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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.

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

The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community. The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community.

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.

Productividad del agua (producción primaria, pp en gCarbon.m-2.day-1 a 4 km de resolución): La producción primaria representa la cantidad de carbono orgánico producido a través de la fotosíntesis del fitoplancton. Es un elemento crítico del presupuesto de carbono de la Tierra y de la red alimentaria marina. La producción primaria integrada en profundidad se modela a partir de la concentración de biomasa de fitoplancton basada en satélites y el PAR. Waterproductiviteit (primaire productie, pp in gCarbon.m-2.day-1 bij een resolutie van 4 km): De primaire productie vertegenwoordigt de hoeveelheid organische koolstof die wordt geproduceerd door middel van fytoplanktonfotosynthese. Het is een cruciaal element van het koolstofbudget van de aarde en het mariene voedselweb. De diepgeïntegreerde primaire productie wordt gemodelleerd van de satellietgebaseerde fytoplanktonbiomassaconcentratie en PAR. Il-produttività tal-ilma (produzzjoni primarja, pp f’gCarbon.m-2.day-1 b’riżoluzzjoni ta’ 4 km): Il-produzzjoni primarja tirrappreżenta l-ammont ta’ karbonju organiku prodott permezz tal-fotosinteżi tal-fitoplankton. Huwa element kritiku tal-baġit tal-karbonju tad-Dinja u tax-xibka tal-ikel tal-baħar. Il-produzzjoni primarja integrata fil-fond hija mmudellata mill-konċentrazzjoni tal-bijomassa tal-fitoplankton ibbażata fuq is-satellita u PAR. Productivité de l’eau (production primaire, pp dans gCarbon.m-2.day-1 à une résolution de 4 km): La production primaire représente la quantité de carbone organique produite par la photosynthèse phytoplancton. C’est un élément essentiel du budget carbone de la Terre et du réseau alimentaire marin. La production primaire intégrée en profondeur est modélisée à partir de la concentration de biomasse du phytoplancton par satellite et du PAR. Производителност на водата (първично производство, pp в gCarbon.m-2.day-1 при разделителна способност 4 km): Първичното производство представлява количеството органичен въглерод, произведен чрез фотосинтеза на фитопланктона. Това е критичен елемент от въглеродния бюджет на Земята и морската хранителна мрежа. Дълбочинно интегрирано първично производство е моделирано от сателитната концентрация на фитопланктоновата биомаса и PAR. Παραγωγικότητα του νερού (πρωτογενής παραγωγή, pp σε gCarbon.m-2.ημέρα-1 σε ανάλυση 4 km): Η πρωτογενής παραγωγή αντιπροσωπεύει την ποσότητα οργανικού άνθρακα που παράγεται μέσω φωτοσύνθεσης φυτοπλαγκτού. Είναι ένα κρίσιμο στοιχείο του προϋπολογισμού άνθρακα της Γης και του θαλάσσιου ιστού τροφίμων. Η ενσωματωμένη σε βάθος πρωτογενής παραγωγή διαμορφώνεται με βάση τη συγκέντρωση βιομάζας φυτοπλαγκτού μέσω δορυφόρου και την PAR. Produttività dell'acqua (produzione primaria, pp in gCarbon.m-2.day-1 a risoluzione 4 km): La produzione primaria rappresenta la quantità di carbonio organico prodotto attraverso la fotosintesi del fitoplancton. È un elemento critico del bilancio del carbonio della Terra e della rete alimentare marina. La produzione primaria integrata in profondità è modellata dalla concentrazione satellitare di biomassa di fitoplancton e PAR. Wasserproduktivität (Primärproduktion, pp in gCarbon.m-2.day-1 bei 4 km Auflösung): Die Primärproduktion repräsentiert die Menge an organischem Kohlenstoff, der durch Phytoplankton-Photosynthese erzeugt wird. Es ist ein kritisches Element des CO2-Budgets der Erde und des marinen Nahrungsnetzes. Die tiefenintegrierte Primärproduktion wird aus der satellitengestützten Phytoplankton-Biomasse-Konzentration und PAR modelliert. Produtividade da água (produção primária, pp em gCarbon.m-2.day-1 com resolução de 4 km): A produção primária representa a quantidade de carbono orgânico produzido através da fotossíntese de fitoplâncton. É um elemento crítico do orçamento de carbono da Terra e da rede alimentar marinha. A produção primária integrada em profundidade é modelada a partir da concentração de biomassa de fitoplâncton por satélite e PAR. Продуктивність води (первинне виробництво, pp в gCarbon.m-2.day-1 при роздільній здатності 4км): Первинне виробництво являє собою кількість органічного вуглецю, отриманого шляхом фотосинтезу фітопланктону. Це важливий елемент вуглецевого бюджету Землі і морської продовольчої мережі. Глибина інтегрованого первинного виробництва моделюється з концентрації біомаси на основі супутника фітопланктону та PAR.

Średnia miesięczna temperatura powierzchni morza (w stopniach C przy rozdzielczości 4 km) pochodząca z czujnika PATHFINDER (dane satelitarnej teledetekcji barwy oceanu): Temperatura powierzchni morza to temperatura wody blisko powierzchni morza. SST jest standardowym produktem z satelitarnych czujników termicznych na podczerwień oraz czujników optycznych uzupełnionych pasmami podczerwieni. Średnia miesięczna temperatura powierzchni morza (w stopniach C przy rozdzielczości 4 km) pochodząca z czujnika PATHFINDER (dane satelitarnej teledetekcji barwy oceanu): Temperatura powierzchni morza to temperatura wody blisko powierzchni morza. SST jest standardowym produktem z satelitarnych czujników termicznych na podczerwień oraz czujników optycznych uzupełnionych pasmami podczerwieni. Température moyenne mensuelle de la surface de la mer (en degrés C à une résolution de 4 km) dérivée du capteur PATHFINDER (Télédétection satellite de la couleur de l’océan): La température de la surface de la mer est la température de l’eau près de la surface de la mer. SST est un produit standard à partir de capteurs infrarouges thermiques satellitaires, et de capteurs optiques complétés par des bandes infrarouges. Μηνιαία μέση θερμοκρασία επιφάνειας της θάλασσας (σε βαθμό-C σε ανάλυση 4 km) προερχόμενη από τον αισθητήρα PATHFINDER (Δορυφορικά δεδομένα χρώματος Ωκεανού τηλεπισκόπησης): Η θερμοκρασία επιφάνειας της θάλασσας είναι η θερμοκρασία του νερού κοντά στην επιφάνεια της θάλασσας. Το SST είναι ένα πρότυπο προϊόν από δορυφορικούς θερμικούς υπέρυθρους αισθητήρες και οπτικούς αισθητήρες που συμπληρώνονται με υπέρυθρες ζώνες. Temperatura media mensual de la superficie del mar (en grado C a una resolución de 4 km) derivada del sensor PATHFINDER (datos de color del océano de teledetección por satélite): La temperatura de la superficie del mar es la temperatura del agua cerca de la superficie del mar. SST es un producto estándar de sensores infrarrojos térmicos basados en satélites, y sensores ópticos complementados con bandas infrarrojas. Месечна средна температура на морската повърхност (в градус-C при разделителна способност 4 km), получена от сензора PATHFINDER (Сателитна дистанционна сензорна информация за цветовете на океана): Температурата на морската повърхност е температурата на водата в близост до морската повърхност. SST е стандартен продукт от сателитни термоинфрачервени сензори и оптични сензори, допълнени с инфрачервени ленти. Meánteocht mhíosúil dhromchla na farraige (i gcéim-C ag taifeach 4 km) a dhíorthaítear ón mbraiteoir PATHFINDER (sonraí cianbhraiteachta satailíte maidir le dath an Aigéin): Is é teocht dhromchla na farraige teocht an uisce gar do dhromchla na farraige. Is táirge caighdeánach é SST ó bhraiteoirí infridhearg teirmeacha atá bunaithe ar shatailítí, agus braiteoirí optúla arna gcomhlánú le bandaí infridhearg. Mėnesio vidutinė jūros paviršiaus temperatūra (C laipsniais esant 4 km skyrai), gauta iš PATHFINDER jutiklio (palydovinio nuotolinio stebėjimo vandenyno spalvų duomenys): Jūros paviršiaus temperatūra yra vandens temperatūra arti jūros paviršiaus. SST yra standartinis produktas iš palydovinių šiluminių infraraudonųjų spindulių jutiklių ir optinių jutiklių, papildytų infraraudonųjų spindulių juostomis. It-temperatura medja fix-xahar tas-superfiċje tal-baħar (fi grad-C b’riżoluzzjoni ta’ 4 km) derivata mis-sensur PATHFINDER (data tal-kulur tal-Oċean tat-telerilevament bis-satellita): It-temperatura tal-wiċċ tal-baħar hija t-temperatura tal-ilma qrib wiċċ il-baħar. L-SST huwa prodott standard minn sensuri infraħomor termali bbażati fuq is-satellita, u sensuri ottiċi kumplimentati b’faxex infra-aħmar. Temperatura media mensile della superficie del mare (in grado C a risoluzione di 4 km) derivata dal sensore PATHFINDER (Satellite telerilevamento dati del colore dell'oceano): La temperatura superficiale del mare è la temperatura dell'acqua vicino alla superficie del mare. SST è un prodotto standard da sensori termici a infrarossi satellitari e sensori ottici integrati con bande a infrarossi. Monatliche mittlere Meeresoberflächentemperatur (in Grad-C bei 4 km Auflösung), abgeleitet vom PATHFINDER-Sensor (Satellitenfernerkundung Ozeanfarbdaten): Meeresoberflächentemperatur ist die Temperatur des Wassers in der Nähe der Meeresoberfläche. SST ist ein Standardprodukt von satellitengestützten thermischen Infrarotsensoren und optischen Sensoren, die durch Infrarotbänder ergänzt werden.

The datasets contain hourly capacity factors for a PV plant with 394 kWp capacity and a windpower facility with 800 kWp capacity. The demand profile provided contains houlrly consumption data (kWh) of Auroville. All these datasets have been used to generate the results that can be found in D6.1 and D6.2.

Αυτή η μελέτη διερευνά την άμεση σχέση μεταξύ ενός δείκτη βιομάζας μεσοζοζοπλαγκτού, που προέρχεται από την έρευνα Continuous Plankton Recorder και των δορυφορικών μετωπικών χαρακτηριστικών παραγωγικότητας στον Βόρειο Ατλαντικό. Η ποιότητα του ημερήσιου ενδιαιτήματος σίτισης για τα συνηθέστερα είδη μεσοζοπλαγκτού σχετίζεται με την οριζόντια χλωροφύλλη — μια κλίση που προέρχεται από δορυφορικούς αισθητήρες χρώματος των ωκεανών. ΠΕΡΙΣΣΟΤΕΡΕΣ ΠΛΗΡΟΦΟΡΙΕΣ: https://fishreg.jrc.ec.europa.eu/fish-habitat, δημοσίευση που αξιολογήθηκε από ομοτίμους: https://www.nature.com/articles/s41598-019-41212-2 Diese Studie untersucht den direkten Zusammenhang zwischen einem Index von Mesozooplankton-Biomasse, abgeleitet aus der Continuous Plankton Recorder-Vermessung und satellitengestützten Produktivitätsfrontmerkmalen im Nordatlantik. Die Qualität des täglichen Futterlebensraums für die häufigsten Arten von Mesozooplankton hängt mit dem horizontalen Chlorophyll zusammen – einem Gradienten, der von Satellitensensoren mit Meeresfarbe abgeleitet wird. Weitere Informationen: https://fishreg.jrc.ec.europa.eu/fish-habitat, Peer-reviewed Publikation: https://www.nature.com/articles/s41598-019-41212-2 Dan l-istudju jinvestiga l-assoċjazzjoni diretta bejn indiċi tal-bijomassa mesozooplankton, derivat mill-istħarriġ Plankton Recorder Kontinwu u karatteristiċi frontali tal-produttività derivati mis-satellita fl-Atlantiku tat-Tramuntana. Il-kwalità tal-ħabitat tal-għalf ta’ kuljum għall-ispeċijiet l-aktar komuni ta’ mesozooplankton hija relatata mal-gradjent orizzontali tal-klorofilla-a derivat minn sensuri satellitari ta’ kulur oċeaniku. Għal aktar tagħrif: https://fishreg.jrc.ec.europa.eu/fish-habitat, pubblikazzjoni riveduta mill-pari: https://www.nature.com/articles/s41598-019-41212-2 Cette étude étudie l’association directe entre un indice de biomasse mésozoooplancton, dérivé de l’étude Continuous Plancton Recorder et des caractéristiques frontales de productivité dérivées des satellites dans l’Atlantique Nord. La qualité de l’habitat d’alimentation quotidienne des espèces les plus courantes de mésozooplancton est liée à la chlorophylle horizontale, un gradient dérivé de capteurs satellites de couleur océanique. Plus d’informations: https://fishreg.jrc.ec.europa.eu/fish-habitat, publication révisée par les pairs: https://www.nature.com/articles/s41598-019-41212-2 Imscrúdaíonn an staidéar seo an comhlachas díreach idir innéacs bithmhaise mesozooplankton, a dhíorthaítear ón suirbhé Leanúnach Plankton Recorder agus gnéithe tosaigh táirgiúlachta a dhíorthaítear ó shatailítí san Atlantach Thuaidh. Baineann cáilíocht na gnáthóige beathaithe laethúla do na speicis is coitianta de mhosozooplankton leis an ngrádán cothrománach a dhíorthaítear ó bhraiteoirí satailíte de dhath na n-aigéan. Tuilleadh eolais: https://fishreg.jrc.ec.europa.eu/fish-habitat, foilseachán a ndearnadh athbhreithniú piaraí air: https://www.nature.com/articles/s41598-019-41212-2 Este estudio investiga la asociación directa entre un índice de biomasa mesozooplancton, derivado de la encuesta Continuous Plankton Recorder y características frontales de productividad derivadas de satélites en el Atlántico Norte. La calidad del hábitat de alimentación diaria para las especies más comunes de mesozooplancton está relacionada con la clorofila horizontal, un gradiente derivado de sensores satelitales de color oceánico. Más información: https://fishreg.jrc.ec.europa.eu/fish-habitat, publicación revisada por pares: https://www.nature.com/articles/s41598-019-41212-2 Questo studio indaga l'associazione diretta tra un indice di biomassa mesozooplancton, derivato dall'indagine Continuous Plankton Recorder e le caratteristiche frontali di produttività derivate dai satelliti nel Nord Atlantico. La qualità dell'habitat alimentare giornaliero per le specie più comuni di mesozooplancton è correlata alla clorofilla orizzontale, un gradiente derivato dai sensori satellitari di colore dell'oceano. Per maggiori informazioni: https://fishreg.jrc.ec.europa.eu/fish-habitat, pubblicazione peer-reviewed: https://www.nature.com/articles/s41598-019-41212-2 Acest studiu investighează asocierea directă dintre un indice al biomasei mezozooplanctonice, derivat din studiul Continuous Plankton Recorder și caracteristicile frontale de productivitate derivate din satelit în Atlanticul de Nord. Calitatea habitatului zilnic de hrănire pentru cele mai comune specii de mezozooplancton este legată de clorofila orizontală – un gradient derivat din senzorii sateliți de culoare oceanică. Mai multe informații: https://fishreg.jrc.ec.europa.eu/fish-habitat, publicație revizuită inter pares: https://www.nature.com/articles/s41598-019-41212-2 Deze studie onderzoekt de directe associatie tussen een index van mesozooplankton biomassa, afgeleid van de Continuous Plankton Recorder survey en satelliet-afgeleide productiviteit frontale kenmerken in de Noord-Atlantische Oceaan. De kwaliteit van de dagelijkse voeding habitat voor de meest voorkomende soorten mesozooplankton is gerelateerd aan de horizontale chlorofyl-een gradiënt afgeleid van satellietsensoren van oceaankleur. Meer informatie: https://fishreg.jrc.ec.europa.eu/fish-habitat, Peer-reviewed publicatie: https://www.nature.com/articles/s41598-019-41212-2 Este estudo investiga a associação direta entre um índice de biomassa mesozooplâncton, derivado do estudo Continuous Plankton Recorder e características frontais de produtividade derivadas por satélite no Atlântico Norte. A qualidade do habitat alimentar diário para as espécies mais comuns de mesozooplâncton está relacionada com o gradiente horizontal de clorofila-um derivado de sensores de satélite de cor oceânica. Mais informações: https://fishreg.jrc.ec.europa.eu/fish-habitat, publicação revista pelos pares: https://www.nature.com/articles/s41598-019-41212-2

# Data on: Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China [https://doi.org/10.5061/dryad.vdncjsz1z](https://doi.org/10.5061/dryad.vdncjsz1z) This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. ## Description of the data and file structure The predicted annual incidence of national SFTS cases with or without human population reduction under four RCPs under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The value represents the annual incidence, and the unit is 105/year. The Dataset-1 file includes the predicted annual incidence of national SFTS cases with a fixed future human population under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The Dataset-2 file includes the predicted annual incidence of national SFTS cases in the 2030s, 2050s, and 2080s with human population reduction (SSP2) under four RCPs. ## Sharing/Access information Data was derived from the following sources: * https://doi.org/10.1111/gcb.16969 This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. The SFTS incidence in three time periods (2030-2039, 2050-2059, 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. The projected spatiotemporal dynamics of SFTS will be heterogeneous across provinces. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. These findings highlight the need for population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas.  See the Materials and methods section in the original paper. The code used in the statistical analyses are present in the paper and/or the Supplementary Materials.