulated three different population distributions (random, trail‐based and aggregated) and three abundance scenarios. The simulations were validated with a field experiment focused on eight species with different behavioural traits, including artiodactyls, carnivores, lagomorphs, and birds. When a fixed number of sampling points were monitored simultaneously, clustered designs generally resulted in an increase in the precision of detection rates compared to single designs. The absolute reduction in the coefficient of variation by clustered designs was on average 0.07 units (min: 0.01, max: 0.15), which represents an average relative reduction in CV of 31% (min:6%, max:44%). An improvement in precision was also observed as a higher number of sampling points was used for all population distributions and sampling designs tested. When a fixed number of cameras were available, rotating the cameras to independent locations improved precision (an absolute reduction of 0.19 CV units) when monitoring aggregated populations, but not for random and trail‐based population distributions. Synthesis and applications: Our research provides a guideline for wildlife managers and researchers to improve the precision of camera trap detection rates and optimize resource allocation. In general, the study design should accommodate the behaviour of the target species (e.g. spatial aggregation and abundance), monitoring program logistic resources (both human and economic) and study area characteristics (e.g. accessibility and vandalism).

Time stability of soil volumetric water content and its optimal sampling design in contrasting forest catchments

Characterizing the temporal variation across a forest catchment soil volumetric water content (VWC) with different environmental conditions at different depths for different seasons requires a robust point-scale sampling strategy that balances monitoring cost with predicting accuracy. In the study, the most time-stable locations (MTSLs) using the time-stable concept and the minimum number of required samples (NRS) based on random selection method were studied to explore the optimal sampling design for catchment-mean VWC with an acceptable error (the relative bias (RBIAS) less than 10 %). VWC monitoring was conducted at approximately 25 plots throughout the year in five forest catchments with varying areas and VWC status. Results revealed that the number of MTSLs for mean-VWC estimation with RBIAS within 10 % was directly related to the spatial variability of VWC. Specifically, one MTSL was feasible for accurately estimating mean VWC at different depths in high-VWC catchments with area around or less than 100 ha; one MTSL was adequate for each depth in the catchment with moderate-level VWC; whereas more than one MTSL were necessary for each depth in low-VWC catchment with a larger are of 1800 ha. To identify the MTSL for each catchment, the location with mean VWC was typically situated at the mid-elevation or soil apparent electrical conductivity (ECa) at a certain spatial scale. The NRS for catchment-mean VWC could be determined based on the combination of VWC status, its temporal stability, and potential catchment size, and could be ranked based on the spatial variation of each catchment. If the MTSL cannot be guaranteed for a catchment, the NRS might be a better alternative for optimum sampling design. These finding could be useful in providing guidelines for optimizing VWC monitoring in forest catchments by sampling at a few selected locations representative of the catchment-scale behavior.

Contribution of gas concentration and transfer velocity to CO2 flux variability in northern lakes

AbstractThe CO2 flux () from lakes to the atmosphere is a large component of the global carbon cycle and depends on the air–water CO2 concentration gradient (ΔCO2) and the gas transfer velocity (k). Both ΔCO2 and k can vary on multiple timescales and understanding their contributions to is important for explaining variability in fluxes and developing optimal sampling designs. We measured and ΔCO2 and derived k for one full ice‐free period in 18 lakes using floating chambers and estimated the contributions of ΔCO2 and k to variability. Generally, k contributed more than ΔCO2 to short‐term (1–9 d) variability. With increased temporal period, the contribution of k to variability decreased, and in some lakes resulted in ΔCO2 contributing more than k to variability over the full ice‐free period. Increased contribution of ΔCO2 to variability over time occurred across all lakes but was most apparent in large‐volume southern‐boreal lakes and in deeper (> 2 m) parts of lakes, whereas k was linked to variability in shallow waters. Accordingly, knowing the variability of both k and ΔCO2 over time and space is needed for accurate modeling of from these variables. We conclude that priority in assessments should be given to direct measurements of at multiple sites when possible, or otherwise from spatially distributed measurements of ΔCO2 combined with k‐models that incorporate spatial variability of lake thermal structure and meteorology.

Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests.

From passive acoustic monitoring (PAM) recordings, the vocal activity rate (VAR), vocalizations per unit of time, can be calculated and is essential for assessing bird population abundance. However, VAR is subject to influences from a range of factors, including species and environmental conditions. Identifying the optimal sampling design to obtain representative acoustic data for VAR estimation is crucial for research objectives. PAM commonly uses temporal sampling strategies to decrease the volume of recordings and the resources needed for audio data management. Yet, the comprehensive impact of this sampling approach on VAR estimation remains insufficiently explored. In this study, we used vocalizations extracted from recordings of 12 bird species, taken at 14 PAM stations situated in subtropical montane forests over a four-month period, to assess the impact of temporal sampling on VAR across three distinct scales: short-term periodic, diel, and hourly. For short-term periodic sampling analysis, we employed hierarchical clustering analysis (HCA) and the coefficient of variation (CV). Generalized additive models (GAMs) were utilized for diel sampling analysis, and we determined the average difference in VAR values per minute for the hourly sampling analysis. We identified significant day and species-specific VAR fluctuations. The survey season was divided into five segments; the earliest two showed high variability and are best avoided for surveys. Data from days with heavy rain and strong winds showed reduced VAR values and should be excluded from analysis. Continuous recordings spanning at least seven days, extending to 14 days is optimal for minimizing sampling variance. Morning chorus recordings effectively capture the majority of bird vocalizations, and hourly sampling with frequent, shorter intervals aligns closely with continuous recording outcomes. While our findings are context-specific, they highlight the significance of strategic sampling in avian monitoring, optimizing resource utilization and enhancing the breadth of monitoring efforts.

Optimal sampling designs for multidimensional streaming time series with application to power grid sensor data

Optimal sampling designs for multidimensional streaming time series with application to power grid sensor data

Lateral variations of shear wave velocity (VS) profile and VS30 over gentle terrain

Lateral variations of shear wave velocity (VS) profile and time-averaged shear wave velocity in the upper 30 m (VS30) are important indicators of site condition and significant contributors to uncertainty in earthquake ground motion and seismic hazards. Few previous studies involved in quantifying these lateral variations, especially at short distances, due to limited data at closely spaced sites. This study quantitatively investigated the lateral variation of VS profile and VS30 over plain and piedmont terrains, focusing on short distances ranging from hundreds of meters to several kilometers. 2510 plain and 749 piedmont borehole profiles from the mainland China borehole profile database were employed to examine these variations using the variogram as the geo-statistics tool. The main findings include: 1) Over plain terrain, the variation in site conditions between sites does not significantly increase with separation distance within a certain range (typically from 1 km to 3–5 km). Over piedmont terrain, site condition variation steadily increases with distance. 2) The variation of site condition over piedmont terrain is higher than that over plain terrain. Nonetheless, for both terrains, the dispersion of site condition between sites at distance of <1 km is engineering negligible, and the correlation of site conditions between sites within 5 km remains strong; 3) For both terrains, the lateral variation of VS in deeper layers is higher than that in shallower layers. The study outcomes emphasize the importance of incorporating the information of nearby measurements in site condition estimations. The study outcomes can also be utilized in the optimization of borehole sampling design for microzonation projects and the evaluation of site condition dispersion at inferred sites.

Movedesign: Shiny R app to evaluate sampling design for animal movement studies

Abstract Projects focused on movement behaviour and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. Without these guidelines, designing an animal tracking study to produce reliable estimates of space‐use and movement properties (necessary to answer basic movement ecology questions), is often done in an ad hoc manner. We developed ‘movedesign’, a user‐friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed and distance travelled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration). Leveraging the ‘ctmm’ R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated kernel density estimators (AKDEs) and continuous‐time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade‐off between long battery life and high resolution of GPS locations collected by the devices, which may result in a compromise between reliably estimating home range or speed and distance. ‘movedesign’ has broad applications for researchers and decision‐makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade‐off associated with these choices.

Optimal two-stage spatial sampling design for estimating critical parameters of SARS-CoV-2 epidemic: Efficiency versus feasibility

The COVID-19 pandemic presents an unprecedented clinical and healthcare challenge for the many medical researchers who are attempting to prevent its worldwide spread. It also presents a challenge for statisticians involved in designing appropriate sampling plans to estimate the crucial parameters of the pandemic. These plans are necessary for monitoring and surveillance of the phenomenon and evaluating health policies. In this respect, we can use spatial information and aggregate data regarding the number of verified infections (either hospitalized or in compulsory quarantine) to improve the standard two-stage sampling design broadly adopted for studying human populations. We present an optimal spatial sampling design based on spatially balanced sampling techniques. We prove its relative performance analytically in comparison to other competing sampling plans, and we also study its properties through a series of Monte Carlo experiments. Considering the optimal theoretical properties of the proposed sampling plan and its feasibility, we discuss suboptimal designs that approximate well optimality and are more readily applicable.

Practical strategies for operationalizing optimal allocationin stratified cluster-based outcome-dependent sampling designs.

Cluster-based outcome-dependent sampling (ODS) has the potential to yield efficiency gains when the outcome of interest is relatively rare, and resource constraints allow only a certain number of clusters to be visited for data collection. Previous research has shown that when the intended analysis is inverse-probability weighted generalized estimating equations, and the number of clusters that can be sampled is fixed, optimal allocation of the (cluster-level) sample size across strata defined by auxiliary variables readily available at the design stage has the potential to increase efficiency in the estimation of the parameter(s) of interest. In such a setting, the optimal allocation formulae depend on quantities that are unknown in practice, currently making such designs difficult to implement. In this paper, we consider a two-wave adaptive sampling approach, in which data is collected from a first wave sample, and subsequently used to compute the optimal second wave stratum-specific sample sizes. We consider two strategies for estimating the necessary components using the first wave data: an inverse-probability weighting (IPW) approach and a multiple imputation (MI) approach. In a comprehensive simulation study, we show that the adaptive sampling approach performs well, and that the MI approach yields designs that are very near-optimal, regardless of the covariate type. The IPW approach, on the other hand, has mixed results. Finally, we illustrate the proposed adaptive sampling procedures with data on maternal characteristics and birth outcomes among women enrolled in the Safer Deliveries program in Zanzibar, Tanzania.

Optimal sampling allocation for outcome-dependent designs in cluster-correlated data settings.

In clinical and public health studies, it is often the case that some variables relevant to the analysis are too difficult or costly to measure for all individuals in the population of interest. Rather, a subsample of these individuals must be identified for additional data collection. A sampling scheme that incorporates readily-available information for the entire target population at the design stage can increase the statistical efficiency of the intended analysis. While there is no universally optimal sampling design, under certain principles and restrictions, a well-designed and efficient sampling strategy can be implemented. In two-phase designs, efficiency can be gained by stratifying on the outcome and/or auxiliary information that is known at phase I. Additional gains in efficiency can be obtained by determining the optimal allocation of the sample sizes across the strata, which depends on the quantity that is being estimated. In this paper, the inference is concerned with one or multiple regression parameter(s) where the study units are naturally clustered and, thus, exhibit correlation in outcomes. We propose several allocation strategies within the framework of two-phase designs for the estimation of the regression parameter(s) obtained from weighted generalized estimating equations. The proposed methods extend existing theory to address the objective of the estimating regression parameters in cluster-correlated data settings by minimizing the asymptotic variance of the estimator subject to a fixed sample size. Through a comprehensive simulation study, we show that the proposed allocation schemes have the potential to yield substantial efficiency gains over alternative strategies.

Evaluating sample sizes and design for monitoring and characterizing the spatial variations of potentially toxic elements in the soil

Cost-effective, representative and spatial coverage sampling designs are required to monitor the effects of potentially toxic elements (PTEs) in the soil. This study aims to evaluate the minimum sample sizes and placement of soil sampling designs to monitor and characterize the spatial variation of the PTEs (Cu, Zn, Cd, Cr, Pb, and Ni) in the soils. However, there is no standardized approach for evaluating the optimum soil sample size and monitoring location because of the spatial heterogeneity of PTEs in the soil. As a result, three broad techniques were applied. The first step was to use Global Moran's I and q-statistic values to describe the variability of soil PTEs and select appropriate evaluation methods. Second, using simple random sampling (SRS), ordinary kriging (OK), and Mean of Surface with Non-homogeneity (MSN), we estimated and evaluated soil PTEs in the current soil sampling schemes. Finally, MSN and spatial simulated annealing (SSA) optimization techniques were used to assess the required sample sizes and placements in the existing designs. Method performance was evaluated using a standard error (SE) and a relative standard error of the mean (RSE). Except for Zn and Cd, all PTEs tested showed heterogeneous distributions over the area. The MSN lowered the predicted SE by 79–86 % compared with SRS. The OK approach also outperformed the SRS method regarding mean estimated values of soil PTEs by 42–57 %. After SSA refined the initial design, the predicted SE by MSN of Cr and Zn was lowered by 13 % and 39 %, respectively. The MSN was effective with small sample sizes, reducing sample sizes and surveying costs by 39 % after SSA optimized the existing sample numbers. Thus, integrating various sampling strategies may be efficient for building optimal sample designs to monitor PTEs in the soil.

Beyond body size-new traits for new heights in trait-based modelling of predator-prey dynamics.

Food webs map feeding interactions among species, providing a valuable tool for understanding and predicting community dynamics. Using species’ body sizes is a promising avenue for parameterizing food-web models, but such approaches have not yet been able to fully recover observed community dynamics. Such discrepancies suggest that traits other than body size also play important roles. For example, differences in species’ use of microhabitat or non-consumptive effects of intraguild predators may affect dynamics in ways not captured by body size. In Laubmeier et al. (2018), we developed a dynamic food-web model incorporating microhabitat and non-consumptive predator effects in addition to body size, and used simulations to suggest an optimal sampling design of a mesocosm experiment to test the model. Here, we perform the mesocosm experiment to generate empirical time-series of insect herbivore and predator abundance dynamics. We minimize least squares error between the model and time-series to determine parameter values of four alternative models, which differ in terms of including vs excluding microhabitat use and non-consumptive predator-predator effects. We use both statistical and expert-knowledge criteria to compare the models and find including both microhabitat use and non-consumptive predator-predator effects best explains observed aphid and predator population dynamics, followed by the model including microhabitat alone. This ranking suggests that microhabitat plays a larger role in driving population dynamics than non-consumptive predator-predator effects, although both are clearly important. Our results illustrate the importance of additional traits alongside body size in driving trophic interactions. They also point to the need to consider trophic interactions and population dynamics in a wider community context, where non-trophic impacts can dramatically modify the interplay between multiple predators and prey. Overall, we demonstrate the potential for utilizing traits beyond body size to improve trait-based models and the value of iterative cycling between theory, data and experiment to hone current insights into how traits affect food-web dynamics.

Spatial sampling design optimization of monitoring network for terrestrial ecosystem in China

The rapid socioeconomic development leads to the deterioration of ecological environment. Ecosystem assessment has been conducted worldwide, e.g. the Millennium Ecosystem Assessment to assess consequences of ecosystem change for human well-being. To enhance ecosystem assessment in China, this study proposes the design of a monitoring network for the terrestrial ecosystem consisting of core stations and localized points. With focus on ecosystem services of NPP, water conservation, soil retention and sandstorm prevention, core stations of the monitoring network for observing all four services are first selected by assessing and improving spatial representativeness in ecoregions of forest, grassland and desert ecosystems. Then a spatial sampling method is applied to choose localized points for observing each specific service. Eventually expert's knowledge is used to make final decisions of added stations and points by utilizing existing networks and considering factors such as topography, spatial coverage. Combining both aforementioned approaches and experts knowledge, 60 core stations and 176 localized points are finally determined for the monitoring network. For the forest ecosystem, 39 core stations are decided with 31 selected from existing networks and eight newly added core stations improve spatial representativeness by 51.58 %, 68.11 % and 75.55 % in Temperate grasslands, Temperate desert and Alpine vegetation in Tibet Plateau respectively. For the grassland and desert ecosystem, 21 core stations are chosen with 18 from existing networks and three newly added core stations improve the representativeness by 21.60 % and 44.88 % in Tibet alpine grassland and Grassland in southern mountain areas respectively. Priorities in the implementation phase should be given to instruments installation for monitoring all four services in core stations from existing networks and setting up new stations in regions where representativeness are significantly improved.

Multiwave validation sampling for error-prone electronic health records.

Electronic health record (EHR) data are increasingly used for biomedical research, but these data have recognized data quality challenges. Data validation is necessary to use EHR data with confidence, but limited resources typically make complete data validation impossible. Using EHR data, we illustrate prospective, multiwave, two-phase validation sampling to estimate the association between maternal weight gain during pregnancy and the risks of her child developing obesity or asthma. The optimal validation sampling design depends on the unknown efficient influence functions of regression coefficients of interest. In the first wave of our multiwave validation design, we estimate the influence function using the unvalidated (phase 1) data to determine our validation sample; then in subsequent waves, we re-estimate the influence function using validated (phase 2) data and update our sampling. For efficiency, estimation combines obesity and asthma sampling frames while calibrating sampling weights using generalized raking. We validated 996 of 10,335 mother-child EHR dyads in six sampling waves. Estimated associations between childhood obesity/asthma and maternal weight gain, as well as other covariates, are compared to naïve estimates that only use unvalidated data. In some cases, estimates markedly differ, underscoring the importance of efficient validation sampling to obtain accurate estimates incorporating validated data.

Assessing small-mammal trapping design using spatially explicit capture recapture (SECR) modeling on long-term monitoring data.

Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.

Meta‐analyses of environmental DNA downstream transport and deposition in relation to hydrogeography in riverine environments

Abstract Environmental DNA (eDNA) analysis is a promising tool to monitor species distribution and abundance in aquatic ecosystems. These estimates can be biased in lotic environments by the effects of eDNA transport and deposition processes. However, our understanding of eDNA downstream transport dynamics is limited because it can fluctuate greatly depending on target species abundance and eDNA quantity in a source site and the design of aquatic sampling. Furthermore, eDNA deposition into bottom substrates also determines the availability of aqueous eDNA, but knowledge about deposition is much more limited. Consequently, there is little consensus about eDNA downstream transport and deposition, as well as their interactions with hydrogeographic conditions. In this study, we compiled previous papers concerning riverine eDNA transport and synthesised knowledge about eDNA transport and deposition. A few studies have recently applied a hydrodynamic modelling approach and calculated the average length of eDNA downstream transport (Sw) and velocity of eDNA deposition (Vdep). Referring to those studies, we manually calculated, or directly extracted, Sw and Vdep from the nine eDNA papers and conducted meta‐analyses to reveal the association of these estimates with river discharges. Furthermore, we simulated the mean length of eDNA downstream transport in a channel with given hydrogeographic conditions. The estimated Sw and Vdep values ranged from 2.6 to 13,187.4 m and 0.003 to 1.900 mm/s, respectively. When log‐transformed, the median Sw and Vdep values were 102.188 (= 154.1) m and 10–1.387 (= 0.041) mm/s. The calculated maximum eDNA transport distances until 95% and 99% of the target eDNA particles are deposited ranged from 334.0 to 1,272.7 m and 513.5 to 1,956.4 m, respectively. Moreover, we found that river discharges were positively associated with Sw, whereas Vdep was lower for natural environments than mesocosms. This trend could be due to other environmental factors, such as bottom substrates. Our meta‐analysis implied that, under ordinary hydrogeographic conditions, most eDNA particles could travel less than 2 km downstream even when considering the resuspension of eDNA from riverbeds. By contrast, our study did not consider multiple environmental factors that potentially affect eDNA transport and would accordingly introduce a moderate to high degree of heterogeneity across studies. These issues will be addressed in future studies. To our knowledge, this study is the first to quantitatively synthesise the length of eDNA downstream transport in riverine environments and infer general properties of eDNA downstream transport distance and deposition velocity. According to our results, much of the eDNA released from individuals might have little effect on the detection of target eDNA in the sampling sites more than 2 km downstream. Although further accumulation of empirical studies is necessary, our findings propose the groundwork for optimal eDNA sampling designs in riverine ecosystems to reduce the false‐positive inference of species presence and false‐negative eDNA detection.