The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

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Optical measurements including remote sensing provide a potential tool for the identification of 1 dominant phytoplankton groups and for monitoring spatial and temporal changes in biodiversity 2 in the upper ocean. We examine the application of an unsupervised hierarchical cluster analysis 3 to phytoplankton pigment data and spectra of the absorption coefficient and remote-sensing 4 reflectance with the aim of discriminating different phytoplankton assemblages in open ocean 5 environments under non-bloom conditions. This technique is applied to an optical and 6 phytoplankton pigment data set collected at several stations within the eastern Atlantic Ocean, 7where the surface total chlorophyll-a concentration (TChla) ranged from 0.11 to 0.62 mg m -3 . 8Stations were selected on the basis of significant differences in the ratios of the two most 9 dominant accessory pigments relative to TChla, as derived from High Performance Liquid 10 Chromatography (HPLC) analysis. The performance of cluster analysis applied to absorption and 11 remote-sensing spectra is evaluated by comparisons with the cluster partitioning of the 12 corresponding HPLC pigment data, in which the pigment-based clusters serve as a reference for 13 identifying different phytoplankton assemblages. Two indices, cophenetic and Rand, are utilized 14 in these comparisons to quantify the degree of similarity between pigment-based and optical-15 based clusters. The use of spectral derivative analysis for the optical data was also evaluated, and 16 sensitivity tests were conducted to determine the influence of parameters used in these 17 calculations (spectral range, smoothing filter size, band separation). The results of our analyses 18 indicate that the second derivative calculated from hyperspectral (1 nm resolution) data of the 19 phytoplankton absorption coefficient, a ph (λ), and remote-sensing reflectance, R rs (λ), provide 20 better discrimination of phytoplankton pigment assemblages than traditional multispectral band-21 ratios or ordinary (non-differentiated) hyperspectral data of absorption and remote-sensing 22 reflectance. The most useful spectral region for this discrimination extends generally from 23 3 wavelengths of about 425 -435 nm to wavelengths within the 495 -540 nm range, although in 24 the case of phytoplankton absorption data a broader spectral region can also provide satisfactory 25 results. 26 27 4

Fish otolith morphology has been closely related to landmark selection in order to establish the most discriminating points that can help to differentiate or find common characteristics in sets of otolith images. Fourier analysis has traditionally been used to represent otolith images, since it can reconstruct a version of the contour that is close to the original by choosing a reduced set of harmonic terms. However, it is difficult to locate the contour’s singularities from this spectrum. As an alternative, wavelet transform and curvature scale space representation allow us to quantify the irregularities of the contour and determine its precise position. These properties make these techniques suitable for pattern recognition purposes, ageing, stock determination and species identification studies. In the present study both techniques are applied and used in an otolith classification system that shows robustness against affine image transformations, shears and the presence of noise. The results are interpreted and discussed in relation to traditional morphology studies.

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