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Changes in sea urchin behavior following detection of chemical cues from predatory fishes may influence key ecological dynamics but have rarely been experimentally quantified. Here, we measured feeding rates on a habitat-forming macroalgae by two size classes of the New Zealand sea urchin ( Evechinus chloroticus ) exposed to either ambient seawater or seawater carrying excretions from a predatory fish (blue cod; Parapercis colias ). We created a Bayesian model that combined uncertainty in kelp growth rates and probability of urchin feeding to generate robust estimates of fish-exposure effects on multiple feeding metrics. We then compared our results to re-analyzed data characterizing behavioral responses of E. chloroticus to lobster cues ( Jasus edwardsii ). Larger urchins (6–8 cm test diameter) consumed ~ 40% less kelp in both predator treatments, exhibiting indistinguishable responses to blue cod and lobster despite being less susceptible to fish predation. Responses of smaller urchins (3–5 cm test diameter) to both predators were equivocal, though were more consistent with reduced feeding in the presence of lobster. Here we provide novel evidence that fish cues can suppress urchin feeding rates, even in the absence of urchin alarm cues, and discuss our findings in the context of the specificity of predator cue detection-reception pathways and possible mechanisms for risk-induced reductions in urchin feeding.

Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors. There is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors.

X-ray diffractometry (XRD) is commonly used to determine both aragonite:calcite ratio and Mg content (in calcite) in biogenic skeletal carbonate. Bimineral taxa, such as many abalone, combine aragonite and calcite, or sometimes two distinct calcites, in a single skeleton. At least some abalone shells are, however, formed of three discrete carbonate minerals: aragonite, high-Mg calcite, and low-Mg calcite. Here we develop and apply a new system based on a Bayesian calibration model, an extension of the Reference Intensity Ratio method that accommodates heteroskedastic noise, for determining relative proportions in trimineralic biogenic carbonate using XRD patterns. We describe the system, validate and assess the system using biomineral standards, and quantify sources of error. We then use the system to describe mineralogical variation within the sometimes-trimineralic New Zealand black-footed pāua Haliotis iris . All specimens contained aragonite, and most contained low-Mg calcite, with older shell showing decreasing amounts of calcite (presumably due to wear of this external layer). Almost all specimens from Kaikoura contained at least some high-Mg calcite, thus being tri-mineralic. This mixture of three biogenic carbonates is most unusual, so we have used our new method of analysing XRD patterns to estimate the proportions of three co-occurring skeletal carbonate minerals in this marine invertebrate. We also provide the first detailed analysis of uncertainty and precision in XRD analysis of skeletal carbonate mineralogy.

Overdispersion often occurs when fitting a binomial, multinomial or Poisson model to count data. In the Bayesian setting, failure to allow for overdispersion leads to the posteriors for the parameters being too narrow. A simple and natural approach is to incorporate parameter heterogeneity in the model, e.g. by adding a random effect to the linear predictor. However, overdispersion can also be caused by a lack of independence, which may not be straightforward to model explicitly. In addition, there may still be some residual overdispersion after allowing for heterogeneity or lack of independence. In many settings where overdispersion is present, it is reasonable to assume that the variance of the response variable is proportional to that assumed by the model. When this is the case, we propose estimating the amount of overdispersion, and discuss the link between this estimate and the use of a posterior predictive p-value to check lack-of-fit. We also provide a residual plot that can be used to check the assumption of proportionality. We show how to use the estimate of overdispersion to make a simple adjustment to the posterior distribution for each parameter, analogous to the use of quasi-likelihood in the frequentist setting. We use two examples, regression modelling of count data and estimation of survival from a mark-recapture study, to illustrate the calculation of the estimate of overdispersion, and the resulting adjustment to the posteriors. We perform simulation studies based on the examples to assess the frequentist coverage properties of the adjusted posteriors. In both simulation studies, the adjusted posteriors lead to credible intervals that have approximately the correct coverage for a range of overdispersion scenarios. Our approach provides a new, simple and robust tool for Bayesian modelling of overdispersed data, when it is reasonable to assume that the variance of the response variable is proportional to that assumed by the model.

Regularized time series of ocean carbon data are necessary for assessing seasonal dynamics, annual budgets, and interannual and climatic variability. There are, however, no standardized methods for filling data gaps and limited evaluation of the impacts on uncertainty in the reconstructed time series when using various imputation methods. Here we present an empirical multivariate linear regression (MLR) model to estimate the concentration of dissolved inorganic carbon (DIC) in the surface ocean, that can utilize remotely sensed and modeled data to fill data gaps. This MLR was evaluated against seven other imputation models using data from seven long-term monitoring sites in a comparative assessment of gap-filling performance and resulting impacts on variability in the reconstructed time series. Methods evaluated included three empirical models – MLR, mean imputation, and multiple imputation by chained equation (MICE) – and five statistical models – linear, spline, and Stineman interpolation; exponential weighted moving average; and Kalman filtering with a state space model. Cross validation was used to determine model error and bias, while a bootstrapping approach was employed to determine sensitivity to varying data gap lengths. A series of synthetic gap filters, including 3-month seasonal gaps (spring, summer, autumn winter), 6-month gaps (centered on summer and winter), and bimonthly (every 2 months) and seasonal (four samples per year) sampling regimes, were applied to each time series to evaluate the impacts of timing and duration of data gaps on seasonal structure, annual means, interannual variability, and long-term trends. All models were fit to time series of monthly mean DIC, with MLR and MICE models also applied to both measured and modeled temperature and salinity with remotely sensed chlorophyll. Our MLR estimated DIC with a mean error of 8.8 µmol kg−1 among five oceanic sites and 20.0 µmol kg−1 for two coastal sites. The MLR performance indicated reanalysis data, such as GLORYS, can be utilized in the absence of field measurements without increasing error in DIC estimates. Of the methods evaluated in this study, empirical models did better than statistical models in retaining observed seasonal structure but led to greater bias in annual means, interannual variability, and trends compared to statistical models. Our MLR proved to be a robust option for imputing data gaps over varied durations and may be trained with either in situ or modeled data depending on application. This study indicates that the number and distribution of data gaps are important factors in selecting a model that optimizes uncertainty while minimizing bias and subsequently enables robust strategies for observational sampling.

The pH behavior in the μm to cm thick diffusion boundary layer (DBL) surrounding many aquatic species is dependent on light-controlled metabolic activities. This DBL microenvironment exhibits different pH behavior to bulk seawater, which can reduce the exposure of calcifying species to ocean acidification conditions. A low-cost time-domain dual-lifetime referencing (t-DLR) interrogation system and an optical fiber fluorescent pH sensor were developed for pH measurements in the DBL interface. The pH sensor utilized dual-layer sol-gel coatings of pH-sensitive iminocoumarin and pH-insensitive Ru(dpp)3-PAN. The sensor has a dynamic range of 7.41 (±0.20) to 9.42 ± 0.23 pH units (95% CI, T = 20 °C, S = 35), a response time (t90) of 29 to 100 s, and minimal salinity dependency. The pH sensor has a precision of approximately 0.02 pHT units, which meets the Global Ocean Acidification Observing Network (GOA-ON) “weather” measurement quality guideline. The suitability of the t-DLR optical fiber pH sensor was demonstrated through real-time measurements in the DBL of green seaweed Ulva sp. This research highlights the practicability of optical fiber pH sensors by demonstrating real-time pH measurements of metabolic-induced pH changes.

A suite of factors may have contributed to declines in the tītī (sooty shearwater; Ardenna grisea ) population in the New Zealand region since at least the 1960s. Recent estimation of the magnitude of most sources of non-natural mortality has presented the opportunity to quantitatively assess the relative importance of these factors. We fit a range of population dynamics models to a time-series of relative abundance data from 1976 until 2005, with the various sources of mortality being modelled at the appropriate part of the life-cycle. We present estimates of effects obtained from the best-fitting model and using model averaging. The best-fitting models explained much of the variation in the abundance index when survival and fecundity were linked to the Southern Oscillation Index, with strong decreases in adult survival, juvenile survival and fecundity being related to El Niño-Southern Oscillation (ENSO) events. Predation by introduced animals, harvesting by humans, and bycatch in fisheries also appear to have contributed to the population decline. It is envisioned that the best-fitting models will form the basis for quantitative assessments of competing management strategies. Our analysis suggests that sustainability of the New Zealand tītī population will be most influenced by climate, in particular by how climate change will affect the frequency and intensity of ENSO events in the future. Removal of the effects of both depredation by introduced predators and harvesting by humans is likely to have fewer benefits for the population than alleviating climate effects.

Fishes can undergo dramatic social and morphological changes throughout development that drive ontogenetic shifts in diet and habitat association. Measurements of trophic ontogeny at the individual level often complement population‐level assessments, detailing foraging strategies that have underpinned long‐term growth and survival. Using stable isotope measurements from muscle and eye lenses, we modeled size‐based patterns in basal resource use and trophic position across multiple levels of organization for three New Zealand reef fishes ( Notolabrus fucicola , Odax pullus, and Parapercis colias ). From lens‐derived data series, we were able to estimate trends and variability in lifetime trophic ontogeny of each species, as well as size‐structured changes in breadth and interspecific overlap of resource use. For adults, broadly similar trophic shifts were reflected in isotopic composition of both muscle tissue and lens layers, with subtle differences between tissues for some combinations of species and ecological metric. Critically, only samples from eye lenses yielded estimates of resource use that supported early growth. Specifically, our measurements suggested heightened reliance on macroalgal food webs during post‐settlement dispersal of two carnivores ( N. fucicola , P. colias ), as well as variable peaks in omnivory at small sizes for O. pullus , a primary herbivore. Trophic shifts modeled from eye lenses of carnivorous species were generally similar throughout early development, but highly inconsistent among juvenile O. pullus . Analyses of eye lenses also yielded evidence of trophic breadth contraction around size‐at‐maturity of all three species, coincident with apparent differentiation of adult resource use between sampled carnivores. Finally, in addition to high‐resolution assessments of trophic ontogeny, we provide analytic considerations that may strengthen use of eye lenses for investigation of fish life history, particularly through examination of calibration assumptions in a novel system.

In many scientific studies, the underlying data-generating process is unknown and multiple statistical models are considered to describe it. For example, in a factorial experiment we might consider models involving just main effects, as well as those that include interactions. Model-averaging is a commonly-used statistical technique to allow for model uncertainty in parameter estimation. In the frequentist setting, the model-averaged estimate of a parameter is a weighted mean of the estimates from the individual models, with the weights typically being based on an information criterion, cross-validation, or bootstrapping. One approach to building a model-averaged confidence interval is to use a Wald interval, based on the model-averaged estimate and its standard error. This has been the default method in many application areas, particularly those in the life sciences. The MA-Wald interval, however, assumes that the studentized model-averaged estimate has a normal distribution, which can be far from true in practice due to the random, data-driven model weights. Recently, the model-averaged tail area Wald interval (MATA-Wald) has been proposed as an alternative to the MA-Wald interval, which only assumes that the studentized estimate from each model has a N(0, 1) or t-distribution, when that model is true. This alternative to the MA-Wald interval has been shown to have better coverage in simulation studies. However, when we have a response variable that is skewed, even these relaxed assumptions may not be valid, and use of these intervals might therefore result in poor coverage. We propose a new interval (MATA-SBoot) which uses a parametric bootstrap approach to estimate the distribution of the studentized estimate for each model, when that model is true. This method only requires that the studentized estimate from each model is approximately pivotal, an assumption that will often be true in practice, even for skewed data. We illustrate use of this new interval in the analysis of a three-factor marine global change experiment in which the response variable is assumed to have a lognormal distribution. We also perform a simulation study, based on the example, to compare the lower and upper error rates of this interval with those for existing methods. The results suggest that the MATA-SBoot interval can provide better error rates than existing intervals when we have skewed data, particularly for the upper error rate when the sample size is small.

Species distribution models (SDMs) can be used to predict distributions in novel times or space (termed transferability) and fill knowledge gaps for areas that are data poor. In conservation, this can be used to determine the extent of spatial protection required. To understand how well a model transfers spatially, it needs to be independently tested, using data from novel habitats. Here, we test the transferability of SDMs for Hector's dolphin (Cephalorhynchus hectori), a culturally important (taonga) and endangered, coastal delphinid, endemic to Aotearoa New Zealand. We collected summer distribution data from three populations from 2021 to 2023. Using Generalised Additive Models, we built presence/absence SDMs for each population and validated the predictive ability of the top models (with TSS and AUC). Then, we tested the transferability of each top model by predicting the distribution of the remaining two populations. SDMs for two populations showed useful performance within their respective areas (Banks Peninsula and Otago), but when used to predict the two areas outside the models' source data, performance declined markedly. SDMs from the third area (Timaru) performed poorly, both for prediction within the source area and when transferred spatially. When data for model building were combined from two areas, results were mixed. Model interpolation was better when presence/absence data from Otago, an area of low density, were combined with data from areas of higher density, but was otherwise poor. The overall poor transferability of SDMs suggests that habitat preferences of Hector's dolphins vary between areas. For these dolphins, population‐specific distribution data should be used for conservation planning. More generally, we demonstrate that a one model fits all approach is not always suitable. When SDMs are used to predict distribution in data‐poor areas an assessment of performance in the new habitat is required, and results should be interpreted with caution. Species distribution models (SDMs) are commonly used to predict habitat and distribution for species in data poor areas, under the assumption that habitat use is the same across space. We tested the transferability of SDMs for Hector's dolphin, revealing differences in habitat use between populations. These results indicate that to understand habitat use of Hector's dolphin, local data is required for the model building process.