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Monitoring phytoplankton community composition from space is an important challenge in ocean remote sensing. Researchers have proposed several algorithms for this purpose. However, the in-situ data used to train and validate such algorithms at the global scale are often clustered along ship cruise tracks and in some well-studied locations, whereas many large marine regions have no in-situ data at all. Furthermore, oceanographic variables are typically spatially auto-correlated. In this situation, the common practice of validating algorithms with randomly chosen held-out observations can underestimate errors. Based on a global database of in-situ HPLC data, we applied supervised learning methods to train and test empirical algorithms predicting the relative concentrations of eight diagnostic pigments that serve as biomarkers for different phytoplankton types. For each pigment, we trained three types of satellite algorithms distinguished by their input data: abundance-based (using only chlorophyll-a as input), spectral (using remote sensing reflectance), and ecological algorithms (combining reflectance and environmental variables). The algorithms were implemented as statistical models (smoothing splines, polynomials, random forests and boosted regression trees). To address clustering of data and spatial auto-correlation, we tested the algorithms by means of spatial block cross-validation. This provided a less confident picture of the potential for global mapping of diagnostic pigments and hence the associated phytoplankton types using existing satellite data than suggested by some previous research and a 5-fold cross-validation conducted for comparison. Of the eight diagnostic pigments, only two (fucoxanthin and zeaxanthin) could be predicted in marine regions that the algorithms were not trained in with considerably lower errors than a constant null model. Thus, global-scale algorithms based on existing, multi-spectral satellite data and commonly available environmental variables can estimate relative diagnostic pigment concentrations and hence distinguish phytoplankton types in some broad classes, but are likely inaccurate for some classes and in some marine regions. Overall, the ecological algorithms had the lowest prediction errors. Finally, our results suggest that more discussion of the best approaches for training and validating empirical satellite algorithms is needed if the in-situ data are unevenly distributed in the study region and spatially clustered.

Expert knowledge can help fill gaps in quantitative empirical information about complex ecological phenomena. We examined the level of agreement between 21 studies that collected expert ratings of the sensitivity of species and habitats to human activities and their pressures as input data for mapping the human impact on marine ecosystems. Our analyses revealed broad agreement about which human activities and pressures many species and habitats are sensitive to. These agreements reflect a common view of the main threats to ocean ecosystems. In contrast, scores provided by individual experts varied both within and across studies. Sensitivity scores collected with the same method for different regions were often more similar than scores collected for the same region but with different methods. These results highlight how inconsistencies in the design of many expert surveys can lead to variable outcomes. It is important to employ more consistent and theoretically grounded methods and protocols when eliciting expert ratings of species’ sensitivity to pressures, to ensure compatibility across studies and maintain rigour in analyses supporting effective ocean management.

Marine remote sensing provides comprehensive characterizations of the ocean surface across space and time. However, cloud cover is a significant challenge in marine satellite monitoring. Researchers have proposed various algorithms to fill data gaps “below the clouds”, but a comparison of algorithm performance across several geographic regions has not yet been conducted. We compared ten basic algorithms, including data-interpolating empirical orthogonal functions (DINEOF), geostatistical interpolation, and supervised learning methods, in two gap-filling tasks: the reconstruction of chlorophyll a in pixels covered by clouds, and the correction of regional mean chlorophyll a concentrations. For this purpose, we combined tens of cloud-free images with hundreds of cloud masks in four study areas, creating thousands of situations in which to test the algorithms. The best algorithm depended on the study area and task, and differences between the best algorithms were small. Ordinary Kriging, spatiotemporal Kriging, and DINEOF worked well across study areas and tasks. Random forests reconstructed individual pixels most accurately. We also found that high levels of cloud cover led to considerable errors in estimated regional mean chlorophyll a concentration. These errors could, however, be reduced by about 50% to 80% (depending on the study area) with prior cloud-filling.

Effective management and mitigation of multiple human impacts on marine ecosystems require accurate knowledge of the spatial patterns of human activities and their overlap with vulnerable habitats. Cumulative impact (CI) mapping combines spatial information and the intensity of human activities with the spatial extent of habitats and their vulnerabilities to those stressors into an intuitive relative CI score that can inform marine spatial planning processes and ecosystem‐based management. Here, we mapped potential CIs of 45 human activities from five sectors (climate change, land‐based, marine‐based, coastal, commercial fishing) on 21 habitats in Atlantic Canada's Scotian Shelf bioregion. We applied an uncertainty and sensitivity analysis to assess the robustness of results and identify hot and cold spots of CIs. Nearly the entire Scotian Shelf bioregion experiences the CIs of human activities, and high CIs were frequently associated with multiple stressors. CIs varied widely across habitats: CI scores in habitats >30 m deep were dominated by climate change and commercial fishing, while nearshore habitats were influenced by a much wider range of activities across all five sectors. When standardized by area, coastal habitats had among the highest CI scores, highlighting the intensity of multiple stressors in these habitats despite their relatively small spatial extent and emphasizing the importance of a multisector approach when managing coastal ecosystems. Robust hot spots of CIs (i.e., areas with high CI scores that were insensitive to alternative modeling assumptions and simulated data quality issues) occurred mostly in coastal areas where multiple high‐intensity activities overlapped with highly vulnerable biogenic habitats. In contrast, robust cold spots of CI mostly occurred offshore. Overall, our results emphasize the need to consider CIs in management and protection and demonstrates that, in many areas, targeting only one activity will be insufficient to reduce overall human impact. The CI map will be useful to highlight areas in need of protection from multiple human impacts, provide information for ecological indicator development, and establish a baseline of the current state of human use in the bioregion.

Aim: Many studies have quantified and mapped cumulative human impacts on marine ecosystems. These maps are intended to inform management and planning, but uncertainty in them has not been studied in depth. This paper aims to: (1) quantify the uncertainty in cumulative impact maps and related spatial modelling results; (2) attribute this uncertainty to specific model assumptions and problems with data quality; (3) identify and test sound approaches to such analyses. Location: We used the Baltic Sea and the Mediterranean and Black Seas as example regions. The methods and conclusions are relevant for human impact mapping anywhere. Methods: We conducted computational experiments to test the effects of nine model assumptions and data quality problems (factors) on maps of human impact and related modelling results. The factors were implemented on the basis of a literature review. We quantified aggregate uncertainty using Monte Carlo simulations, and ranked the factors by their influence on modelling results using the elementary effects method. Both methods are well established and theoretically suitable for complex models, but had to be modified for application to spatial human impact models. Results: Some, but not all, modelling results were robust. This contradicts previous studies that found only minor effects of the factors they tested. Of the nine factors tested here, eight had a considerable influence on at least one modelling result in at least one of the two study regions. Main conclusions: Model assumptions and data quality have larger aggregate effects on maps of human impact than found in previous analyses. These effects depend on the study region and the data that describe it. Future human impact mapping studies should thus include comprehensive uncertainty analyses. Computational experiments allow us to distinguish robust from less reliable modelling results and to prioritize improvements in models and data.

Increasing anthropogenic pressure on marine ecosystems from fishing, pollution, climate change, and other sources is a big concern in marine conservation. Scientists have thus developed spatial models to map cumulative human impacts on marine ecosystems. However, these models are based on many assumptions and incorporate data that suffer from substantial incompleteness and inaccuracies. Rather than using a single model, we used Monte Carlo simulations to identify which parts of the oceans are subject to the most and least impact from anthropogenic Stressors under 7 simulated sources of uncertainty (factors: e.g., missing Stressor data and assuming linear ecosystem responses to stress). Most maps agreed that high-impact areas were located in the Northeast Atlantic, the eastern Mediterranean, the Caribbean, the continental shelf off northern West Africa, offshore parts of the tropical Atlantic, the Indian Ocean east of Madagascar, parts of East and Southeast Asia, parts of the northwestern Pacific, and many coastal waters. Large low-impact areas were located off Antarctica, in the central Pacific, and in the southern Atlantic. Uncertainty in the broad-scale spatial distribution of modeled human impact was caused by the aggregate effects of several factors, rather than being attributable to a single dominant source. In spite of the identified uncertainty in human-impact maps, they can—at broad spatial scales and in combination with other environmental and socioeconomic information—point to priority areas for research and management. El incremento de la presión antropogénica sobre los ecosistemas marinos a partir de la pesca, la contaminación, el cambio climático, y otras fuentes es causa de una gran preocupación dentro de la conservación marina. Por esto, los científicos han desarrollado modelos espaciales para mapear los impactos humanos acumulativos sobre los ecosistemas marinos. Sin embargo, estos modelos están basados en muchas suposiciones e incorporan datos que sufren de errores y falta de información sustanciales. En lugar de utilizar solamente un modelo, usamos simulaciones Monte Cario para identificar las regiones de los océanos que están sujetas al mayor y al menor impacto por estresantes antropogénicos bajo siete fuentes simuladas de incertidumbre (factores: p. ej., falta de datos sobre el estresante y la suposición de respuestas ambientales lineales ante el estrés). La mayoría de los mapas concordaron en que las áreas de alto impacto estaban localizadas en el noreste del Atlántico, el este del Mediterráneo, el Caribe, la plataforma continental del oeste de África, algunas regiones del litoral del Atlántico tropical, el océano índico al este de Madagascar, algunas partes del este y sureste de Asia, algunas partes del noroeste del Pacífico, y muchas aguas costeras. Las grandes áreas de bajo impacto se ubicaron en las costas de la Antártida, en el centro del Pacífico, y en el sur del Atlántico. La incertidumbre en la distribución espacial a escala general de los impactos humanos fue causada por los efectos agregados de varios factores, en lugar de ser atribuible a un solo origen dominante. A pesar de la incertidumbre identificada en los mapas de impacto humano, estos pueden - a escalas espaciales generalizadas y en combinación con otra información ambiental y socioeconómica - señalar hacia áreas prioritarias para la investigación y el manejo. 越来越多渔业、污染、气候变化和其它来源的人类活动压カ正在成为海洋生态系统保护的一大问题。科 学家为此开发了空间模型来模拟人类对海洋生态系统的累计影响。然而，这些模型建立在许多假说上，还整合了 大量不完整和不准确的数据。相比于单ー模型，我们则使用了蒙特卡罗模拟来确定在七个模拟的不确定性因素 (如缺失压力因素的数据、假设生态系统对压カ的响应是线性的) 下，海洋受到人类活动压カ影响最大和最小的 地区。大多数模拟结果都显示，受到影响较大的是大西洋东北部、地中海东部、加勒比海、西非北部大陆架、 热带大西洋近海地区、马达加斯加以东的印度洋、东亚和东南亚部分地区、太平洋西北部的部分地区，以及许 多沿海水域。而受到影响较小的大片区域则位于南极洲外、太平洋中部和大西洋南部。模拟人类影响的大尺 度空间分布分析中的不确定性来自多个因素的综合效应，而不能归因于某个单ー的主要因素。虽然人类影响效 应确实存在不确定性，但它们可以在较大空间尺度上, 結合其它环境和社会经济学信息，指出研究和管理的优先 区域。

Machine learning is a popular tool in ecology but many scientific applications suffer from data leakage, causing misleading results. We highlight common pitfalls in ecological machine-learning methods and argue that discipline-specific model info sheets must be developed to aid in model evaluations.

People use and affect the oceans in many ways. This has resulted in a world-wide degradation of coastal and marine ecosystems. The need for better spatial management of threats like fishing and pollution has led researchers to develop maps of human impact on marine ecosystems. Yet these maps are based on simple spatial models, whereas experimental research shows that the effects of multiple stressors on marine organisms, populations and ecosystems are complex. This dissertation thus seeks to improve the methodological and conceptual foundations of spatial human impact modeling by advancing two parallel lines of research. First, I use computational experiments to investigate uncertainty related to model assumptions and data quality in regional and global maps created with a widely used spatial human impact model. This research shows that sources of uncertainty which alone have only small effects on human impact maps, have large effects in combination. However, I also identify spatial patterns of human impact that are robust, i.e. that are consistently found under a wide range of model assumptions and that are insensitive to data quality. Second, as an empirical alternative to established models, I use statistical learning methods to train and test regression models that predict indicators of marine ecosystem condition based on human stressor maps, satellite images and other spatial data. This research shows that such methods can generate accurate maps of ecological indicators, but also that the availability of suitable data is a major barrier to using statistical and machine learning for human impact mapping to its full potential.

This report outlines the results of the internationally coordinated census of wintering waterbirds in the Baltic Sea 2007-2009 undertaken under the SOWBAS project (Status of wintering Waterbird populations in the Baltic Sea). The estimated total number of wintering waterbirds was 4.41 million compared to 7.44 million during the last co-ordinated census 1992-1993.  Despite the general declines stable or increasing populations of herbivorous species were recorded. While benthic carnivores with a coastal distribution have either shown moderate declines, stable or increasing populations seaducks with an offshore distribution have declined seriously. Based on analyses of trends in wintering waterbirds and pressures indicators are suggested as performance indicators in relation to the international and national actions taken to reduce the anthropogenic pressures in the Baltic Sea.

Winterbourne Under-15s all-rounder Kieran Slade was in outstanding form, contributing an unbeaten 50 and claiming 3-6 with the ball to engineer a 44-run triumph over Rockhampton in Section A. In the first round of the Harry Secombe National Cup, Bishopston's Ben Jarman hit 31 as his side made 84-7 against Bristol YMCA B. In a nail-biting finish, YMCA had Sam Higgins to thank as his 52 not out steered his side to a one-wicket victory as they reached 85-9.