Effects of Measuring Devices and Sampling Strategies on the Interpretation of Monitoring Data for Long-Term Trend Analysis

A thorough and reliable assessment of changes in sea surface water temperatures (SSWTs) is essential for understanding the effects of global warming on long-term trends in marine ecosystems and their communities. The first long-term temperature measurements were established almost a century ago, especially in coastal areas, and some of them are still in operation. However, while in earlier times these measurements were done by hand every day, current environmental long-term observation stations (ELTOS) are often fully automated and integrated in cabled underwater observatories (UWOs). With this new technology, year-round measurements became feasible even in remote or difficult to access areas, such as coastal areas of the Arctic Ocean in winter, where measurements were almost impossible just a decade ago. In this context, there is a question over what extent the sampling frequency and accuracy influence results in long-term monitoring approaches. In this paper, we address this with a combination of lab experiments on sensor accuracy and precision and a simulated sampling program with different sampling frequencies based on a continuous water temperature dataset from Svalbard, Arctic, from 2012 to 2017. Our laboratory experiments showed that temperature measurements with 12 different temperature sensor types at different price ranges all provided measurements accurate enough to resolve temperature changes over years on a level discussed in the literature when addressing climate change effects in coastal waters. However, the experiments also revealed that some sensors are more suitable for measuring absolute temperature changes over time, while others are more suitable for determining relative temperature changes. Our simulated sampling program in Svalbard coastal waters over 5 years revealed that the selection of a proper sampling frequency is most relevant for discriminating significant long-term temperature changes from random daily, seasonal, or interannual fluctuations. While hourly and daily sampling could deliver reliable, stable, and comparable results concerning temperature increases over time, weekly sampling was less able to reliably detect overall significant trends. With even lower sampling frequencies (monthly sampling), no significant temperature trend over time could be detected. Although the results were obtained for a specific site, they are transferable to other aquatic research questions and non-polar regions.

Using Social Networks to Sample Migrants and Study the Complexity of Contemporary Immigration: An Evaluation Study

We test the effectiveness of a link-tracing sampling approach, Network Sampling with Memory (NSM) to recruit samples of rare immigrant populations with an application among Chinese immigrants in the Raleigh-Durham Area of North Carolina. NSM uses the population network revealed by data from the survey to improve the efficiency of link-tracing sampling, and has been shown to substantially reduce design effects in simulated sampling. Our goals are: (1) to show that it is possible to recruit a probability sample of a locally rare immigrant group using NSM and achieve high response rates; (2) to demonstrate feasibility of collection and benefits of new forms of network data that transcend kinship networks in existing surveys and can address unresolved questions about the role of social networks in migration decisions, the maintenance of transnationalism, and the process of social incorporation; (3) to test the accuracy of the NSM approach to recruit immigrant samples by comparison with the American Community Survey (ACS). Our results indicate feasibility, high performance, cost-effectiveness and accuracy of the NSM approach to sample immigrants for studies of local immigrant communities. This approach can also be extended to recruit multi-site samples of immigrants at origin and destination.

Consistent forest biomass stock and change estimation across stand, property and landscape levels

Stimulating climate change mitigation actions in the forest sector requires methods to quantify the biomass stocks and changes at different geographical levels. Often, differences in data and estimation methods that are available at each level cause inconsistencies in forest parameters estimated at different levels. We propose a method to align model-based and model-assisted estimators to ensure cross-sectional and time series consistency of stock and change estimates of above-ground biomass (AGB). The method adjusts estimates within their confidence intervals using heuristic optimization to minimize the estimation errors. The method is evaluated under simulated sampling in a case study representing a forested area of approximately 50 km2 in southeastern Norway. The area is divided into 93 forest properties encompassing 3324 forest stands. The artificial forest population is generated for two time points using wall-to-wall airborne laser scanning (ALS) data acquired in 2001 and 2016, as well as field surveys conducted within a similar timeframe. The adjusted AGB stock and change estimators at different levels of aggregation are compared to the original unadjusted estimators in terms of bias and RMSE. The results show that the adjusted estimators do not introduce bias, and the increase in RMSE is small for the forest stand-level estimators, and even decreasing for the forest property-level estimators. The method can easily be adapted to complex systems of estimators that need to be consistent.

Retrieval of among-stand variances from one observation per stand

Forest inventories provide predictions of stand means on a routine basis from models with auxiliary variables from remote sensing as predictors and response variables from field data. Many forest inventory sampling designs do not afford a direct estimation of the among-stand variance. As consequence, the confidence interval for a model-based prediction of a stand mean is typically too narrow. We propose a new method to compute (from empirical regression residuals) an among-stand variance under sample designs that stratify sample selections by an auxiliary variable, but otherwise do not allow a direct estimation of this variance. We test the method in simulated sampling from a complex artificial population with an age class structure. Two sampling designs are used (one-per-stratum, and quasi systematic), neither recognize stands. Among-stand estimates of variance obtained with the proposed method underestimated the actual variance by 30-50%, yet 95% confidence intervals for a stand mean achieved a coverage that was either slightly better or at par with the coverage achieved with empirical linear best unbiased estimates obtained under less efficient two-stage designs.

The representativeness of a semi-random sampling method for animal welfare assessments on mink farms

In this study we present a semi-random sampling method developed for the sampling of mink (Neovison vison) for on-farm welfare assessments according to the WelFur-Mink system. The only information required for implementation of this method is the number of cages in use in each shed on the farm. The representativeness of samples selected with this method was evaluated in relation to the physical characteristics of the farm and the mink characteristics by simulated sampling on a farm with a complicated structure in the growth period. The selection of 10,000 samples was simulated. The trueness was, in general, high, ie the method has no systematic skewness. The precision was low for certain factors due to the high variation within sheds. The sampling in sections of six adjacent cages means that it is often not possible to select a sample which is an exact representation of the mink and their housing environment. If accepting a deviation of ± one cage section, the estimated probability of selecting a representative sample was high for most of the individual factors. However, the estimated probability of selecting a sample that is representative according to all factors was rather low. This deviation from exact representativeness ought to be evaluated in the light of the increased feasibility and repeatability offered by the method. Also, we expect that the representativeness of samples selected with this method will be higher on other less-complicated farms. We suggest that this simple method balances feasibility and representative sampling in a way that makes it useful in the WelFur-Mink system.

The challenge of estimating a residual spatial autocorrelation from forest inventory data

Estimates of stand averages are needed by forest management for planning purposes. In forest enterprise inventories supported by remotely sensed auxiliary data, these estimates are typically derived exclusively from a model that does not consider stand effects in the study variable. Variance estimators for these means may seriously underestimate uncertainty, and confidence intervals may be too narrow when a model used for computing a stand mean omits a nontrivial stand effect in one or more of the model parameters, a nontrivial spatial distance dependent autocorrelation in the model residuals, or both. In simulated sampling from 36 populations with stands of different sizes and differing with respect to (i) the correlation between a study variable (Y) and two auxiliary variables (X), (ii) the magnitude of stand effects in the intercept of a linear population model linking X to Y, and (iii) a first-order autoregression in Y and X, we learned that none of the tested designs provided reliable estimates of the within-stand autocorrelation among model residuals. More-reliable estimates were possible from stand-wide predictions of Y. The anticipated bias in an estimated autoregression parameter had a modest influence on estimates of variance and coverage of nominal 95% confidence intervals for a synthetic stand mean.

Development and assessment of a new method for combining catch per unit effort data from different fish sampling gears: multigear mean standardization (MGMS)

Fish community assessments are often based on sampling with multiple gear types. However, multivariate methods used to assess fish community structure and composition are sensitive to differences in the relative scale of indices or measures of abundance produced by different sampling methods. This makes combining data from different sampling gears and methods a serious challenge. We developed a method of combining catch per unit effort data that standardizes catch per unit effort data across gear types, which we call multigear mean standardization (MGMS). We evaluated how well MGMS and other types of standardization reflect underlying community structure through a computer simulation that generated model riverine-fish communities and simulated sampling data for two gears. In these simulations, combining sampling observations from two gears with MGMS produced community structure estimates that were highly correlated with true community structure under a variety of conditions that are common in large rivers. Our simulation results indicate that the use of MGMS to combine data from different sampling gears is an effective data manipulation method for the analysis of fish community structure.