Climatologies are characterized by strong spatial gradients and a large number of weather stations is necessary to capture them, even if available for a short period. Consequently, an interpolation technique that exploits the dependence of climate normals on orography and other geographic parameters is necessary to reconstruct such climatologies 35 , 36 , Anomalies, on the other hand, are linked to climate change and variability; for this reason, they are usually characterized by higher spatial coherence A limited number of weather stations can therefore be sufficient to capture spatial patterns through simpler interpolation methods, but it is pivotal to use long-term time series to have a satisfactory temporal coverage over the past.
These series must in turn be corrected for errors deriving from the history of the stations changes of station and instrument location, instrument replacements, changes in observation protocols, etc. For each record, we reconstructed the climate information of the Voronoi cell centre using average cell elevation following Brunetti et al. For each centre we first estimated the climate normals calculating a weighted linear regression of the data from nearby stations as a function of elevation.
Anomalies were interpolated at the same locations exploiting an improved version of the dataset presented in Brunetti et al. Bioclimatic variables were finally calculated on monthly temperature and precipitation using the dismo R package All the above-mentioned analyses were run using custom R and Fortran codes. The final dataset includes , occurrence records deriving from 8, binomials and 16, localities, dating between and CE Fig. Temporal distribution of the records from ClimCKmap.
Total number of records: , from 8, binomials and 16, localities. Temporal coverage: — CE. The numerals above each bar indicate the percentage of records falling in that time period. The starting dataset CKmap 5. After taxonomic simplification and cleaning, , records from 10, binomials i.
The subsequent geographic cleaning and georeferencing phase allowed assigning coordinates to , records from 19, localities. The removal of non- or dubiously-dated records restricted the size of the resulting dataset to , records. The removal of duplicates led to a final dataset including , distribution records deriving from 8, binomials and 16, localities, dating between and CE Fig. The georeferencing methodology returned satisfactory results in term of precision Fig.
Climate reconstructions did not allow assigning climate data to only a small fraction of the total dataset records dating between and CE; 0.
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Monthly means of minimum and maximum temperature and total precipitation were thus reconstructed for the remaining part of the records , records dating between e CE. Distance between sample point and the farthest vertex of the Voronoi cell precision. The numerals above each bar indicate the percentage of records falling in that distance class. In the last two centuries, biodiversity has been facing abrupt climate and environmental changes globally, mainly due to human activities.
Huge efforts have been spent to expand our knowledge of species distribution worldwide, but the picture is still far from complete.
Vallo della Lucania - Angellara
In fact, apart from rare exceptions 43 , low taxonomic coverage and temporal gaps still limit our understanding of ecosystem functioning under climate change and biodiversity loss. The dataset we compiled and presented here aimed at moving a step forward to fill this gap, by providing spatially, temporally and climatically accurate distribution information for the fauna of one of the most biodiverse countries in Europe.
ClimCKmap is currently the largest spatially, temporally and climatically explicit distribution database ever assembled for the Italian fauna. Future research should consider exploiting the database, for example deriving empirical relationships between climate change and biodiversity loss and using them to evaluate impacts of future climate change scenarios, as well as to identify climate-biodiversity change hotspots.
Proper use of the data will also allow for developing knowledge-based biodiversity conservation measures. This database has of course some potential issues that have to be taken into account when working with it. First, we opted for retaining the original classification and taxonomy, i. Additionally, some authors reported biases due to spatially unequal sampling effort 45 , 46 , as well as temporal and taxon-specific biases in the completeness of the database Hortal, J. Seven shortfalls that beset large-scale knowledge of biodiversity.
Yang, W. Geographical sampling bias in a large distributional database and its effects on species richness—environment models. Reddy, S. Geographical sampling bias and its implications for conservation priorities in Africa. Ficetola, G. Estimating patterns of reptile biodiversity in remote regions. Franklin, J. Big data for forecasting the impacts of global change on plant communities. Global Ecol. Colwell, R. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages.
Engemann, K. Belbin, L. ZooKeys , 67—76 Wiser, S.
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Achievements and challenges in the integration, reuse and synthesis of vegetation plot data. Guralnick, R. PLoS Biol. Gratton, P. A world of sequences: can we use georeferenced nucleotide databases for a robust automated phylogeography? Lavoie, C.
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The role of natural history collections in documenting species declines. Lenoir, J. A significant upward shift in plant species optimum elevation during the 20th century. Science , — Tingley, M. Detecting range shifts from historical species occurrences: new perspectives on old data.
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