Dataset of Iberian ibex population in Sierra Nevada (Spain)
Observatorio de seguimiento de los efectos del cambio global de Sierra Nevada. Centro Andaluz de Medio Ambiente, Universidad de Granada, Junta de Andalucía.Acronym: iberianibex
This dataset provides long-term information about Iberian ibex (Capra pyrenaica hispanica Schimper, 1848) presence in Sierra Nevada (SE Iberian Peninsula), as a result of annual sampling from 1993 to 2018 done by the managers of the Sierra Nevada Natural and National Park. They carried out the transects collecting different variables such as the number of individuals observed, the perpendicular distance of each group of goats to the transect line and, at an individual level and sex as well as age of individuals in the case of males. These data enabled the calculation of population parameters such as density, sex ratio, birth rate and age structure. These parameters are key for Iberian ibex conservation and management, given that Sierra Nevada harbours the largest population of this species in the Iberian Peninsula. The data set we present is structured using the Darwin Core biological standard, which contains 3,091 events (582 transect walk events and 2,509 group sighting events), 5,396 occurrences, and 2,502 measurements. The occurrences include the sightings of 11,436 individuals (grouped by sex and age) from 1993 to 2018 in a total of 88 transects distributed along Sierra Nevada, of which 33 have been continuously sampled since 2008.
The geographical area where the dataset was obtained corresponds to Sierra Nevada: a mountainous region located in the south-east Iberian Peninsula (37°14'-36°54' N; 2°37'-3°39' W) within the Baetic System, in the called Penibaetic mountain ranges, near to the Mediterranean Sea. Sierra Nevada has the highest summits of the Iberian Peninsula, the peak Mulhacén reaching 3,479 m a.s.l., making this the second-highest mountain range in mainland Europe, after the Alps.
Different validation processes were applied in the data cycle stages described below:
(a) Field data collection in surveys.
During the sampling, the observers fundamentally cross-checked the sightings in situ.
(b) Digitalisation and storage in an Access database.
In this second step, due to the large volume of data, we implement some controls and validation rules in the Access form in order to reduce human errors and facilitate the digitalisation:
- Input masks to control data entry formats (especially date/time data type).
- We defined required fields (e.g. transect number and sampling date).
- We made lists of predefined values (e.g. group types: male alone, female alone, males, females, females with kids, and mixed groups).
- We established some “control fields”, that is, variables that whoever digitalise the data calculated manually to facilitate the information identification. For instance: before introducing the observations, the person had to indicate the total number of groups identified in each survey; the size of each group; the type of group categorised by sex and age; etc.
As for transects, a more accurate digitalisation was carried out at a scale of 1:1000 in ArcGIS 10.2 (ESRI, 2013), using as cartographic base the orthophotos from PNOA (Spanish National Program for Aerial Orthophoto).
(c) Debugging and disaggregation.
The data were processed through the PostgreSQL relational database management system (RDBMS) version 11.3 (PostgreSQL Global Development Group, 2013) together with R version 3.6.0 (R Core Team, 2019) using the package Rpostgres (Wickham et al. 2018) and the spatial extension PostGIS version 2.5.2 (PostGIS Project Steering Committee, 2019), in addition to other packages: DBI (R-SIG-DB, 2018), knitr (Xie, 2019), dplyr (Wickham et al. 2019) and splitstackshape (Mahto, 2019). In this way, we created a validation process in R and SQL code to check specific errors derived from digitalisation and corrected them. When it was necessary, the surveys were re-checked and we ran several validation rounds were run. Specific examples are given below:
- We checked if all the information was associated: samplings without any observers assigned, groups that had no observations assigned, etc.
- Regarding null values, we checked if all the essential variables were filled out, e.g. males without age variable, groups without size value, etc.
- We identified if there was duplicated information.
- We revised if there were incongruous data, e.g. the hour when a group was observed had to be between the start and end time of sampling.
- We also checked the “control fields” because they were susceptible to contain errors, e.g. the automatic sum of individuals did not match the indicated group size; groups categorised as mixed should be males and females with kids, etc.
(d) The standardisation to Darwin Core was done according to the practices recommended by the TDWG guidelines (https://dwc.tdwg.org/terms/).
In each sampling, the observers walk the linear transects taking notes on the ibex groups sighted and collecting different variables such as: the number of individuals observed (group size); the contact hour; and perpendicular distance of each group of goats to the transect line. At the individual level, records are made of physical condition (mainly the presence of lesions caused by sarcoptidosis), the sex of each ibex, and the age in the case of the males. In addition, the date as well as the starting and ending times of the sampling are also recorded, as well as the identity of the observers.
The transects are sampled by two or more observers, on foot or by vehicle, when terrain conditions allow, at a speed of no more than 15 km/h. The sampling time is adapted to the dates when the field work is carried out, recording the official time in the surveys. In summer time, the observers walk the transects mainly at dawn and dusk. In the other seasons, the sampling time is extended throughout the day. The optical materials used are binoculars (8 x 35) and a telescope (20 x 40). When circumstances prevent a satisfactory viewing (individuals far away or hidden by the surrounding vegetation, temporary brevity of contact, etc.) sightings are not taken into account.
The probability of detecting an individual is related to spatial distribution of the sightings (Buckland et al. 2004) and visibility conditions, habitat coverage, land topography, animal and group size, as well as the density. The method assumes that, if the density is high, many individuals will be sighted up close. If the density is low, only a few individuals will be sighted, and far away. The following premises must be assumed: animals on the transect line are always observed; animals must be immobile when they are observed or located on the spot before they move; no animal should be counted twice; distances and sighting angles must be calculated accurately and sightings are independent events.
With all these data collected, the parameters that define the population were: density (number of individuals/km²), sex ratio (number of females/number of males), birth rate (number of kids/number of adult females) and age pyramid mainly for males, in which the size of the horns and body morphology make it easier to determine the years of age or age class to which they belong.
Type of content
Includes: point occurrence data.
Granados Torres J E (2020): Dataset of Iberian ibex population in Sierra Nevada (Spain). v1.7. Sierra Nevada Global Change Observatory. Andalusian Environmental Center, University of Granada, Regional Government of Andalusia. Dataset/Samplingevent. https://doi.org/10.15470/3ucqfm
To the extent possible under law, the publisher has waived all rights to these data and has dedicated them to the Public Domain (CC0 1.0). Users may copy, modify, distribute and use the work, including for commercial purposes, without restriction.
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Metadata last updated on 2020-05-14 12:24:38.0