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877 results for “distribution modelling”

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zenodo44/100

Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries

Panzeri D.1, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović &Scaron;ifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S2. 1 dpanzeri@ogs.it 2 slibralato@ogs.it Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures. Data (csv) for Panzeri et al. 2023 1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values  (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a>  2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a> 3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a>

opencc-by-4.0Sep 2023View details →

edi44/100

Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes

Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.

openCC (other)Feb 2022View details →

edi44/100

Distribution models of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019)

Long-term ecological field surveys from the McMurdo Dry Valleys Long Term Ecological Research program (MCM LTER) have documented the abundance and diversity of microbial mat types across ephemeral glacial meltwater streams in the McMurdo Dry Valleys region of Antarctica. However, field surveys are limited and are incapable of being performed across the entirety of streams within a field season. Therefore, we used remote sensing to examine the distribution of these diverse communities across streams in order to determine whether large scale distribution patterns are similar to those the MCM LTER has already thoroughly studied in situ. As part of our 2018-2019 field campaign, we established two to three 20 x 20 m plots within five different streams (Bowles Creek, McKnight Creek, a relict channel, Canada Stream, and Crescent Stream) in the Fryxell Basin of Taylor Valley. We performed point transect and quadrat field surveys of microbial mat and moss cover within each 20 x 20 m plot. We then used hyperspectral measurements of mat and moss collected in the field, previously archived in “Spectral and biological characteristics of microbial mats and mosses across Fryxell Basin, Taylor Valley, Antarctica (2018-2019),” in linear spectral mixing models to determine mat and moss coverage in the same 20 x 20 m plots within an atmospherically corrected WorldView-2 satellite image from Dec. 12, 2018. We ground truthed our modeled mat and moss abundances with our field survey coverages and determined the limitations of our methods. We then modeled mat and moss coverage across Huey Creek and Von Guerard Stream to apply our methods to streams without ground truthing measurements. Our results demonstrate the spatial distribution of moss and black, orange, red, and green microbial mat across Fryxell Basin streams. Observations of mat and moss coverage at the basin-wide scale are similar to those seen in localized stream areas.

openCC (other)Sep 2023View details →

zenodo40/100

DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats

Inferences of the interactions between species’ ecological niches and spatial distribution have been historically based on simple metrics such as low-resolution dietary breadth and range size, which might have impeded the identification of meaningful links between niche features and spatial patterns. We analysed the relationship between dietary niche breadth and spatial distribution features of European bats, by combining continent-wide DNA metabarcoding of faecal samples with species distribution modelling. Our results show that while range size is not correlated with dietary features of bats, the homogeneity of the spatial distribution of species exhibits a strong correlation with dietary breadth. We also found that dietary breadth is correlated with bats’ hunting flexibility. However, these two patterns only stand when the phylogenetic relations between prey are accounted for when measuring dietary breadth. Our results suggest that the capacity to exploit different prey types enables species to thrive in more distinct environments and therefore exhibit more homogeneous distributions within their ranges.

opencc-by-4.0Jan 2020View details →

zenodo40/100

Model outputs for occurrence and hunting data‐based models of wild boar distribution and abundance, July 2019 update

These maps  are wild boar habitat suitability outputs based on newly available data of wild boar, and models for predicting wild boar relative abundance using hunting yields. Objectives: - Validation of previously produced hunting yield maps and new ones - Downscaling to 10x10 km grid - Downscaling to 2x2 km grid Model settings and predictors:     - Model from ENETWILD report August 2019 - Assuming cells as municipality in 10x10 km grid downscaling - Assuming cells as hunting grounds in 2x2 km grid downscaling     Conclusions guiding future methodological steps - To update wild boar hunting yield data for some specific regions; - To increase hunting yield data resolution; - To explore model independent parametrization for each bioregion. Files: August_2019_HY_nut00_10x10        >> Model outputs based on hunting yield GLM analyses August_2019_occurrences_bioclim   >> Model outputs based on Bioclim analyses August_2019_occurrences_glm         >> Model outputs based on Generalised linear model August_2019_occurrences_ksvm      >> Model outputs based on Support vector Machine analyses August_2019_occurrences_maxent   >> Model outputs based on Maxent analyses August_2019_occurrences_randomForest>> Model outputs based on Random Forest analyses --------------------------------------------------------------------------------------------------------------------------------------------------------- These maps are models obtained in intermediate phases of the ENETWILD project based on available information.  There are frequent updates in order to improve the results. For methodological approach and details check the paper:  ENETWILD‐consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A.  Cohen, R. Soriguer, J. Vicente (2019). ENETwild modelling of wild boar distribution and abundance: update of occurrence and hunting data‐based models. EFSA Supporting Publications, 16(8), 1674E. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2Fabs%2F10.2903%2Fsp.efsa.2019.EN-1674&data=02%7C01%7C%7Ca8ad922eefde42f5cb5208d7c5054851%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637194498792136402&sdata=fqdiYEOqYIlHaDbp5a7kVdGQ6FWuFEydJNhSWOghH%2FQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/abs/10.2903/sp.efsa.2019.EN-1674</a> . Permission for reuse occurrence  outputs records is granted under the terms of a CC-BY-NC license. Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.

opencc-by-4.0Mar 2020View details →

zenodo40/100

Bioclimatic data for species distribution modelling in the Amazon Basin

In this dataset, bioclimatic data regarding the Amazon Basin, in the near of the cities of Manaus and Manacapuru are available. There are 11 environmental data variables, referring to temperature, atmospheric pressure, concentration of pollutants and aerosols, such as carbon monoxide, ozone, carbon dioxide, among others. These were collected by the G-159 Gulfstream aircraft during its two periods of operation (IOP1 and IOP2), available in the GOAmazon (Green Ocean Amazon) project's data repository. A spatial interpolation methodology (linear barycentric interpolation) was applied to each variable, in order to obtain a larger area of data. The species occurrence data were collected from the repositories of the ICMBio (Instituto Chico Mendes de Conservação da Biodiversidade) Portal da Biodiversidade and GBIF (Global Biodiversity Information Facility), referring to the same date and location of the environmental data.

opencc-by-4.0Nov 2020View details →

dryad40/100

Data from: Species distribution models of the Spotted Wing Drosophila (Drosophila suzukii, Diptera: Drosophilidae) in its native and invasive range reveal an ecological niche shift

The Spotted Wing Drosophila (Drosophila suzukii) is native to Southeast Asia. Since its first detection in 2008 in Europe and North America, it has been a pest to the fruit production industry as it feeds and oviposits on ripening fruit. Here we aim to model the potential geographical distribution of D. suzukii. We performed an extensive literature review to map the current records. In total, 517 documented occurrences (96 native and 421 invasive) were identified spanning 52 countries. Next, we constructed three species distribution models (SDMs) based on occurrence records in: 1) the native range (SDMnative), 2) the invasive range in Europe (SDMEurope) and 3) a global model of all records (SDMglobal). The models aimed to investigate, whether this species will be able to occupy additional ecological niches beyond its native range and expand its current geographic distribution both globally and in Europe. The SDMs were generated using Maximum Entropy algorithms (Maxent) based on present occurrence records and bioclimatic variables (WorldClim). Predictions of habitat suitability vary greatly depending on the origins of occurrence records. According to all models, precipitation and low temperatures were key limiting factors for the distribution of D. suzukii, which suggests that this species requires a humid environment with mild winters in order to establish a permanent population in its invasive range. Several regions in the invasive range, not presently occupied by this species, were predicted highly suitable, especially in northern Europe, suggesting that D. suzukii is not occupying its full fundamental niche yet. Synthesis and applications. Based on these models of potential geographic distribution of the Spotted Wing Drosophila (Drosophila suzukii), we show a shift in the ecological niche in D. suzukii populations, emphasizing the importance of using presence and local environmental data. Further investigation regarding new occurrences is recommended to secure optimal pest management. Despite a continuing expansion, many countries still lack proper surveillance schemes, and we urge policymakers to initiate appropriate management programs.

opencc-zeroDec 2017View details →

dryad40/100

Data from: Complementary strengths of spatially-explicit and multi-species distribution models

Species distribution models (SDMs) project the outcome of community assembly processes - dispersal, the abiotic environment, and biotic interactions - onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock (Tsuga canadensisL.) and five additional co-occurring overstory tree species in 35,569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability. Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species-environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.

opencc-zeroDec 2019View details →

zenodo40/100

Code and supplementary plots for "Flexible distributed lag models for count data using mgcv"

R code and supplementary plots accompanying the paper: "Flexible distributed lag models for count data using mgcv".

opencc-by-4.0May 2024View details →

dryad40/100

Guanaco distribution modeling in the last 2500 years in Northwest Patagonia

Context: The guanaco is one of the four species of South American camels, and is the largest native mammal inhabiting arid and semi-arid environments in South America. Although guanaco was abundant and widely distributed in the past, currently its density and distribution range are substantially reduced, inhabiting mainly in Argentine Patagonia in small isolated groups. The decline in guanaco populations is most likely related to the Anthropocene defaunation process that is affecting large mammals in developing countries worldwide, but the extent and causes of these changes are not well understood. Aims: Explore both the changes in the distribution of guanaco populations in Northwest Patagonia and the environmental and anthropic factors that shaped the distribution patterns, employing a long-term perspective spanning from the end of the Late Holocene to present times (i.e., last 2500 years). Methods: We combine archaeological information, ethnohistorical records and current observations and apply Species Distribution Models using bioclimatic and anthropic factors as explanatory variables. Key results: Guanaco spatial distribution in Northwest Patagonia changed significantly throughout time. This change consisted in the displacement of the species towards the east of the region and its disappearance from northwest Neuquén and southwest Mendoza in the last 30 years. In particular, the high-density urban settlements and roads, and secondly, competition with ovicaprine livestock (goats and sheep) for forage are the main factors explaining the change in guanaco distribution. Conclusions: Guanaco and human populations co-existed in the same areas during the Late Holocene and historic times, but during the 20th century the modern anthropic impact generated a spatial dissociation between both species, pushing guanaco populations to drier and unproductive areas that were previously peripheral in its distribution. Implications: As with many other large mammal species in developing countries, Northwest Patagonia guanaco populations are undergoing significant changes in their range due to modern anthropic activities. Considering that these events are directly related to population declines and extirpations, together with the striking low density recorded for Northwest Patagonia guanaco populations, urgent management actions are needed to mitigate current human impacts.

opencc-zeroNov 2023View details →

dryad40/100

Code and data for Bayesian joint species distribution model selection for community-level prediction

Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.

opencc-zeroNov 2023View details →

dryad40/100

Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions

Aim Species distribution models (SDMs) that integrate presence-only and presence-absence data offer a promising avenue to improve information on species' geographic distributions. The use of such 'integrated SDMs' on a species range-wide extent has been constrained by the often-limited presence-absence data and by the heterogeneous sampling of the presence-only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map-based evaluation. We build a new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs. Location South and Central America. Time period 1979-2017. Major taxa studied Hummingbirds. Methods We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process. We validate SDMs with two schemes: i) cross-validation with presence-absence data and ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set. Results The integrated SDM accounting for the spatially varying sampling intensity of the presence-only data was one of the top-performing models in both model validation schemes. Presence-only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence-only data for species that had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction. Main conclusions Integrated SDMs combining presence-only and presence-absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence-absence data.

opencc-zeroNov 2023View details →

dryad40/100

Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects

Premise Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO2 exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO2 exchange rates of these poikilohydric organisms change through time as a function of weather conditions. Methods PoiCarb simulates diel fluctuations of CO2 exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO2-exchange responses to light, temperature, atmospheric CO2 concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO2 exchange to such courses from field measurements on the tropical lichen Crocodia aurata. To demonstrate the model's usefulness, we simulated potential climate-change effects. Results Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration. Conclusions Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation.

opencc-zeroJan 2024View details →

zenodo40/100

Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members' active contributions

This dataset was used in the case study of the following publication:  - Luis Gomes, Zita Vale, "Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members’ active contributions," Sustainable Cities and Society, Volume 101, 2024, 105060, ISSN 2210-6707, <a href="https://doi.org/10.1016/j.scs.2023.105060">https://doi.org/10.1016/j.scs.2023.105060</a>  (if you used this dataset in your publications, please send us your information so we can add your publication to the list above)   The dataset is composed by energy generation, consumption, and forecast (for generation, and for consumption) expressed in Wh. The data considers an energy community of 10 prosumers in 30 days. The dataset also has energy prices that have been collected from MIBEL (Iberian Electricity Market).   We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.

opencc-by-4.0Jan 2024View details →

dryad40/100

Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts

Aim: Climate change and habitat loss or degradation are some of the greatest threats that species face today, often resulting in range shifts. Species traits have been discussed as important predictors of range shifts, with the identification of general trends being of great interest for conservation efforts. However, studies reviewing relationships between traits and range shifts have questioned the existence of such generalized trends, due to mixed results and weak correlations, as well as analytical shortcomings. The aim of this study was to test this relationship empirically, using analytical approaches that account for common sources of bias when assessing range trends. Location: Tanzania, East Africa. Time period: 1980-1999 and 2000-2020. Major taxa studied: 57 savannah specialist birds found in Tanzania, belonging to 26 families and 11 orders. Methods: We applied recently developed integrated spatio-temporal species distribution models in R-INLA, combining citizen science and bird atlas data to estimate ranges of species, quantify range shifts, and test the predictive power of traditional trait groups, as well as exposure-related and sensitivity traits. We based our study on 40 years of bird observations in East African savannahs, a biome that has experienced increasing climatic and non-climatic pressures over recent decades. We correlated patterns of change with species traits. Results: We find indications of relationships identified by previous research, but low average explanatory power of traits from an ecological perspective, confirming the lack of meaningful general associations. However, our analysis finds compelling species-specific results. Main conclusions: We highlight the importance of individual assessments, while demonstrating the usefulness of our analytical approach for analyses of range shifts.

opencc-zeroMar 2024View details →

zenodo40/100

Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 in A revision of the genus Armillipora Quate (Diptera: Psychodidae) with the descriptions of two new species

Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 is equal to the highest probability of distribution, while 0 is the lowest probability.

opencc-by-4.0Mar 2024View details →

zenodo40/100

The data used for "Exploring how differences in dust particle size distribution and complex refractive indices affect dust direct radiative fluxes using the CAS-FGOALS-SPRINTARS global climate model"

These data are used for " Exploring how differences in dust particle size distribution (PSD) and complex refractive indices (CRI) affect direct radiative effect (DRE) using the CAS-FGOALS-SPRINTARS global climate model ".  (1)  AS83+OPAC: The control experiment, dust PSD is the original AS83, and the generic CRI is from OPAC.  (2)  BFT22+OPAC: Same as the control experiment, but the PSD is updated to use BFT22. (3)  BFT22+DB: Same as the experiment BFT22+OPAC, but the generic OPAC CRI is replaced by nine regionally dependent DB CRIs. (4)  BFT22+DB strong abs: Same as the experiment BFT22+DB, but the generic CRI consists of 10% percentile real and 90% percentile imaginary parts and no regional dependencies. (5)  BFT22+DB weak abs: Same as the experiment BFT22+DB, but the generic CRI consists of 90% percentile real and 10% percentile imaginary parts and no regional dependencies. All experiments mentioned above are run for 5 years (2010-2014). The annual average simulation results are stored here. Note: AS83 represents the dust PSD scheme from d'Almeida and Schütz. (1983). BFT22 represents the new dust PSD developed by Meng et al. (2022) based on the improved brittle fragmentation theory. OPAC: the Optical Properties for Aerosols and Clouds dataset, DB: the CRIs from Di Biagio et al. (2017, 2019). References d'Almeida, G. A., & Schütz, L. (1983). Number, Mass and Volume Distributions of Mineral Aerosol and Soils of the Sahara. Journal of Applied Meteorology and Climatology, 22(2), 233-243. https://doi.org/https://doi.org/10.1175/1520-0450(1983)022<0233:NMAVDO>2.0.CO;2 Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2019). Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content. Atmospheric Chemistry and Physics, 19(24), 15503-15531. https://doi.org/10.5194/acp-19-15503-2019 Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2017). Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing. Atmospheric Chemistry and Physics, 17(3), 1901-1929. https://doi.org/10.5194/acp-17-1901-2017 Meng, J., Huang, Y., Leung, D. M., Li, L., Adebiyi, A. A., Ryder, C. L., et al. (2022). Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust. Geophysical Research Letters, 49(8), e2021GL097287, https://doi.org/https://doi.org/10.1029/2021GL097287

opencc-by-4.0Dec 2023View details →

dryad40/100

Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling

A necessary component of understanding vector-borne disease risk is the accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data-rich species but may produce inaccurate results for sparsely-documented vectors. In light of global change, vectors that are currently not well-documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a sparsely-documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely-documented species. To demonstrate this approach, we evaluated the ability of twelve models—which differed in their pooling of data from multiple vector species and inclusion of disease data—to improve distribution estimates of sparsely-documented vectors. We assessed our models on two simulated data sets, which allowed us to generalize our results and examine their mechanisms. We found that when the focal species is sparsely documented, incorporating data on related vector species reduces uncertainty and improves accuracy by reducing overfitting. When data on vector species are already incorporated, disease data only marginally improve model performance. However, when data on other vectors are not available, disease data can improve model accuracy and reduce overfitting and uncertainty. We then assessed the approach on empirical data on ticks and tick-borne diseases in Florida and found that incorporating data on other vector species improved model performance. This study illustrates the value of exploiting correlated data via joint modeling to improve distribution models of data-limited species.

opencc-zeroApr 2024View details →

zenodo40/100

Dataset for Integrated Species Distribution Model for pikeperch larvae in the Porvoo-Sipoo archipelago

This record contains the data required to run the code for fitting the Integrated Species Distribution Model described in <a href="https://arxiv.org/abs/2206.08817">arXiv:2206.08817 [stat.ME].</a> <h1>Files in this record</h1> <ul> <li>transect_data.csv Line transect observations from Porvoo-Sipoo archipelago, Finland on June 2017.</li> <li>expert_assessments.tif Rasterized, anonymous expert assessments. Categorical values denoting how likely a given location is to be a spawning location for pikeperch. 4 categories, with smaller values corresponding to higher probabilities.</li> <li>covariate_raster_example.tif Rasterized example environmental covariate values. These are similarly structured as the covariate data used in the study and compatible with the analysis code. However, since we do not have the permission to release the original data set, these values are instead generated based on the projected planar coordinates such that they have roughly similar spatial gradients as the original covariates.</li> </ul> <h1>Detailed descriptions</h1> <h2>Transect data</h2> <h3>Location and replicate identifiers</h3> <ul> <li> id : transect identifier. Replicates of the same transect have the same identifier. </li> <li> id2 : alternate transect identifier, unique for each transect. </li> <li> repeated : whether transect was replicated or not. </li> <li> X_euref : easting coordinate, EUREF_FIN_TM35FIN, for the transect starting location in [meters] </li> <li> Y_euref : northing coordinate, EUREF_FIN_TM35FIN, for the transect starting location in [meters] </li> <li>date : date of the measurement, DD/MM/YYYY</li> <li>week : week number of the measurement date</li> </ul> <h3>In situ measurements</h3> <ul> <li> volume : Transect water volume [m^3]. Transect length (500m) multiplied by sampler surface area. Used as survey effort. </li> <li> heading : compass heading (direction) for the transect, in [degrees]. </li> <li> SumKUHA : total pikeperch (Sander lucioperca, kuha in Finnish) larvae count in each transect [scalar] </li> </ul> <h2>Expert assessments</h2> The raster contains assessments from 10 local experts encoded as separate raster layers (Expert_1, Expert_2, ..., Expert_10). Raster resolution is 50m x 50m and the planar coordinates are based on the same coordinate reference system as the transect observations (UTM zone 35). The assessments are coded as integers with values between 1 and 4, with smaller values corresponding to higher probabilities. <h2>Covariate raster example</h2> This raster has the same spatial dimensions and uses the same coordinate reference system as the expert assessment raster and has three layers, one for each covariate. The covariate values are generated based on the spatial coordinates such that each covariate has similar spatial gradient as the original covariate. The covarites have the same names as in the original covariate data (dptLUKE, dist10m and lined3km). <h1>Creators</h1> Transect data collected and curated by Sanna Kuningas. Original covariate rasters curated by Sanna Kuningas from data sets collected by the Finnish Environment Institute and the Natural Resources Institute Finland. Expert assessments originally digitized and rasterized by Jussi Mäkinen.  Additional refinement to assessment rasters by Karel Kaurila. Preparation for publishing on Zenodo for all of the data sets  by Karel Kaurila. <h2>Change log</h2> <ul> <li> 2025 Jan 31: Included columns date and week for transect_data.csv.</li> </ul>

opencc-by-4.0Nov 2024View details →

dryad40/100

Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management

1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an integrated SDM database (iSDMdb). 2. The iSDMdb is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility, we used the endangered New Zealand sea lion (Phocarctos hookeri). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. 3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the iSDMdb's additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected. 4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The iSDMdb addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an iSDMdb using open-source tools, and how to make diverse, complex assessments more accessible for end-users.

opencc-zeroOct 2021View details →

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