Marine Scotland reports the number of farms sampled in the annual publication "Scottish Shellfish production Survey", available at the following web site: https://www.gov.scot/collections/scottishfish-farm-production-surveys/. In 2019, 129 businesses where sampled, distributed as follows: 44 in the West Highlands, 5 in the Orkney Island, 23 in the Shetland Islands, 43 in the Clyde, and 14 in the Outer Hebrides. Consulted on 21 st August 2020 Line 541: Replace "most cultured" with "seafood product that is cultured more than any other" or something like that.We replaced "most cultured" with "dominant shellfish product" Lines 550-553: this explanation is unclear. If you are referring the estimation of the production function, then the effect is not strictly a marginal effect (its a percentage change in production when DSP exceeds the threshold).Part of the sentence has been moved to an early section and any reference to marginality is demanded to the specific section about the explanation of the production function (methods).Lines 641-657: I think this discussion could be left out (except for the variable definitions) and just start with the transformed function.We have simplified this section removing the text that can be considered redundant. However, we prefer to present the full set of equations...this can be easier to follow for the audience that is not familiar with the Cobb-Douglas model.Lines 647-650: It's unclear what the difference is between HAB and BTX. Does the cell threshold match the bio-toxin threshold, so that they are actually the same variable? Both vectors are measured as frequency above a threshold, as described at line 599-605. The two sets of variables are not the same. HAB is a fraction (0-1) above threshold Highlights (for review)

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.