Find research datasets worth reusing

Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions

Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.

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.

Do food distribution and competitor density affect agonistic behaviour within and between clans in a high fission-fusion species?

Socioecological theory attributes social variation in female-bonded species to differences in within- and between-group competition, shaped by food distribution. Strong between-group contests are expected over large, monopolisable resources, but not when low-quality food is distributed across large, undefended home ranges. Within-group contests are expected to be more frequent with increasing heterogeneity in feeding sites. We tested these predictions in female Asian elephants, which show traits associated with infrequent contests – predominant graminivory, overlapping home ranges, and high fission-fusion. We examined how agonistic interactions within and between female elephant clans (social groupings) vary with food distribution and competitor density. We found stronger between-clan contests than that known from neighbouring forests and more frequent agonism between females between clans than within clans. Such strong between-clan contest is attributable to food patchiness as the Kabini grassland in the study area had three times the grass biomass as adjacent forests. Within-clan agonism was also frequent but was not influenced by food distribution, contradicting socioecological predictions. Contrary to recent claims, increasing within-clan agonism with group (party) size showed that ecological constraints operate despite high fission-fusion in Asian elephants. Thus, despite graminivory and fission-fusion, within-clan and between-clan agonism can be frequent, especially at high population density.

opencc-by-4.0Jan 2024View details →

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-4.0Aug 2023View details →

TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN Distributed temperature sensing, fluxes from eddy covariance measurements, and auxiliary measurements from and at the i-Box station (VF-0) Kolsass

Introduction During the TEAMx-precampaign (TEAMx-PC22) in summer 2022, the Innsbruck Box (i-Box) station at the valley floor in Kolsass (CS-VF0) was extended by a vertical array with fiber-optic distributed temperature sensing (DTS). The i-Box is a testbed for studying boundary layer processes in highly complex terrain (<a href="http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00246.1">Rotach et al. (2017)</a> and <a href="https://fileshare.uibk.ac.at/f/9f1101851849439483de/">i-Box WIKI</a> for further information). The mountain boundary layer is investigated using a 17 m high tower with multi-level observations of turbulence, wind speed, and temperature. DTS measurements  with a spatio-temporal resolution of 0.127 m and 1 s were added  to these profile measurements. The combination of DTS measurements and point observations has the capability of resolving sub-meso scale motions (<a href="https://doi.org/10.1007/s10546-021-00618-0">Pfister et al. 2021</a>) and can reveal processes within the boundary layer during the morning and evening transition (<a href="https://doi.org/10.1029/2020GL092238">Fritz et al. 2021</a>). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://doi.org/10.15203/99106-003-1">Serafin et al. (2020)</a> and in <a href="https://doi.org/10.1175/bams-d-21-0232.1">Rotach et al. (2022)</a>. DATA SET DESCRIPTION 1. Location The i-Box valley-floor site is located on the almost flat floor near the town of Kolsass within the Inn Valley roughly 20 km east-north-east of Innsbruck. The site is characterized by different types of agricultural land. The 17-m high tower is a full energy-balance station and is instrumented with three vertical levels of turbulence measurements. The exact location is 47.305341°N, 11.62219°E (UTM: 698215.03 E, 5242420.95 N) at 545 m above mean sea level. 2. Temporal coverage The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. The i-Box station is running continuously, however, the provided data only covers the period when DTS data is available. The DTS array was running during the following periods: <ul> <li>08.06.-14.06.2022</li> <li>28.06.-18.07.2022</li> </ul> 3. Instrument details Distributed temperature sensing For spatially continuous measurements of vertical temperature profiles at this tower, a DTS array was installed. Temperatures were measured with two channels  at 1~Hz with a spatial resolution of 0.127~m.  The used DTS instrument was an Ultima-HS (Silixa Ltd., Hertfordshire, UK) which was combined with a fibre-optic cable  (900 µm outer diameter; AFL Telecommunications, Spartanburg, SC, USA) consisting of a bend-optimised optical fiber (125 µm with 50 µm core), buffered with Kevlar in a white plastic jacket. The fiber-optic cable was installed vertically towards the west of the tower such that two temperature profiles could be measured simultaneously. For the full array the approximately 450 m long fiber-optic cable was running from one DTS channel through a warm and cold reference bath towards the tower, then up and down the 17-m tower, and back through the baths towards the second channel. Accordingly, the array could be measured in both directions. For mounting at the top and bottom of the tower PVC pipes (diameter 15 cm) were used. The setup with two channels allows for sampling the array in both directions. Before entering the reference baths roughly 200 m were left on the spool slightly affecting signal-to-noise ratio. The vertical array was mapped by cooling packs. Both reference baths observed at the beginning and end of each fiber-optic cable yielded to four reference sections at two temperatures. The array was a double-ended configuration observed as two single-ended configurations which is different from the manufacturer's  provided double-ended mode  (<a href="https://doi.org/10.3390/s20082235">des Tombe et al. 2020</a>, <a href="https://doi.org/10.5194/essd-14-885-2022">Lapo et al. 2022</a>). Reference temperature probes were PT100 from the Ultmia-HS itself. DTS data was calibrated using the weighted-least squares approach described in <a href="https://doi.org/10.3390/s20082235">des Tombe et al. (2020)</a> and implemented in the dtscalibration software package (<a href="https://doi.org/10.5281/zenodo.7111585">des Tombe et al. 2022</a>) and all processing was completed using the pyfocs software package (<a href="https://doi.org/10.5281/zenodo.7111585">Lapo and Freundorfer 2020</a>) . As the fiber-optic cable runs through both calibration baths before and after the array creating four locations within a temperature controlled environment. Of those locations three are used for calibration (every time step) and the fourth is used for validation. A schematic of the setup is given within the files. After calibration a mean bias of -0.05 K and root mean squared difference of 0.22 K was determined with the validation water bath. Unfortunately the mounting towards the west created an artifact as the tower was partially shading the fiber-optic cable creating unphysical temperature gradients. Accordingly, data from 04.00 - 09.00 UTC should not be used for data analysis. The given data is  only a single fiber of the paired vertical sections on the 17-m tower. Artifacts from the plastic ring holders are removed from the fiber. The DTS experiment was named the Innsbruck DTS Experiment (InnDEX22), hence, data names were chosen accordingly. But keep in mind that InnDEX22 was part of TEAMx-PC22. i-Box tower Full site description and all data exceeding the DTS observations can be found on <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a>. Utilized and uploaded data are mainly within three categories: eddy covariance (EC) fluxes at level 1 (4 m agl) and at level 2 (8.7 m agl) and low-frequency data: <ul> <li>EC flux level 1: Combination of ultrasonic anemometer CSAT3 (orientation from North: 30°) and infrared gas analyzer EC150 from Campbell Scientific</li> <li>EC flux level 2: Ultrasonic anemometer CSAT3 (orientation from North: 30°) from Campbell Scientific</li> <li>low-frequency data: <ul> <li>pressure: Setra 278 (Setra Systems, Inc., Boxborough, Maine, USA) at 1.4 m agl</li> <li>radiation: ventilated CGR4 pyrgeometers and CMP21 pyranometers (Kipp & Zonen, Delft, Netherlands) at 2 m agl</li> <li>temperature profile: Rotronic HC2-S3 actively ventilated at 2, 4, 8.7, and 16.9m</li> <li>wind profile: 2D ultrasonic anemometer Gill Windsonic4 at 2, 4, 6, and 12m</li> </ul> </li> </ul> For the EC processing further quality criteria can be applied to assure good data quality. More information on processing of data and quality criteria is given here: <ul> <li>EC fluxes processing: Averaging interval of 30 min performed by the software <a href="https://www.geos.ed.ac.uk/homes/jbm/micromet/EdiRe/">EdiRe</a> Processing includes despiking; double-rotation of the wind components; detrending with a recursive filter and a time constant of 200 s; and applying frequency-response corrections, heat-flux corrections for humidity effects, oxygen corrections for KH20, and WPL corrections. The datafile contains several quality flags and added as description within the netcdf files; zero-plane displacement height of 0 m</li> <li>EC flux Quality Criteria (QC) flags: <ul> <li>-1: all data</li> <li>0: excluding instrument malfunction</li> <li>1: additionally skewness within range (-2 to 2) and kurtosis <8  following Vickers and Mahrt 1997</li> <li>2: additionally exclude non-stationary data</li> </ul> </li> <li>EC flux Flags: <ul> <li>0: data ok</li> <li>1: data not ok (see description of individual flag for further details)</li> </ul> </li> </ul> 4. Data file structure Zip folders Different data sets were generated, as measurements had different temporal resolutions or different sets of parameter. Accordingly the following data is given: <ul> <li>DTS data (1 s): InnDEX22_distributed_temperature_sensing.zip</li> <li>EC flux level 1 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>EC flux level 2 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>Low-frequency data (1 min): InnDEX22_low_frequency_data.zip</li> </ul> File format Each above mentioned folder is filled with netcdf files. One for each day. Global information contains location, instrument type etc. Parameter description is given as attributes for each parameter. 6. Contact Contact lena.pfister(at)uibk.ac.at for any questions regarding the data set. Acknowledgements Special thanks to the "Institut für Meteorologie und Klimaforschung Atmosphärische Umweltforschung" (IMK-IFU), KIT-Campus Alpin, Garmisch-Partenkirchen, for lending us the DTS measurement device for TEAMx-PC22. 7. References Fritz, A. M., Lapo, K., Freundorfer, A., Linhardt, T., & Thomas, C. K. (2021): Revealing the morning transition in the mountain boundary layer using fiber-optic distributed temperature sensing. Geophysical Research Letters, 48, e2020GL092238. <a href="https://doi.org/10.1029/2020GL092238">https://doi.org/10.1029/2020GL092238</a> des Tombe, B., Schilperoort, B., Bakker, M. (2020): Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. Sensors, 20, 2235. <a href="https://doi.org/10.3390/s20082235">https://doi.org/10.3390/s20082235</a> des Tombe, Bas François, & Schilperoort, Bart. (2022): Dtscalibration Python package for calibrating distributed temperature sensing measurements (v1.1.2). Zenodo. <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a> Pfister, L., Lapo, K., Mahrt, L., Thomas, C.K. (2021): Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics. Boundary-Layer Meteorol 180, 187–202. <a href="https://doi.org/10.1007/s10546-021-00618-0">https://doi.org/10.1007/s10546-021-00618-0</a> Lapo, K., Freundorfer, A., (2020): klapo/pyfocs v0.5: Fully-functional python package intended for atmospheric deployments of distributed temperature sensing. Zenodo, <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a> Lapo, K., Freundorfer, A., Fritz, A., Schneider, J., Olesch, J., Babel, W., and Thomas, C. K. (2022): The Large eddy Observatory, Voitsumra Experiment 2019 (LOVE19) with high-resolution, spatially distributed observations of air temperature, wind speed, and wind direction from fiber-optic distributed sensing, towers, and ground-based remote sensing, Earth Syst. Sci. Data, 14, 885–906 <a href="https://doi.org/10.5194/essd-14-885-2022">https://doi.org/10.5194/essd-14-885-2022</a> Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubi&scaron;ić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, (2020): Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a> Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubi&scaron;ic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J.  Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, (2022): A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. Bulletin of the American Meteorological Society, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a>

opencc-by-4.0Jan 2024View details →

Data for sensitivity analysis of Hübler, M., M. Wiese, M. Braun and J. Damster (2023): The distributional effects of CO2 pricing at home and at the border on German income groups

This dataset contains the output files used in the sensitivity analysis of the computable general equilibrium (CGE) model developed in Hübler et al. (2023). Each folder is labeled with the respective set of sector-level elasticity of substitution parameters considered in the analysis: elasticities between domestically produced versus imported goods (esubd), Armington elasticities (esubm) and elasticities between production factors (esubva). For each set of parameters, we generate 1000 random draws from a +-10 % interval around each of the sector-specific elasticities, resulting in 1000 sets of sectoral parameter values. Each .xlsx output file located in a dedicated subfolder corresponds to a model run with a specific set of parameter values. In addition, we conduct sensitivity analyses of two individual parameters, namely the CO2 target (CO2factor) considered in our policy scenarios and the elasticity of substitution in consumption (esub_cons). The sensitivity analysis is carried out using the <a href="https://snakemake.readthedocs.io/en/stable/">Snakeflow</a> workflow management system, and R code for generating parameter spaces and processing the output files is available on <a href="https://github.com/mariuslbraun/climate-trade-distribution-sensitivity">GitHub</a>.

opencc-by-4.0Dec 2017View details →

Fig. 1-2 in Distributional Notes On Some Nosodendridae (Coleoptera) - Xvi. New Faunistics Records From The Philippines

Fig. 1-2. Nosodendron (Dendrodipnis) grande (Reitter, 1881): 1- habitus dorsal aspect; 2- habitus lateral aspect.

opencc-by-4.0Dec 2014View details →

Fig. 2 in Daphnia Cucullata Sars, 1862 (Crustacea: Cladocera) Distribution And Location In Composition Of Zooplankton Cenosis In Lake Dridzis

Fig. 2. Redundancy analysis (RDA) ordination plot for zooplankton abundance from Lake Dridzis during the sampling period of May to September 2011. Abbreviations: ORP- Oxidation-reduction potential; NTU- Turbidity.

opencc-by-4.0Dec 2014View details →

Fig. 1 in Daphnia Cucullata Sars, 1862 (Crustacea: Cladocera) Distribution And Location In Composition Of Zooplankton Cenosis In Lake Dridzis

Fig. 1. Redundancy analysis (RDA) ordination plot for zooplankton abundance from Lake Dridzis during the sampling period of May to September 2010. Abbreviations: ORP- Oxidation-reduction potential; NTU- Turbidity.

Data for: Nonrandom foraging and resource distributions

Nonrandom foraging can cause animals to aggregate in resource-dense areas, increasing host density, contact rates, and pathogen transmission, but when should nonrandom foraging and resource distributions also have density-independent effects? Here, we used a factorial experiment with constant resource and host densities to quantify host contact rates across seven resource distributions. We also used an agent-based model to compare pathogen transmission when host movement was based on random foraging, optimal foraging, or something between those states. Nonrandom foraging strongly depressed contact rates and transmission relative to the classic random movement assumptions used in most epidemiological models. Given nonrandom foraging in the ABM and experiment, contact rates and transmission increased with resource aggregation and average distance to resource patches due to increased host movement in search of resources. Overall, we describe three density-independent mechanisms by which host behavior and resource distributions alter contact rate functions and pathogen transmission.

opencc-by-4.0Apr 2023View details →

Figigure 1. Phyla nodiflora var. nodiflora. A - B in Increasing the knowledge on the distribution of Phyla (Verbenaceae): a new record for the state of Santa Catarina, Southern Brazil

Figigure 1. Phyla nodiflora var. nodiflora. A - B. Habitat and habit; C. Leaves; D - F. Florescences; G. Map showing the new record (red circle) for the state of Santa Catarina, Brazil (Wegener & Garcia 138 - LAG). Acronyms (map): PR = Paraná; SC = Santa Catarina; RS = Rio Grande do Sul.

opencc-zeroFeb 2024View details →

Geographic distribution change and climatic niche change of Odonates in Great Britain

Species are largely thought to maintain broadly static niches over time, an assumption underpinning much theoretical ecology including the implementation of ecological models to project species' current and future distributions. Here, we assess niche conservatism in odonates in Great Britain over the past six decades by simultaneously quantifying changes in species geographic distribution and evaluating temporal trends in species realised climatic niche.

opencc-by-4.0Oct 2023View details →

Data and code associated with "Evaluating the definition and distribution of spring ephemeral wildflowers in eastern North America"

Data and code associated with a paper by Yancy et al titled "Evaluating the definition and distribution of spring ephemeral wildflowers in eastern North America". Metadata is included in files when possible.

opencc-zeroFeb 2024View details →

Data and R code used in: Plant geographic distribution influences chemical defenses in native and introduced Plantago lanceolata populations

Plants growing outside their native range may be confronted by new regimes of herbivory, but how this affects plant chemical defense profiles has rarely been studied. Using Plantago lanceolata as a model species, we investigated whether introduced populations show significant differences from native populations in several growth and chemical defense traits. Plantago lanceolata (ribwort plantain) is an herbaceous plant species native to Europe and Western Asia that has been introduced to numerous countries worldwide. We sampled seeds from nine native and ten introduced populations that covered a broad geographic and environmental range and performed a common garden experiment in a greenhouse, in which we infested half of the plants in each population with caterpillars of the generalist herbivore Spodoptera littoralis. We then measured size-related and resource-allocation traits as well as the levels of constitutive and induced chemical defense compounds in roots and shoots of P. lanceolata. When we considered the environmental characteristics of the site of origin, our results revealed that populations from introduced ranges were characterized by an increase of chemical defense compounds without compromising plant biomass. The concentrations of iridoid glycosides and verbascoside, the major anti-herbivore defense compounds of P. lanceolata, were higher in introduced populations than in native populations. In addition, introduced populations exhibited greater rates of herbivore-induced volatile organic compound emission and diversity, and similar chemical diversity based on untargeted analyses of leaf methanol extracts. In general, the geographic origin of the populations had a significant influence on morphological and chemical plant traits, suggesting that P. lanceolata populations are not only adapted to different environments in their native range but also in their introduced range.

opencc-by-4.0Dec 2012View details →

Fig. 6 in Effect of abiotic variables on fish eggs and larvae distribution in headwaters of Cuiabá River, Mato Grosso State, Brazil

Fig. 6. Temporal (a) and spatial (b) frequency of occurrence of the seven most abundant taxa of fish larvae captured in the headwaters of the Cuiabá River between November 2007 and March 2008.

opencc-by-4.0Dec 2012View details →

Fig. 2 in Effect of abiotic variables on fish eggs and larvae distribution in headwaters of Cuiabá River, Mato Grosso State, Brazil

Fig. 2. Temporal (a) and spatial (b) distribution of density (individuals/10m3) of fish eggs and larvae captured in the headwaters of the Cuiabá River, in all the collection sites, between November 2007 and March 2008.

opencc-by-4.0Feb 2024View details →

Supplementary material from: Prediction of the Cold Flow Properties of Biodiesel using the FAME Distribution and Machine Learning Techniques

The dataset is divided into three sections within the worksheet.  The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.  The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs. The third and final section describes the measured properties Cloud Point (CP), Cold Filter Plugging Point (CFPP) and Pour Point (PP).

opencc-zeroFeb 2024View details →

Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions

Understanding geographic ranges and species distributions is crucial for effective conservation, especially in the light of climate and land use change. However, the spatial, temporal and intraspecific resolution of digital accessible information on species distributions is often limited. Here, we suggest to make better use of high-resolution tracking data to address existing limitations of occurrence records such as spatial biases (e.g. lack of observations in parts of the geographic range), temporal biases (e.g. lack of observations during a certain period of the year), and insufficient information on intraspecific variability (e.g. lack of population- or individual-level variation). Addressing these gaps can improve our knowledge on geographic ranges, intra-annual changes in species distributions, and population-level differences in habitat and space use. We demonstrate this with tracking data and species distribution models (SDMs) of the Barnacle Goose, a migratory bird species wintering in western Europe and breeding in the Arctic. Our analyses show that tracking data can (1) supplement occurrence records from the Global Biodiversity Information Facility (GBIF) in remote areas such as the European and Russian Arctic, (2) improve information on the temporal use of wintering, staging and breeding areas of migratory species, and (3) provide insights into the differences of population-level responses to environmental variables. We recommend a broader use of tracking data to address the Wallacean shortfall (i.e. the incomplete knowledge on the geographic distribution of species) and to improve forecasts of biodiversity responses to climate and land use change (e.g. species vulnerability assessments). To avoid common pitfalls, we provide six recommendations for consideration during the research cycle when using tracking data in species distribution modelling, including steps to assess biases and integrate information on intraspecific variability in modelling approaches.

opencc-zeroMar 2024View details →

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.