Seasonal and interannual variation in spatio-temporal models for index standardization and phenology studies

Climate change is rapidly affecting the seasonal timing of spatial demographic processes. Consequently, resource managers require information from models that simultaneously measure seasonal, interannual, and spatial variation. We present a spatio-temporal model that includes annual, seasonal, and spatial variation in density and then highlight two important uses: (i) standardizing data that are spatially unbalanced within multiple seasons and (ii) identifying interannual changes in seasonal timing (phenology) of population processes. We demonstrate these uses with two contrasting case studies: three bottom trawl surveys for yellowtail flounder (Limanda ferruginea) in the Northwest Atlantic Ocean from 1985 to 2017 and pelagic tows for copepodite stage 3+ copepod (Calanus glacialis/marshallae) densities in the eastern Bering Sea from 1993 to 2016. The yellowtail analysis illustrates how data from multiple surveys can be used to infer density hot spots in an area that is not sampled one or more surveys. The copepod analysis assimilates seasonally unbalanced samples to estimate an annual index of the seasonal timing of copepod abundance and identifies a positive correlation between this index and cold-pool extent. We conclude by discussing additional potential uses of seasonal spatio-temporal models and emphasize their ability to identify climate-driven shifts in the seasonal timing of fish movement and ecosystem productivity.

Recommendations for Kübler Index standardization

Following a round-table discussion at the Mid-European Clay Conference in Dresden 2014, new recommendations for illite ‘crystallinity’ Kübler index standardization have been agreed upon. The use of Crystallinity Index standards in the form of rock-fragment samples will be continued, along with the same numerical scale of measurement presented by Warr & Rice (1994). However, in order to be compatible with the original working definition of Kübler's (1967) anchizone, the upper and lower boundary limits of the Crystallinity Index Standard (CIS) scale are adjusted appropriately from 0.25°2θ and 0.42°2θ to 0.32°2θ and 0.52°2θ. This adjustment is based on an inter-laboratory correlation between the laboratories of Basel, Neuchâtel and the CIS scale. The details of this correction are presented in this first note, as discussed at the round-table meeting and will be further substantiated by a correlation program between CIS and former Kübler–Frey–Kisch standards.

Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes

Indices of abundance are the bedrock for stock assessments or empirical management procedures used to manage fishery catches for fish populations worldwide, and are generally obtained by processing catch-rate data. Recent research suggests that geostatistical models can explain a substantial portion of variability in catch rates via the location of samples (i.e. whether located in high- or low-density habitats), and thus use available catch-rate data more efficiently than conventional “design-based” or stratified estimators. However, the generality of this conclusion is currently unknown because geostatistical models are computationally challenging to simulation-test and have not previously been evaluated using multiple species. We develop a new maximum likelihood estimator for geostatistical index standardization, which uses recent improvements in estimation for Gaussian random fields. We apply the model to data for 28 groundfish species off the U.S. West Coast and compare results to a previous “stratified” index standardization model, which accounts for spatial variation using post-stratification of available data. This demonstrates that the stratified model generates a relative index with 60% larger estimation intervals than the geostatistical model. We also apply both models to simulated data and demonstrate (i) that the geostatistical model has well-calibrated confidence intervals (they include the true value at approximately the nominal rate), (ii) that neither model on average under- or overestimates changes in abundance, and (iii) that the geostatistical model has on average 20% lower estimation errors than a stratified model. We therefore conclude that the geostatistical model uses survey data more efficiently than the stratified model, and therefore provides a more cost-efficient treatment for historical and ongoing fish sampling data.

Standardizing compositional data for stock assessment

Stock assessment models frequently integrate abundance index and compositional (e.g. age, length, sex) data. Abundance indices are generally estimated using index standardization models, which provide estimates of index standard errors while accounting for: (i) differences in sampling intensity spatially or over time; (ii) non-independence of available data; and (iii) the effect of covariates. However, compositional data are not generally processed using a standardization model, so effective sample size is not routinely estimated and these three issues are unresolved. I therefore propose a computationally simple “normal approximation” method for standardizing compositional data and compare this with design-based and Dirichlet-multinomial (D-M) methods for analysing compositional data. Using simulated data from a population with multiple spatial strata, heterogeneity within strata, differences in sampling intensity, and additional overdispersion, I show that the normal-approximation method provided unbiased estimates of abundance-at-age and estimates of effective sample size that are consistent with the imprecision of these estimates. A conventional design-based method also produced unbiased age compositions estimates but no estimate of effective sample size. The D-M failed to account for known differences in sampling intensity (the proportion of catch for each fishing trip that is sampled for age) and hence provides biased estimates when sampling intensity is correlated with variation in abundance-at-age data. I end by discussing uses for “composition-standardization models” and propose that future research develop methods to impute compositional data in strata with missing data.

A comparison of two tree-ring-index standardization techniques

A comparison is made between two tree-ring index chronologies that are based on the same set of site ring measurements but use two different standardization techniques. Both polynomial functions and 50-year Gaussian filtering procedures have been employed to represent the biological growth curve, thereby essentially detrending the resulting ring-index chronologies. It is established that although both approaches generate highly correlated time series at lag 0, significant differences exist in the autocorrelation functions, mean sensitivity values, and spectral amplitudes at the low frequency end of the spectrum. The exclusion of these periodicities is of concern, as they are normally associated with longer term climatic variations as well as site influences. Consequently, the nature of the descriptive statistical indicators generally considered, especially the spectral profile, should be established in conjunction with possible standardization options. Also, it would be prudent to appraise existing chronologies, on the same basis, prior to undertaking any dendrochronological applications.

Standardization of the radioreceptor assay against anti-thyrotrophin receptor antibodies in Graves' disease

We studied the possibility of standardizing the radioreceptor assay for the detection of anti-thyrotrophin receptor antibodies, also called thyrotrophinbinding inhibitor immunoglobulins (TBII), to circumvent the problem of inter-assay variability in TBII index calculations. We developed a procedure for conversion of the thyrotrophin-binding inhibition caused by standard amounts (10 mg IgG/ml) of 1.6 m ammonium sulphate precipitates into thyrotrophin-equivalents, i.e. the amount of thyrotrophin required to cause the same thyrotrophin-binding inhibition. We were able to determine a normal range for thyrotrophin-binding inhibition which exhibited little inter-assay variability and was applicable to individual radioreceptor assays. The normal range, expressed in thyrotrophin-equivalents or the logarithm of μU thyrotrophin, was 2.616–3.645, which corresponds to 413–4420 μU. The thyrotrophin-binding inhibition by 1.6 m ammonium sulphate precipitates from sera of 17 untreated patients with Graves' disease was also expressed in thyrotrophin-equivalents. For 15 patients (88%) the thyrotrophin-equivalent values were above the normal range, whereas only 10 patients (59%) had a positive TBII index (using the same sample). This indicated that 5 patients had anti-thyrotrophin receptor antibodies in spite of their negative TBII index. Standardization based on the IgG content of the test samples therefore seems to be imperative. Pig serum 1.6 m ammonium sulphate precipitates, which were tested in a porcine assay system, yielded thyrotrophin-equivalent values that corresponded closely to the normal range for the human assay system, suggesting that the normal range of thyrotrophin-binding inhibition is in the same order of magnitude in porcine and human assay systems.