Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms

Evaluating sampling techniques for quantifying Asiatic garden beetle (Maladera formosae) (Coleoptera: Scarabaeidae) infestations in commercial mint

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.

Impact assessment of human activities on resources of juvenile horseshoe crabs in Hainan coastal areas, China

The booming coastal zone economy poses increasing anthropogenic threats to marine life and habitats. Using the endangered living fossil horseshoe crab (HSC) as an example, we quantified the intensity of various anthropogenic pressures along the coast of Hainan Island, China, and for the first time assessed their impact on the distribution of juvenile HSCs through a field survey, remote sensing, spatial geographic modeling, and machine learning methods. The results indicate that the Danzhou Bay needs to be protected as a priority based on species and anthropogenic pressure information. Aquaculture and port activities dramatically impact the density of HSCs and therefore be managed priority. Finally, a threshold effect between total, coastal residential, and beach pressure and the density of juvenile HSCs were detected, which indicates the need for a balance between development and conservation as well as the designation of suitable sites for the construction of marine protected areas.

Relationship between water iodine and children's goiters

Excessive iodine can lead to goiters. However, the relationship between the water iodine concentration (WIC) and goiter rate (GR) is unclear. This study aims to explore the factors that influence children's GR in areas with high WIC and analyse the threshold value of the GR increase associated with the WIC. According to the monitoring of the areas with high WIC in China in 2018-2020, a total of 54 050 children in eight high water iodine provinces were chosen. Drinking water, urine and edible salt samples of children were collected. The thyroid volume (Tvol) was measured. A generalised additive model (GAM) was used to analyse the relationship between the WIC and GR in children. Among the 54 050 children in areas with high WIC, the overall GR was 3·34 %, the median of water iodine concentration was 127·0 µg/l, the median of urinary iodine concentration was 318 µg/l and the non-iodised salt coverage rate (NISCR) was 63·51 %. According to the GAM analysis results, water iodine and urinary iodine are factors that influence the Tvol and GR, while the NISCR affects only the GR. When the WIC was more than 420 µg/l or the urinary iodine concentration was more than 800 µg/l, the GR increased rapidly. When the NISCR reached more than 85 %, the GR was the lowest. Thus, in areas with high WIC, WIC more than 420 µg/l may increase the risk of goiter, and the NISCR should be increased to over 85 % to avoid goiters in children.

Quantifying the statistical power of monitoring programs for marine protected areas

Marine Protected Areas (MPAs) are increasingly established globally as a spatial management tool to aid in conservation and fisheries management objectives. Assessing whether MPAs are having the desired effects on populations requires effective monitoring programs. A cornerstone of an effective monitoring program is an assessment of the statistical power of sampling designs to detect changes when they occur. We present a novel approach to power assessment that combines spatial point process models, integral projection models (IPMs) and sampling simulations to assess the power of different sample designs across a network of MPAs. We focus on the use of remotely operated vehicle (ROV) video cameras as the sampling method, though the results could be extended to other sampling methods. We use empirical data from baseline surveys of an example indicator fish species across three MPAs in California, USA as a case study. Spatial models simulated time series of spatial distributions across sites that accounted for the effects of environmental covariates, while IPMs simulated expected trends over time in abundances and sizes of fish. We tested the power of different levels of sampling effort (i.e., the number of 500-m ROV transects) and temporal replication (every 1-3 yr) to detect expected post-MPA changes in fish abundance and biomass. We found that changes in biomass are detectable earlier than changes in abundance. We also found that detectability of MPA effects was higher in sites with higher initial densities. Increasing the sampling effort had a greater effect than increasing sampling frequency on the time taken to achieve high power. High power was best achieved by combining data from multiple sites. Our approach provides a powerful tool to explore the interaction between sampling effort, spatial distributions, population dynamics, and metrics for detecting change in previously fished populations.

A Stepwise Approach to Assess the Occupancy State of Larval Lampreys in Streams

Pacific Lamprey Entosphenus tridentatus is an ecologically and culturally important anadromous species of conservation concern for which fisheries managers use information on occupancy state in streams to assess species status and inform stream management decisions. Here we developed a stepwise approach that incorporates the potential for nondetection and a preselected expected maximum probability of stream occupancy if field crews do not document larval Pacific Lamprey during sampling. Our approach includes seven steps: define the occupancy question; select the maximum acceptable probability of occupancy, if the species is not documented during sampling; define an assumed detection probability for the target organism; calculate required sampling effort; select sampling units; conduct sampling; and interpret sampling results into probabilistic occupancy conclusions. We examined detection probability of our approach for larval lamprey using data from multiple occupied streams in the Pacific Northwest. We illustrated our approach by evaluating Balm Grove Dam as a barrier to Pacific Lamprey migration in Gales Creek, Oregon. Bayesian estimates of detection probability in occupied streams ranged from 0.15 to 0.94, with an overall median of 0.70 (95% credible interval: 0.60–0.79). Assuming detection probability is at least 0.15 (i.e., lowest estimate), 19 reaches are required for the expected maximum probability of occupancy to be not more than 0.05, if the species is not documented through our sampling approach. Although detected downstream, we detected no larvae upstream of Balm Grove Dam; thus, we conclude that the maximum probability of occupancy upstream of Balm Grove Dam was not more than 0.05 at an assumed detection probability of 0.4, suggesting the dam as a barrier to adult migration. We provide an occupancy assessment tool with standardized sampling requirements that incorporates the potential for nondetection and the flexibility to select an expected maximum probability of occupancy if researchers document no larvae, to aid management and restoration in a single stream.

Spatially balanced sampling designs for environmental surveys

Some environmental studies use non-probabilistic sampling designs to draw samples from spatially distributed populations. Unfortunately, these samples can be difficult to analyse statistically and can give biased estimates of population characteristics. Spatially balanced sampling designs are probabilistic designs that spread the sampling effort evenly over the resource. These designs are particularly useful for environmental sampling because they produce good-sample coverage over the resource, they have precise design-based estimators and they can potentially reduce the sampling cost. The most popular spatially balanced design is Generalized Random Tessellation Stratified (GRTS), which has many desirable features including a spatially balanced sample, design-based estimators and the ability to select spatially balanced oversamples. This article considers the popularity of spatially balanced sampling, reviews several spatially balanced sampling designs and shows how these designs can be implemented in the statistical programming language R. We hope to increase the visibility of spatially balanced sampling and encourage environmental scientists to use these designs.

A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits

Selecting an appropriate and efficient sampling strategy in biological surveys is a major concern in ecological research, particularly when the population abundance and individual traits of the sampled population are highly structured over space. Multi-stage sampling designs typically present sampling sites as primary units. However, to collect trait data, such as age or maturity, only a sub-sample of individuals collected in the sampling site is retained. Therefore, not only the sampling design, but also the sub-sampling strategy can have a major impact on important population estimates, commonly used as reference points for management and conservation. We developed a simulation framework to evaluate sub-sampling strategies from multi-stage biological surveys. Specifically, we compare quantitatively precision and bias of the population estimates obtained using two common but contrasting sub-sampling strategies: the random and the stratified designs. The sub-sampling strategy evaluation was applied to age data collection of a virtual fish population that has the same statistical and biological characteristics of the Eastern Bering Sea population of Pacific cod. The simulation scheme allowed us to incorporate contributions of several sources of error and to analyze the sensitivity of the different strategies in the population estimates. We found that, on average across all scenarios tested, the main differences between sub-sampling designs arise from the inability of the stratified design to reproduce spatial patterns of the individual traits. However, differences between the sub-sampling strategies in other population estimates may be small, particularly when large sub-sample sizes are used. On isolated scenarios (representative of specific environmental or demographic conditions), the random sub-sampling provided better precision in all population estimates analyzed. The sensitivity analysis revealed the important contribution of spatial autocorrelation in the error of population trait estimates, regardless of the sub-sampling design. This framework will be a useful tool for monitoring and assessment of natural populations with spatially structured traits in multi-stage sampling designs.