Abstract

Consistent patterns of Harmful Algal Bloom (HAB) events are not evident across the scientific literature, suggesting that local or regional variability is likely to be important in modulating any overall trend. This study summarizes Scotland-wide temporal and spatial patterns in a robust 15-year high temporal frequency time series (2006–2020) of the incidence of HABs and shellfish biotoxins in blue Mussels (Mytilus edulis), collected as part of the Food Standards Scotland (FSS) regulatory monitoring program. The relationship between the countrywide annual incidence of HAB events and biotoxins with environmental variables was also explored. Temporal patterns exhibited interannual variability, with no year-on-year increase, nor any correlation between annual occurrences. Within years, there was a summer increase in bloom frequency, peaking in July for Dinophysis spp. and Pseudo-nitzschia spp., and a plateau from May to July for Alexandrium spp. Temporal-spatial patterns were analyzed with multivariate statistics on data from monitoring sites aggregated monthly into 50-km grid cells, using Principal Component Analysis (PCA) and cluster K-means analysis. PCA analyses showed correlation between areas with similar temporal dynamics, identifying seasonality as one of the main elements of HAB variability with temporal-spatial patterns being explained by the first and second principal components. Similar patterns among regions in timing and magnitude of blooms were evaluated using K-means clusters. The analysis confirmed that the highest risk from HABs generally occurred during summer, but demonstrated that areas that respond in a similar manner (high or low risk) are not always geographically close. For example, the occurrence of the most prevalent HAB genus, Dinophysis spp., is similar countrywide, but there is a regional trend in risk level with “very-high” and “high” clusters located primarily on the southwest coast, the islands of the central and northern west coast and the Shetland Islands. “Early” and “late” blooms were also associated with certain areas and level of risk. Overall, high risk areas mainly face in a southwest direction, whilst low risk locations face a south or southeast direction. We found relatively few countrywide relationships between environmental variables and HABs, confirming the need for regional analysis to support HAB early warning.

Highlights

  • Harmful algal blooms (HABs), associated with human and shellfish toxicity, are temporally and spatially variable

  • This is consistent with recent results from Hallegraeff et al (2021) who used a metaanalysis of Harmful Algae Event Database and Ocean Biodiversity Information system data to demonstrate that there is no empirical support for a global increase in HAB events

  • Temporal Trends While all the HAB genera of interest exhibit a typical annual pattern of increased abundance in the spring, summer and autumn months, the frequency of events above the safety threshold on a countrywide scale is highly variable between years with no increasing pattern throughout the time series (Figure 2) for any of the taxa considered

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Summary

Introduction

Harmful algal blooms (HABs), associated with human and shellfish toxicity, are temporally and spatially variable. Trends of increasing HAB frequency and/or abundance are not evident in all studies (Moore et al, 2009; Díaz et al, 2016; Dees et al, 2017) suggesting that local or regional variability is likely in any overall trend. This is consistent with recent results from Hallegraeff et al (2021) who used a metaanalysis of Harmful Algae Event Database and Ocean Biodiversity Information system data to demonstrate that there is no empirical support for a global increase in HAB events. The economic consequences of HABs for shellfish aquaculture have recently been shown to be significant (Mardones et al, 2020), with Martino et al (2020) estimating that the incidence of biotoxins associated with Diarrheic Shellfish Poisoning (DSP) causes an annual average loss of 15% in mussel production in Scottish waters, equivalent to £1.37 m

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