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Absorption Koeffizient der farbigen detritalen Substanz bei 443nm (adg in m^-1 bei 4 km Auflösung): Der Absorptionskoeffizient adg stellt den Anteil des einfallenden Lichts dar, das sowohl von detritalen Partikeln als auch von farbiger gelöster organischer Substanz (CDOM) absorbiert wird. Gelöste organische Substanz ist ein wichtiger Bestandteil des ozeanischen Kohlenstoffkreislaufs. Es wird auch als Proxy verwendet, um die Auswirkungen von Terrigenous Inputs in Küstengewässern zu bewerten. Συντελεστής απορρόφησης της χρωματισμένης αποτριχωτικής ύλης στα 443nm (adg σε m^-1 σε ανάλυση 4 km): Ο συντελεστής απορρόφησης adg αντιπροσωπεύει το κλάσμα του προσπίπτοντος φωτός που απορροφάται τόσο από τα διακριτικά σωματίδια όσο και από τη χρωματισμένη διαλυμένη οργανική ύλη (CDOM). Η διαλυμένη οργανική ύλη είναι ένα σημαντικό συστατικό του ωκεάνιου κύκλου του άνθρακα. Χρησιμοποιείται επίσης ως υποκατάστατο για την εκτίμηση των επιπτώσεων των εδαφικών εισροών στα παράκτια ύδατα. Współczynnik absorpcji barwnej substancji detrytalnej przy 443 nm (adg w m^-1 przy rozdzielczości 4 km): Współczynnik absorpcji adg reprezentuje ułamek padającego światła pochłanianego zarówno przez cząstki detrytalne, jak i przez kolorowe rozpuszczone substancje organiczne (CDOM). Rozpuszczone materia organiczna jest ważnym składnikiem oceanicznego cyklu węgla. Jest on również wykorzystywany jako wskaźnik zastępczy do oceny wpływu czynników atmosferycznych w wodach przybrzeżnych. Coeficientul de absorbție al materiei detritale colorate la 443nm (adg în m^-1 la o rezoluție de 4 km): Coeficientul de absorbție adg reprezintă fracțiunea de lumină incidentă absorbită atât de particulele detritale, cât și de materia organică colorată dizolvată (CDOM). Materia organică dizolvată este o componentă importantă a ciclului carbonului oceanic. Acesta este, de asemenea, utilizat ca indicator pentru a evalua impactul factorilor de producție terrigeni în apele costiere. Assorbiment Koeffiċjent tal-materja detritali kkulurita f’443nm (adg f’m^-1 b’riżoluzzjoni ta’ 4 km): Il-koeffiċjent ta’ assorbiment adg jirrappreżenta l-frazzjoni ta’ dawl inċidentali assorbit kemm minn partiċelli detritali kif ukoll minn materja organika maħlula kkulurita (CDOM). Il-materja organika maħlula hija komponent importanti taċ-ċiklu tal-karbonju oċeaniku. Tintuża wkoll bħala indikatur biex jiġi vvalutat l-impatt tal-inputs terriġenużi fl-ilmijiet kostali. Coefficiente di assorbimento della materia detritale colorata a 443nm (adg in m^-1 a risoluzione di 4 km): Il coefficiente di assorbimento adg rappresenta la frazione di luce incidente assorbita sia dalle particelle detritali che dalla materia organica disciolta colorata (CDOM). La materia organica disciolta è una componente importante del ciclo del carbonio oceanico. Viene anche utilizzato come proxy per valutare l'impatto degli input terrigeni nelle acque costiere. Coeficiente de absorción de materia detrital de color a 443 nm (adg en m^-1 a 4 km de resolución): El coeficiente de absorción adg representa la fracción de luz incidente absorbida tanto por partículas detritales como por materia orgánica disuelta coloreada (CDOM). La materia orgánica disuelta es un componente importante del ciclo del carbono oceánico. También se utiliza como representante para evaluar el impacto de los insumos territoriales en las aguas costeras. Коефициент на абсорбция на цветна детритална материя при 443nm (adg в m^-1 при разделителна способност 4 km): Коефициентът на поглъщане adg представлява частта от падащата светлина, абсорбирана както от детритните частици, така и от оцветената разтворена органична материя (CDOM). Разтворената органична материя е важен компонент на океанския въглероден цикъл. Той се използва и като заместител за оценка на въздействието на теригенните суровини в крайбрежните води. Coefficient d’absorption de la matière détritique colorée à 443nm (adg en m^-1 à 4 km de résolution): Le coefficient d’absorption adg représente la fraction de lumière incidente absorbée à la fois par les particules détritales et par la matière organique dissoute colorée (CDOM). La matière organique dissoute est une composante importante du cycle du carbone océanique. Il sert également de proxy pour évaluer l’impact des apports terriens dans les eaux côtières. Absorptiecoëfficiënt van gekleurde detritale materie bij 443nm (adg in m^-1 bij 4 km resolutie): De absorptiecoëfficiënt adg vertegenwoordigt de fractie van invallend licht geabsorbeerd door zowel detritale deeltjes als gekleurd opgelost organisch materiaal (CDOM). Opgelost organisch materiaal is een belangrijk onderdeel van de oceanische koolstofcyclus. Het wordt ook gebruikt als volmacht om de impact van terrigeneuze inputs in kustwateren te beoordelen.

Origin of the data: Experimental spectroscopic measurements Data Type: experimental measurements, open access supporting information The data are in CSV, DSW and FBSW format. Supporting information are supplied in PDF format. Data generated by instruments: Varian Cary 5E-UV-Vis-NIR spectrophotometer for UV-Vis measurements, Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements. Analytical and procedural information: Stern-Volmer fluorescence quenching experiments, UV-Vis measurements and Fluorescent Quantum Yield determination via ferrioxalate actinometry. Definition of variables: Wavelength, Absorbance, Concentration Units of measurement: nanometers (nm), moles-per-litre (mol/l) Abbreviations: File names and data headers use the following abbreviations: FQY refers to Fluorescence Quantum Yield determination experiments Light refers to irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. Dark refers to non-irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. SVQuench refers to Stern-Volmer quenching experiments RAxx refer to measurements related to allylbenzene. Xx is the amount of quencher in mol/l (05 should be intended as 0.5 mol/l and so on). RTxx refer to measurements related to S-(4-methylphenyl) 4-methylbenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. RExx refer to measurements related to 1,2-dimethoxy-4-(prop-2-en-1-yl)benzene. Xx is the amount of quencher in mol/l as above. RSxx refer to measurements related to styrene. Xx is the amount of quencher in mol/l. RTFxx refer to measurements related to S-(4-fluorophenyl) 4-fluorobenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. MesAcrMe Xx refers to data related to catalyst 9-mesityl-10-methylacridinium. Xx is the amount of catalyst in mol/l as above. DMC for measurements employing dimethylcarbonate as solvent. ACN for measurements employing acetonitrile as solvent. FBSW and DSW data are used by the proprietary software of the Varian spectrometers (CARY WinUV and Cary Eclipse). Information can be found at https://www.agilent.com/en/product/molecular-spectroscopy/uv-vis-uv-vis-nir-spectroscopy/uv-vis-uv-vis-nir-software/cary-winuv-software and https://www.agilent.com/en/product/molecular-spectroscopy/fluorescence-spectroscopy/fluorescence-software/cary-eclipse-software

As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.

Here, we investigate how stochasticity and age-dependence in energy dynamics influence maternal allocation in iteroparous females. We develop a state-dependent model to calculate the optimal maternal allocation strategy with respect to maternal age and energy reserves, focusing on allocation in a single offspring at a time. We introduce stochasticity in energetic costs– in terms of the amount of energy required to forage successfully and individual differences in metabolism – and in feeding success. We systematically assess how allocation is influenced by age-dependence in energetic costs, feeding success, energy intake per successful feeding attempt, and environmentally-driven mortality. First, using stochastic dynamic programming, we calculate the optimal amount of reserves M that mothers allocate to each offspring depending on their own reserves R and age A. The optimal life history strategy is then the set of allocation decisions M(R, A) over the whole lifespan which maximizes the total reproductive success of distant descendants. Second, we simulated the life histories of 1000 mothers following the optimisation strategy and the reserves at the start of adulthood R1, the distribution of which was determined, the distribution of which was determined using an iterative procedure as described . For each individual, we calculated maternal allocation Mt, maternal reserves Rt, and relative allocation Mt⁄Rt at each time period t. The relative allocation helps us to understand how resources are partitioned between mother and offspring. Third, we consider how the optimal strategy varies when there is age-dependence in resource acquisition, energetic costs and survival. Specifically, we include varying scenarios with an age-dependent increase or a decrease with age in energetic costs (c_t), feeding success (q_t), energy intake per successful feeding attempt (y_t), and environmentally-driven extrinsic mortality rate (d_t) (Table 2). We consider the age-dependence of parameters one at a time or in pairs, altering the slope, intercept, or asymptote of the age-dependence (linear or asymptotic function). Our aim is to identify whether the observed reproductive senescence can arise from optimal maternal allocation. As such, we do not impose a decline in selection in later life as all offspring are equally valuable at all ages (for a given maternal allocation), and there are no mutations. For each scenario, we run the backward iteration process with these age-dependent functions, obtain the allocation strategy, and simulate the life history of 1000 individuals based on the novel strategy. We then fit quadratic and linear models to the reproduction of these 1000 individuals using the lme function, nlme package in R. For these models, the response variable is the maternal allocation Mt and explanatory variables are the time period t and t2 (for the quadratic fit only), with individual identity as a random term. We use likelihood ratio tests to compare linear and quadratic models using the anova function (package nlme) with the maximum-likelihood method. If the comparison is significant (p-value <0.05), we considered the quadratic model to have a better fit, otherwise the linear model is considered more parsimonious. We were particularly interested in identifying scenarios where the fit was quadratic with a negative quadratic term. For each scenario, the pseudo R2 conditional value (proportion of variance explained by the fixed and random terms, accounting for individual identity) is calculated to assess the goodness-of-fit of the lme model, on a scale from 0 to 1, using the “r.squared” function, package gabtool. All calculations and coding are done in R. Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.

[ES] Se ha aplicado la clasificación de tipos de tiempo (Weather Types) de Jenkinson y Collison a la malla de presiones diaria del reanálisis NCAR-NCEP (periodo Enero 1950-Diciembre 2023) correspondiente a la Península Ibérica y Baleares. Por la resolución de dicha malla (2.5º x 2.5º lat/long) el total de nodos de control es de 12. Los tipos de tiempo resultantes incluyen los 8 direccionales puros, Anticiclónico y Ciclónico puro, y la combinación de 8 tipos híbridos entre las categorías previas. Los casos indeterminados fueron distribuidos proporcionalmente entre las clases previas. [EN] It has been applied the Jenkinson & Collison classification of Weather Types to Iberian Peninsula and Balearic Island by using the daily NCAR-NCEP grid surface pressure dataset (January-1950/December-2023). Grid resolution/2.5ºx2.5 lat/long) produces 12 series. Weather Types classification includes 8 directional pure, Anticyclonic and Cyclonic pure types, and combination of previous ones in the hybrid types. Non determines cases were spread homogeneously. [EN] WETYDAS contains 12 TXT archives localized by their coordinates in NCAR grid; information include year, month, day and code of weathee type. [ES] WETYDAS consta de 12 archivos formato TXT geolocalizados por sus coordenadas en la malla NCAR; la información incluye el año, mes y día, así como el código del tipo de tiempo resultante.

Herbivory assessments were made at the plant community and species levels. We focused on three plant species with a widespread occurrence across the temperature gradient: cuckooflower (Cardamine pratensis, Linnaeus), common mouse-ear (Cerastium fontanum, Baumgerten), and marsh violet (Viola palustris, Linnaeus). For assessments of invertebrate herbivory at the species level, thirty individuals per species of C. pratensis, C. fontanum, and V. palustris were marked in each of ten plots, using a stratified random sampling method where individuals were randomly selected, but the full range of within-plot soil temperatures was represented. For assessments of invertebrate herbivory at the community level, five 50 × 50 cm quadrats were marked at random points in eight of the plots that best captured the full temperature gradient. The community-level herbivory assessment was conducted on 19th June. The number of damaged plants was recorded out of 100 random individuals, selected using a 10 × 10 grid within each 50 × 50 cm quadrat. For the species-level herbivory assessment, individual marked plants were surveyed for signs of invertebrate herbivory every two weeks from 30th May to 2nd July, generating three time-points per species. At each survey, all marked individuals for each species were assessed within a 48-hour period. Plants were recorded as damaged or not damaged by invertebrate herbivores at each time-point. Further details of how phenological stage of development, vegetation community composition, soil temperature, moisture, pH, nitrate, ammonium, and phosphate were recorded are provided in the supporting documentation. This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate.

We performed systematic mapping of EPC data applications by time, geographical spread, data features & auxiliary data used, problem domains addressed and complexity of employed data analysis. This mapping work was intended to answer the following questions: Q1. Which research studies have used EPC data (hereafter “applications”)? Q2. What input data were used by the EPC data applications? Q3. Which problem domains were addressed by the EPC data applications? Q4. How did the EPC data applications change within the study period? Purpose: To understand how the energy performance certificates (EPC) data has been used since introduction of the first national EPC registers. Kartläggning av tillämpningar av EPC data. En mer detaljerad beskrivning är tillgängligt på den engelska katalogsidan: https://snd.gu.se/en/catalogue/study/SND1066

# sRGB Reflectances from Iberian butterflies [https://doi.org/10.5061/dryad.1g1jwsv0q](https://doi.org/10.5061/dryad.1g1jwsv0q) Data on wing reflectance (visible spectrum, mean standard RGB values (243.7= white, to 52= black) from 224 species of butterflies (Lepidoptera, Papilionoidea): 223 from the Iberian Peninsula and one (*C. webbianus*) from the Canaries. Average of male and female, sample size as indicated in column n. The data from *C. webbianus* and *C. marshalli* were not included in our analyses of reflectance. Text file, CSV format, columns delimited by periods, 225 rows (including headings) and 38 columns. Any means presented are weighted averages taking into account the areas of the parts involved. Wing reflectances refer to the parts of the wings exposed in a living butterfly (except FW\_AREA and HW\_AREA which are total wing surfaces). * **Ord**, row number (roughly a taxonomic arrangement) * **Species**, species name (abbreviated genus, contains a blank space, e.g., *Heteropterus morpheus*) * **N**, sample size * **FWL**, forewing length (mm) * **DFT**, reflectance, dorsal forewing * **DFp**, reflectance, dorsal forewing, proximal area * **DFd**, reflectance, dorsal forewing, distal area * **DHT**, reflectance, dorsal hindwing * **DHp**, reflectance, dorsal hindwing, proximal area * **DHd**, reflectance, dorsal hindwing, distal area * **DB**, reflectance, dorsal body area * **D(Tp+B)**, reflectance of the exposed dorsal body plus proximal wing surfaces * **DT**, reflectance of the dorsal areas (body plus whole wing) * **DTp**, reflectance of the dorsal, proximal wing areas * **DTd**, reflectance of the dorsal, distal wing areas * **VFT**, reflectance, ventral forewing * **VFp**, reflectance, ventral forewing, proximal area * **VFd**, reflectance, ventral forewing, distal area * **VHT**, reflectance, ventral hindwing * **VHp**, reflectance, ventral hindwing, proximal area * **VHd**, reflectance, ventral hindwing, distal area * **VB**, reflectance, ventral body area * **V(Tp+B)**, reflectance of the exposed ventral body plus proximal wing surfaces * **VT**, reflectance of the ventral areas (body plus whole wing) * **VTp**, reflectance of the ventral, proximal wing areas * **VTd**, reflectance of the ventral, distal wing areas * **Mean**, mean total reflectance (dorsal and ventral surfaces) * **p\_Mean**, mean reflectance of the proximal (dorsal and ventral) wing areas * **p\_Otimum**, mean reflectance of the proximal dorsal (for dorsal baskers) or ventral (for lateral basking species) wing areas. * **FW\_area**, total forewing area (mm2) * **HW\_area**, total hindwing area (mm2) * **T\_Mean\_Iberia\_10km**, Iberian mean species temperature, Centigrade degrees, 10 x 10 km resolution * **P\_Mean\_Iberia\_10km**, mean species annual precipitation, mm, Iberian Peninsula, 10 x 10 km resolution * **T\_Mean\_Ibera\_50km**, mean species temperature, Centigrade degrees, Iberian Peninsula, 50 x 50 km resolution * **P\_Mean\_Iberia\_50km**, mean species annual precipitation, mm, Iberian Peninsula, 50 x 50 km resolution Data on wing reflectance (visible spectrum, mean standard RGB values (243.7= white, to 52= black) from 224 species of butterflies (Lepidoptera, Papilionoidea): 223 from the Iberian Peninsula and one (Cyclyrius webbianus) from the Canary Islands. Average of male and female, sample size as indicated in column n. The data from C. webbianus and Cacyreus marshalli are provided although these species were not included in our analyses of reflectance. The data were measured from digital images of set (collection) specimens taken in fixed conditions, with grey (average RGB) values standardized a posteriori to fit the scale white= 243.7= white, to black= 52. The data set includes the mean length of the forewing (mm) and the total areas (mm2) of the fore and hind wings.

Climatologie mensuelle de la surface de la mer Chlorophylle-a concentration (en mg.m^-3 (log10) à résolution de 4 km) dérivée du capteur SeaWiFS (Satellite télédétection de la couleur de l’océan): La chlorophylle est un pigment photosynthétique couramment présent dans toutes les espèces de phytoplancton. Il est utilisé comme indicateur de la biomasse du phytoplancton. La concentration de chlorophylle est un produit standard à partir de capteurs optiques satellitaires, habituellement récupérés à partir d’algorithmes empiriques utilisant des rapports de réflectance à deux bandes d’onde ou plus. Μηνιαία κλιματολογική επιφάνεια της θάλασσας χλωροφύλλη — μια συγκέντρωση (σε mg.m^-3 (log10) σε ανάλυση 4 km) που προέρχεται από τον αισθητήρα SeaWiFS (Δορυφορικά δεδομένα χρωμάτων του Ωκεανού με τηλεπισκόπηση): Η χλωροφύλλη είναι μια φωτοσυνθετική χρωστική ουσία που υπάρχει συνήθως σε όλα τα είδη φυτοπλαγκτού. Χρησιμοποιείται ως υποκατάστατο της βιομάζας φυτοπλαγκτού. Η συγκέντρωση χλωροφύλλης είναι ένα πρότυπο προϊόν από δορυφορικούς οπτικούς αισθητήρες, που συνήθως ανακτώνται από εμπειρικούς αλγόριθμους χρησιμοποιώντας αναλογίες ανάκλασης σε δύο ή περισσότερες ζώνες κύματος. Monatliche Klimatologie der Meeresoberfläche Chlorophyll-eine Konzentration (in mg.m^-3 (log10) bei 4 km Auflösung) abgeleitet vom SeaWiFS-Sensor (Satellite Remote Sensing Ocean Color Data): Chlorophyll ist ein photosynthetisches Pigment, das häufig in allen Phytoplanktonarten vorhanden ist. Es wird als Proxy für Phytoplanktonbiomasse verwendet. Chlorophyll-Konzentration ist ein Standardprodukt von satellitenbasierten optischen Sensoren, die normalerweise aus empirischen Algorithmen unter Verwendung von Reflexionsverhältnissen bei zwei oder mehr Wellenbändern abgerufen werden. Kull xahar il-klimatoloġija wiċċ il-baħar Chlorophyll-a konċentrazzjoni (f’mg.m^-3 (log10) b’riżoluzzjoni ta’ 4 km) derivata mis-sensur SeaWiFS (data tal-kulur tal-Oċean tat-telerilevament bis-satellita): Il-klorofilla hija pigment fotosintetiku preżenti b’mod komuni fl-ispeċijiet kollha tal-fitoplankton. Jintuża bħala indikatur għall-bijomassa tal-fitoplankton. Il-konċentrazzjoni tal-klorofilla hija prodott standard minn sensuri ottiċi bbażati fuq is-satellita, normalment miksuba minn algoritmi empiriċi bl-użu ta’ proporzjonijiet ta’ riflessjoni f’żewġ wavebands jew aktar. Tiúchan Clíomeolaíochta míosúil dromchla farraige Chlorophyll-a (i mg.m^-3 (log10) ag taifeach 4 km) a dhíorthaítear ón mbraiteoir SeaWiFS (sonraí maidir le dath an Aigéin chianbhraiteachta satailít): Is lí fótaisintéiseach é clóraifill atá i láthair go coitianta i ngach speiceas fíteaplanctóin. Úsáidtear é mar sheachvótálaí le haghaidh bithmhais fíteaplanctóin. Is táirge caighdeánach é tiúchan clóraifille ó bhraiteoirí optúla bunaithe ar shatailít, a fhaightear ó algartaim eimpíreacha de ghnáth agus cóimheasa frithchaite á n-úsáid ag dhá thonnbhanda nó níos mó. Climatología mensual de Chlorophyll-una concentración (en mg.m^-3 (log10) a una resolución de 4 km) derivada del sensor SeaWiFS (datos de detección remota por satélite del color del océano): La clorofila es un pigmento fotosintético comúnmente presente en todas las especies de fitoplancton. Se utiliza como un indicador de la biomasa de fitoplancton. La concentración de clorofila es un producto estándar de sensores ópticos basados ​​en satélites, generalmente recuperado de algoritmos empíricos utilizando relaciones de reflectancia en dos o más bandas de onda. Щомісячна кліматологія морської поверхні Хлорофіл-а концентрація (в мг.м^-3 (log10) при роздільній здатності 4 км), отримана з датчика SeaWiFS (дані кольору супутникового дистанційного зондування океану): Хлорофіл - це фотосинтетичний пігмент, який зазвичай присутній у всіх видах фітопланктону. Використовується як проксі для біомаси фітопланктону. Концентрація хлорофілу є стандартним продуктом з супутникових оптичних датчиків, зазвичай отриманих з емпіричних алгоритмів з використанням коефіцієнтів відбиття на двох або більше хвильових діапазонах. Месечна климатология на морската повърхност Хлорофил — концентрация (в mg.m^-3 (log10) при резолюция 4 km), получена от сензора SeaWiFS (Satellite дистанционно наблюдение на данните за цвета на океана): Хлорофилът е фотосинтетичен пигмент, обикновено присъстващ във всички видове фитопланктон. Използва се като прокси за фитопланктоновата биомаса. Концентрацията на хлорофил е стандартен продукт от сателитни оптични сензори, обикновено извличани от емпирични алгоритми, използващи съотношения на отразяване при две или повече ленти на вълната. Miesięczny klimatologia powierzchni morza Chlorophyll – stężenie (w mg.m^-3 (log10) w rozdzielczości 4 km) pochodzące z czujnika SeaWiFS (dane satelitarnej teledetekcji barwy oceanu): Chlorofil jest pigmentem fotosyntetycznym powszechnie występującym we wszystkich gatunkach fitoplanktonu. Jest stosowany jako zastępca biomasy fitoplanktonu. Stężenie chlorofilu jest standardowym produktem z satelitarnych czujników optycznych, zwykle pobieranych z algorytmów empirycznych przy użyciu współczynników odbicia w dwóch lub więcej pasmach fal. Climatologie lunară la suprafața mării clorofilă – o concentrație (în mg.m^-3 (log10) la o rezoluție de 4 km) derivată din senzorul SeaWiFS (Satelit teledetecție date de culoare ocean): Clorofila este un pigment fotosintetic prezent frecvent în toate speciile de fitoplancton. Este folosit ca un indicator pentru biomasa fitoplanctonă. Concentrația de clorofilă este un produs standard al senzorilor optici bazați pe sateliți, de obicei extrași din algoritmi empirici folosind raporturi de reflexie la două sau mai multe benzi de undă.