Towards standardising the collection of game statistics in Europe: a case study

Abstract

In order to monitor wildlife populations in a manner that supports policy makers and natural resource managers, data must be collected using frameworks and methodologies that allow for comparisons between projects and across time. Though hunting statistics may represent a reliable data source for monitoring population trends in game species, a standardised framework for collecting and analysing this data has never been established in Europe, even within countries. Here we describe a case study on the use of hunting statistics in Spain in order to (i) describe the variability in big game statistics collection frameworks across mainland regions of Spain and (ii) propose a minimum common denominator for a standardised approach at the country level. The main differences in methodologies identified are that each region collects different variables, uses different spatial and temporal resolution, and follows different methodologies. We described spatial patterns by grouping regions based on similarities in the hunting data collection system and identified socio-economic factors as a potential driver of differences in methodologies among regions. Hunting effort-related variables and improved temporal resolution (to the event level) must be incorporated in order to achieve country-level standardisation of methodologies. The use of application software to collect information from the field in a standardised way is recommended, which necessitates engaging stakeholders as part of the monitoring process. Applications software should be designed intentionally, and only after clear objectives for the monitoring program have been defined. Making hunting data open access will improve collaboration and information transfer to scientific and professional sectors. Our recommendations can be adapted to other European countries, which would make hunting data more useful for population monitoring and wildlife policy-making at large spatial scales. Initiatives such as the “European Wildlife Observatory” (www.wildlifeobservatory.org), a network of wildlife observation and monitoring points in Europe, may improve data exchange and standardise protocols, leading to better utilisation of hunting statistics for European wildlife population monitoring.

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Vol.:(0123456789)
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European Journal of Wildlife Research (2023) 69:122
https://doi.org/10.1007/s10344-023-01746-3
RESEARCH
Towards standardising thecollection ofgame statistics inEurope:
acase study
CarmenRuiz‑Rodríguez1· JoséA.Blanco‑Aguiar1· AzaharaGómez‑Molina1· SoniaIllanas1·
JavierFernández‑López2,3· PelayoAcevedo1· JoaquínVicente1
Received: 6 October 2022 / Revised: 30 October 2023 / Accepted: 31 October 2023 / Published online: 29 November 2023
© The Author(s) 2023
Abstract
In order to monitor wildlife populations in a manner that supports policy makers and natural resource managers, data must be
collected using frameworks and methodologies that allow for comparisons between projects and across time. Though hunting
statistics may represent a reliable data source for monitoring population trends in game species, a standardised framework
for collecting and analysing this data has never been established in Europe, even within countries. Here we describe a case
study on the use of hunting statistics in Spain in order to (i) describe the variability in big game statistics collection frame-
works across mainland regions of Spain and (ii) propose a minimum common denominator for a standardised approach at
the country level. The main differences in methodologies identified are that each region collects different variables, uses
different spatial and temporal resolution, and follows different methodologies. We described spatial patterns by grouping
regions based on similarities in the hunting data collection system and identified socio-economic factors as a potential driver
of differences in methodologies among regions. Hunting effort-related variables and improved temporal resolution (to the
event level) must be incorporated in order to achieve country-level standardisation of methodologies. The use of application
software to collect information from the field in a standardised way is recommended, which necessitates engaging stakehold-
ers as part of the monitoring process. Applications software should be designed intentionally, and only after clear objectives
for the monitoring program have been defined. Making hunting data open access will improve collaboration and information
transfer to scientific and professional sectors. Our recommendations can be adapted to other European countries, which would
make hunting data more useful for population monitoring and wildlife policy-making at large spatial scales. Initiatives such
as the “European Wildlife Observatory” (www. wildl ifeob serva tory. org), a network of wildlife observation and monitoring
points in Europe, may improve data exchange and standardise protocols, leading to better utilisation of hunting statistics for
European wildlife population monitoring.
Keywords Hunting statistics· Big game· Europe· Method standardisation· Hunting effort· Wildlife monitoring
Introduction
Wildlife monitoring is a fundamental part of sustainable
population and ecosystem management. “Monitoring” in
a wildlife management context means regularly observing
and recording information on wildlife populations and the
environment they inhabit to characterise change over time
(Apollonio etal. 2010). Successful monitoring also requires
considering a number of factors relevant for management,
such as anthropogenic impacts, human-wildlife conflicts, and
stakeholder and societal acceptance of management actions
(Redpath etal. 2004). Management approaches are normally
aimed at long-term feasibility, i.e. being sustainable over time.
Wildlife population monitoring and the use of indicators sup-
ports understanding essential ecological, epidemiological, and
* Joaquín Vicente
joaquin.vicente@uclm.es
José A. Blanco-Aguiar
joseantonio.blanco@uclm.es
1 Grupo Sanidad y Biotecnología (SaBio), Instituto de
Investigación en Recursos Cinegéticos (IREC), UCLM-
CSIC-JCCM, 13071CiudadReal, Spain
2 CEFE, Université Montpellier, CNRS, EPHE,
IRD34090Montpellier, France
3 Universidad Complutense de Madrid, 28040Madrid, Spain
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European Journal of Wildlife Research (2023) 69:122
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socio-economic processes. Such knowledge is necessary to
develop proactive management and to utilise the adaptive
management model (Gamelon etal. 2012). Adaptive manage-
ment is the process of making decisions supported by the best
available knowledge, while continually adjusting objectives
and resources to make management more efficient, effective,
or practical over time (Riley etal. 2003).
Wildlife population monitoring should be approached
in a rigorous and systematic way according to scientific and
technical standards, and data must be managed and analysed
in a standardised way to be able to credibly support manage-
ment decisions and legal arguments, even in court (Thompson
etal.1998, Vicente etal. 2019). Only through this approach will
the collected information be comparable among data collection
frameworks, and useful in decision making. Selection of the
monitoring framework and specific methods (study design) to
be implemented depends on the goals and logistical capabilities
of the monitoring system (e.g. Acevedo etal. 2008; Nichols etal.
2001). Even when a wide set of methodologies are suitable to
monitor wildlife populations, the methods are constrained by the
need to be applicable at large spatial scales for most monitoring
systems (ENETWILD-consortium etal. 2021a).
Hunting statistics, in general terms, include variables
related to the total number of animals seen and/or hunted dur-
ing a hunting event or period, within a given area or time, and
sometimes associated with other hunting effort and hunting
effectiveness variables (Nichols etal. 2001). These data may
offer a reliable alternative for monitoring population trends of
big game species, and can be used to model their distribution
and abundance patterns at large spatial scales (e.g. Gamelon
etal. 2012; Imperio etal. 2010; Ruiz-Rodríguez etal. 2022).
However, hunting statistics can be influenced by many factors
not always measured in the datasets (such as regional hunting
traditions and hunting regulations, hunting pressure, hunting
ground size, hunting area characteristics, and hunters’ availabil-
ity, training, and engagement). When these factors are not taken
into account, it makes it difficult to directly compare datasets
across territories (e.g. Bosch etal. 2012; Vajas etal. 2021).
However, when hunting statistics are recorded under a rigor-
ous and systematic way, including relevant variables related
to hunting effort (e.g. surface of the hunting area), they can
be used as simple indicators of relative population abundance,
even achieving estimations of population density (Artelle etal.
2018; ENETWILD-consortium etal. 2019), or may feed more
complex models (Gamelon etal. 2012). This can support long-
term and large-scale population monitoring systems that are in
high demand by wildlife managers and epidemiologists (Aubry
etal. 2020). As different interests and stakeholders may be in
conflict with one another (e.g. urban vs rural, hunters vs animal
rights activists), societies require science-informed policies.
Therefore, it is essential to generate wildlife demographics data
support policies and modern wildlife management (Delibes-
Mateos 2015; Martínez-Jauregui etal. 2020).
Currently, there is not a standardised framework for
hunting statistics collection in Europe at the country level
(particularly for Federal or similarly decentralized countries
where data collection depends on local/regional methodolo-
gies); each country/region typically collects this data using its
own methods, and stores the data in repositories with variable
accessibility (ENETWILD-consortium etal. 2018a). In addi-
tion, there are differences in hunting traditions and policies
followed by countries/regions, which makes describing meta-
data essential to standardise the data. Differences in the way
hunting statistics are collected among European countries are
potential obstacles to the common use of hunting statistics at
a large scale. As a result, there are calls in the literature for
the creation of standardised data collection systems for all
countries in order to obtain large-scale, quality data (Aubry
etal. 2020; ENETWILD-consortium etal. 2018b). In order
to create a proposal for a standard methodology to collect
and manage hunting statistics, it is necessary to first know
each country’s current data collection systems for big game,
and the limitations and advantages of these existing systems.
Here we aim to describe a case study in Europe, in order
to (i) describe the variability in the big game statistics col-
lection frameworks across mainland regions in Spain, and
(ii) to propose a minimum common denominator among
regions for a standardised framework of data collection that
could be useful for wildlife managers and feasible to imple-
ment at the country level.
Material andmethods
Spain is composed of 17 Autonomous Communities (AC
hereafter; NUTS2 level), 15 of which are mainland communi-
ties composed of 47 provinces (NUTS3 level) where hunting
legislation and management policies may be shared. Under the
umbrella of the national legislation “Ley 1/1970, de 4 de Abril
de Caza” (see Martinez-Jauregui etal. 2011), each AC has
its own hunting regulations and data collection methodology.
We distributed a questionnaire (see Hunting Question-
naire in Supplemental information) to mainland Spain AC
governmental hunting agencies, except for Basque Country,
which had the questionnaire distributed to each of its three
provinces (NUTS3, i.e. Araba, Bizkaia, Gipuzkoa), which
operated independently in terms of hunting management
and hunting statistics data compilation (regions hereafter,
a total of 17). Questionnaires were designed with the pur-
pose of collecting information about data collected in every
region. This encompassed not just the statistics on hunted
animals, but also general aspects of the hunting activity, such
as the hunting grounds or management units, the number
of animals seen during the hunting activity, the number of
animals taken, carcass management, and data management.
The questionnaire had a total of 20 questions (Table1)
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regarding the type of data available about big game hunting,
and answers were coded as recorded/not recorded. Question-
naires covered different aspects of the information collected
on big game species present in each region. The animals
that were considered to be big game for the purposes of the
questionnaire included all hunting species of wild ungulates:
wild boar (Sus scrofa), red deer (Cervus elaphus), roe deer
(Capreolus capreolus), fallow deer (Dama dama), Iberian
wild goat (Capra pyrenaica), Pyrenean Chamois (Rupicapra
pyrenaica), mouflon (Ovis musimon), and Barbary sheep
(Ammotragus lervia). In certain regions, the specific types
or range of data collected varied depending on the game spe-
cies. In such instances, governmental hunting agencies were
requested to provide information that could be generalised
and applied broadly, focusing on the records collected for the
most widely distributed big game species in Spain, namely
wild boar, red deer, and roe deer.
We distributed questionnaires in 2018–2019 to govern-
mental hunting agencies, and we offered support via email
and telephone. The information collected was updated in
2021, so this paper reflects the situation in that year. The
responses from these questionnaires were compared to the
responses from forms provided to hunters or managers
of hunting grounds for reporting the results of hunts con-
ducted by each region’s governmental hunting agencies.
This allowed for a comparison between the information
gathered through the questionnaires and the data recorded
through the official reporting system used by governmental
hunting agencies in each region. However, in many cases,
it is not mandatory to provide all the hunting statistics, and
numerous hunting results are submitted without filling in
all the variables requested by the regional hunting agencies.
Consequently, the data collected through the questionnaires
represent the best available data in Spain. We first used
Table 1 List and description of the variables used in the analyses collected through the questionnaires and grouped according to the level of
information
Variables that could be collected at different temporal resolutions (season vs hunting event) are underlined in the “Variable” column
Level of information Variable Variable description
Hunting ground characteristics
and management Type ground Is the type of hunting ground recorded? (Public/Private/Protected
area?)
Fencing ground Is whether the hunting ground is fenced recorded?
Feeding ground Is whether supplementary feeding is provided recorded?
Presence livestock Is data on the presence of extensive livestock recorded?
Which livestock Is the species of livestock present recorded?
Livestock number Is the number of herds/flocks per species known?
Game animals and hunting events No. of hunted animals • Is the no. of hunted animals per species and season recorded?
• Same as above, but per event
No. of observed animals per event Is the no. of observed animals recorded, by species and event?
Hunting modality • Is the hunting modality used per season recorded? (Drive hunting/
still hunting/stalking)
• Same as above, but per event
No. of hunters • Is the no. of hunters per season recorded?
• Same as above, but per event
Hunting area per event Is the hunting area surface (in case of hunting drives) per event
recorded?
No. of dogs • Is the no. of dogs used in each season in the hunting ground
recorded?
• Same as above, but per event
Hunted animal sex • Is the no. of hunted animals per season according to sex recorded?
• Same as above, but per event
Hunted animal age • Is the no. of hunted animals per season according to age recorded?
• Same as above, but per event
Hunted animal fertility Is the fertility condition of females per event recorded?
Hunted animal weight It the body weight of each hunted animal per event recorded?
Carcasses management Carcass search Is there an active search for carcasses of dead animals? (surveillance)
No. of carcasses If yes, is the no. of found carcasses known per time unit (week/
months, etc.)?
Data management Application Is there a mobile or web application software available for the collec-
tion of hunting data?
Data accessibility Are the hunting statistics data available online to be downloaded?
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descriptive statistics to identify differences among regions
in the big game statistics data collection methodologies.
We used multivariate statistics using a hierarchical cluster-
ing analysis with the packages “stats”, “cluster” (Maechler
etal. 2022), and “factoextra” (Kassambara and Mundt2020)
using R software (R Core Team 2021) to select the number
of groups with similar data collection frames.
We conducted a comprehensive analysis using principal
component analysis (PCA) in conjunction with a logistic
regression model in order to analyse the underlying factors
influencing the differences in hunting data collection sys-
tems across regions, Firstly, we collected data on various
factors including economic outcomes, human resources,
and hunting activity coverage from each AC (see Table2)
and summarized these descriptors in a PCA. The main
sources for these data were the Forest Statistical Yearbook
(Anuario de Estadística Forestal 2020) and the National
Statistical Institute (Instituto Nacional de Estadística 2020).
The data associated with human resources within each
department was collected from the Spanish Association of
Forestry and Environmental Agents (2005–2013) but was
updated by reviewing the public job vacancies offered and
published by each Autonomous Region and was comple-
mented with additional surveys that were given to the heads
of governmental hunting agencies in each region.
Secondly, we constructed a logistic regression model
(binomial, logit link) using the “MASS” library (Venables
and Ripley 2002) in R. PCA factors were treated as explana-
tory variables, modelling the probability of a region belong-
ing to cluster 1 versus cluster 2 given its economic, logistic,
and hunting characteristics. The best model was selected using
likelihood ratio tests (LRT) and a step-wise procedure-routine
based on the Akaike information criterion (AIC; Akaike 1974).
This approach ensured the inclusion of relevant and influential
variables in explaining cluster membership within the context
of hunting data collection systems.
Finally, to propose a standardised and minimally com-
plete data collection methodology at the country level, we
identified the key variables that must be collected by the
regional data collection frameworks.
Table 2 Variables used in the PCA to characterise each of the Autonomous Communities
Variables considered for inclusion were economic, human resources, and the volume or coverage of hunting resources available in each of the
regional administrations
Variable ID Variable descriptor Source
HuntGrounds Number of hunting grounds (2020) Forest Statistical Yearbook
https:// www. miteco. gob. es/ es/ biodi versi dad/ estad istic as/
fores tal_ anuar io_ 2020. aspx
HuntArea Hunting grounds area (ha) (2020) Forest Statistical Yearbook
https:// www. miteco. gob. es/ es/ biodi versi dad/ estad istic as/
fores tal_ anuar io_ 2020. aspx
PrivGrounds Percentage of private hunting grounds in relation to the total
area of hunting grounds
Forest Statistical Yearbook
https:// www. miteco. gob. es/ es/ biodi versi dad/ estad istic as/
fores tal_ anuar io_ 2020. aspx
HuntLicenses Number of hunting licenses Forest Statistical Yearbook
https:// www. miteco. gob. es/ es/ biodi versi dad/ estad istic as/
fores tal_ anuar io_ 2020. aspx
HunLinArea Number of hunting licenses per area of hunting grounds (2020) Forest Statistical Yearbook
https:// www. miteco. gob. es/ es/ biodi versi dad/ estad istic as/
fores tal_ anuar io_ 2020. aspx
EaArea Number of environmental agents per area of hunting grounds
(2005–2013)
Association of Forestry and Environmental Agents
https:// www. aeafma. es/ polic iamed ioamb iental/ distr ibuci on-
terri torial/ comun idades- auton omas
Updated in this study
TsArea Number of technicians per area of hunting grounds (2022–
2023) Updated this study
HuntBG Total number of big game animals hunted by year (2020) Forest Statistical Yearbook
https:// www. miteco. gob. es/ es/ biodi versi dad/ estad istic as/
fores tal_ anuar io_ 2020. aspx
GDPpp Percentage of Gross Domestic Product dedicated to primary
production (2020)
National Statistical Institute
https:// ine. es/ jaxi/ Tabla. htm? tpx= 31677 &L=0
GDPpc Gross Domestic Product per capita (2020) National Statistical Institute
https:// ine. es/ jaxi/ Tabla. htm? tpx= 31677 &L=0
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Results anddiscussion
The data collection of big game hunting statistics in 2021
in Spain was highly heterogeneous among regions (Table3;
Fig.1) which could be caused by differences in the way big
game is managed and hunted. However, most of the regions
included highly detailed spatio-temporal resolution, i.e. at
the hunting event and hunting ground spatial resolution. In
Spain, there are four main hunting modalities for big game
species. Although they are practiced throughout the terri-
tory, there may be regional preferences for some modalities
depending on the target species and local hunting culture:
drive hunting with dogs, battue, stalking, and fixed point.
“Drive hunting with dogs” is widely used throughout South-
Central Spain. In this modality, the hunt area is surrounded
by hunting posts and beaten with dogs. Although mainly
used for hunting wild boar, battue can also be used for red
deer, roe deer, fallow deer, and mouflon. “Battue” is similar
to a drive hunt with dogs, with a smaller surface area and
fewer hunters, typically resulting a smaller effort than a
“drive hunt with dogs”. Additionally, the dogs are usually
replaced by people who battue the hunting area. “Stalk-
ing” here refers to a method where the hunter goes alone,
tracks the animal, and moves slowly to a favourable position
for shooting. In Spain, this modality is principally used for
hunting roe deer, although it is also used for other species
such as Iberian wild goat and Pyrenean chamois. “Fixed
point” is a widely used modality for hunting wild boar,
where the hunter stays at a fixed point, usually at night, at a
location where the target species is expected to come. The
diversity in hunting techniques and the absence of a stand-
ardised framework for collecting big game hunting statistics
across all regions of Spain present significant challenges
when comparing different territories. Furthermore, in the
case of small game species, the implementation of manage-
ment measures can vary even within distinct hunting areas
(Arroyo etal. 2012), making it more complex to consoli-
date and standardise the collected data. Consequently, it is
necessary to analyse hunting statistics separately for small
game and big game species due to the substantial hetero-
geneity in management approaches, which can differ even
within the same regions.
Table 3 Summary of the collected variables per region/province (red cells represent the variables collected in 2021)
Level of
informaonRegionsAndalucía Aragón Asturias Araba *Bizkaia *Gipuzkoa *Cantabria Ca slla-la
Mancha
Caslla y
León
Catalunya
ExtremaduraGalicia La RiojaMadridMurciaNavarra Com.
Valenciana Score
Hunng
Services
Monitoring
Program of
Wild Boar
Hunng
grounds
characteriscs
and
management
Type of
ground 17
Fencing
ground 15
Feeding
ground 7
Presence of
livestock 3
Which
livestock 2
Livestock
number 2
Game animals
and hunng
events
Nº of
hunted
animals
17
Nº of
observed
animals per
event
7
Hunng
modality 13
Nº of
hunters 13
Hunted
area per
event
10
Nº of dogs 9
Hunted
animals sex 17
Hunted
animals age8
Hunted
animals
ferlity
3
Hunted
animals
weight
4
Carcasses
management
Carcass
search 3
Nº of
carcasses 2
Data
management
Applicaon 9
Accessibility 7
TotalTotal per
Region 12 411813 13 7109 8161012111011136
Total, event
level variables
Nº of
variables
recorded at
evet-level
405466050064560550
Where variables could be collected at different temporal resolutions (underlined in the “Regions” column), we differentiated between regions
where they were collected at the hunting season level (red cells) from those collected at the hunting event level (grey cells). Note that the 3 prov-
inces [NUTS3] belonging to the Basque Country [NUTS2] are marked with *. For Catalonia, information is also detailed separately for the Wild
Boar Monitoring Program
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Although the main problem for comparing data across
regions is that different information is collected, the resolu-
tion is also not equivalent and different methodologies for
data collection were used (Martinez-Jauregui etal. 2011).
However, despite these comparability issues, overall Span-
ish regions have relatively complete data collection frame-
works (Fig.1) in terms of the number of parameters col-
lected when compared to other European countries. The
number of hunted animals at the hunting ground and season
levels was collected in all regions (ENETWILD-consortium
etal. 2018b). Additionally, all hunting grounds perimeters
have been characterised at the national level by georeferenc-
ing (ENETWILD-consortium etal. 2021b), which may be
useful for both research and management purposes (i.e. in
case of disease outbreaks such as African Swine Fever; see
Fernández-López etal. 2022 and ENETWILD-consortium
etal. 2021b for examples of implementation at the national
and international levels).
Patterns inregional data collection frameworks
overmainland Spain
We grouped Spanish regions according to similarities in
their hunting data collection systems based on the number
and type of variables collected by each region. Results from
hierarchical clustering analysis identified 2 main clusters
(Fig.2 top). The first cluster is made up of a larger number
Fig. 1 Percentage of regions in mainland Spain that collected each
hunting variable. Variables collected with different temporal resolu-
tions between regions are marked with a “*” and represented with
two colours: red represents variables collected at the hunting season
level, and grey at the hunting event level
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of regions, where, with the exception of Madrid and Mur-
cia, the regions are related to other spatially close regions.
The second cluster is formed by regions located in the north
and north-west of Spain (Navarra, Bizkaia, Gipuzkoa, Astu-
rias, La Rioja, Araba and Galicia) and contains the regions
with the most complete hunting data collection systems (see
Fig.2 bottom, map C). However, there are additional regions
that collected the key parameter “number of hunted animals”
at the event level, and other parameters at the event level
(Fig.2 bottom left map B in grey), that are distributed in
South-Central regions of mainland Spain.
We conducted a PCA incorporating the ten variables
characterising each region (see Table2) in order to bet-
ter understand the factors contributing to the clustering of
regions. From this PCA, we selected the first two axes; PC1
and PC2 (based on criteria retention factors with eigenvalue
higher than 1) that resulted in a cumulative explained vari-
able of 73% (Table4).
The PC1 explained 52% of the variance and may be inter-
preted as a workload gradient; this axis allows us to dif-
ferentiate between regions with high GDP per capita and a
high number of technicians working in governmental hunt-
ing agencies per hunting area, from other regions with fewer
economic and technical resources but a high volume of hunt-
ers, hunting grounds, and number of hunted animals. The
second, PC2 (21% explained variance), could be interpreted
as a gradient within the hunting intensity that differentiates
regions with a greater dedication to activities associated with
hunting vs. regions more dedicated to primary production
(agriculture and or livestock activities).
We also modelled the probability that these factors
(PC1 and PC2) were associated with the likelihood of
belonging to one of the two clusters. Following a likeli-
hood ratio test analysis (LRT, p < 0.005), and AIC criteria
(∆AIC = 6.02), the best logistic model included both prin-
cipal component factors (PC1 and PC2), explaining 43% of
deviance (pseudo-R). The model showed that cluster 2 was
associated positively with PC1, indicating high economic
and human resources. Simultaneously, it displayed a nega-
tive association with the number of hunting grounds, hunt-
ing area, and number of hunters. In contrast, for cluster
1, the situation was reversed, with a positive association
observed with a higher workload associated to hunting
activities (e.g. number of hunting grounds, hunting area,
and hunting bags; see Table5), coupled with higher val-
ues of PC2, representing a greater hunter density. This
aggregation within cluster 1, comprising regions with a
larger volume of information to manage (high numbers
of hunters and hunting grounds), was also characterized
by having a less complete hunting data collection sys-
tem. This could be attributed to organisational issues, as
the digitisation of information is not yet widespread in
most regions, which could lead to regions with a larger
volume of data having difficulties in adequately manag-
ing high quality information. Despite the limitations of a
small sample size, these results suggest that regions with a
higher GDP per capita invested more in human resources,
and this is associated with more complete hunting data
collection systems. This is particularly relevant in regions
with a large proportion of hunting surface in their territory.
To improve data quality, it is recommended that hunting
event data (such as group hunts) should be collected in
those regions where they are only collected seasonally
(ENETWILD-consortium etal. 2019). These data would
potentially allow for more precise abundance estimation
through the drive count methods (ENETWILD-consortium
etal. 2018b, 2020a,2021a). However, enhancing the quan-
tity and temporal resolution of the collected data requires
significant efforts from the hunting agencies. Unfortu-
nately, some regional agencies face challenges due to
insufficient financial and personnel resources. Therefore,
it becomes crucial to motivate staff, to provide adequate
training, to augment the budgets for staff in some regions,
and to provide information technology tools to support
data collection activities (“from the field to the desktop”
strategy). By implementing these measures, it would be
possible to address the proposed improvements effectively,
and to establish a national big game data collection sys-
tem capable of providing reliable data to support wildlife
management and conservation efforts.
The fact that regions in cluster 2 have similar hunting
data collection systems may reflect similarities in the hunt-
ing management and activities in these areas. In Northern
regions, hunting is mainly a social activity, whereas in
the South the economic and commercial components are
more important. Hunting modalities (e.g. specific charac-
teristics of driven hunts) may also differ between Northern
and Southern Spain (López-Ontiveros and García-Verdugo
1991). These socio-cultural differences could also explain
the differences in the way hunting statistics data are col-
lected between the two clusters.
Different data sources can be essential for monitoring
hunted wildlife populations in Spain and in Europe. Hunt-
ing statistics are essential, but not sufficient to adequately
characterise the populations and the different management
models that condition the level of hunting effort.
Accessibility, transparency, and improved spatial and
temporal resolution of the information collected, as well
as improved procedures to streamline the quality and speed
of data collection, are key. This will allow for a rapid
response to unforeseen scenarios such as new epidemio-
logical challenges, and to activate early warnings or detect
disease emergencies. The current Spanish data model has
some strengths and limitations, and the description pre-
sented here allowed us to elaborate a proposal to improve
hunting data collection systems (see below).
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Hunting ground characteristics andmanagement
Collecting hunting statistics at the smallest possible
management unit is key to guide management. It is
noteworthy that in recent decades, many estates in
mainland Spain (particularly in the Centre and South) have
been fenced in order to allow more intensive management
of big game populations with independence from the
surrounding areas (Vicente etal. 2006). This implies that
the relationship between the management unit (the surface
of connected land subject to a common criterion of hunting
management) and the ecological unit (the area of land
that corresponds to an ecosystem that maintains a certain
independence within the environment, Carranza 1999)
may vary according to the region. Therefore, information
on fencing is essential. The presence of perimetral fencing
of the hunting grounds is widely recorded (15 out of 17
regions; Fig.1), and the two cases which do not report
fences are regions known to have an absence of big game
fences (i.e., most Atlantic areas of Spain: Galicia, Asturias,
Cantabria, Araba, Bizkaia, Gipuzkoa). Therefore, data
collection on fencing can be considered complete in Spain.
The variables with the lowest recorded rate are related to
livestock presence within hunting grounds (Table3; Fig.1).
This information (together with carcass management; see
below) is relevant to characterise the epidemiological
interface between domestic and wild species, and may
help to improve our understanding on (i) shared diseases
(du Toit etal. 2017; Gortázar et al. 2007; Siembieda
etal. 2011) and (ii) determining the “stocking rate or
grazing load” of ungulates (both wild and domestic) over
management units. We are aware this information is available
at regional administrations (animal health services), and
therefore, an effort should be made to integrate information
between Departments. Wildlife monitoring must be
integrated since the inclusion of different animal types (such
as domestic animals) can notably enhance our potential to
understand and manage ecological and epidemiological
processes (Cardoso etal. 2022; Vicente etal. 2019).
In regard to intentional artificial feeding (see Glossary in
Supplemental information), seven of the 17 regions surveyed
recorded information on any kind of feeding aimed at big
game in hunting grounds. Not all regions interpreted the arti-
ficial feeding question in the same way. Different interpreta-
tions likely depended on the local/regional feeding practices
and supplementary feeding was sometimes confused with
baiting. Baiting can be defined as a strategy used prior to a
hunting event or during stalking to attract animals, and thus
increase the effectiveness of hunting(Inslerman etal. 2006).
Though supplementary feeding in hunting grounds is forbid-
den by law in Spain (RD. 138/2020), there are exceptions
in which supplementary feeding may be used with the prior
authorisation of the regional hunting authority. Some of
these exceptions that allow artificial feeding in hunting areas
are to increase the effectiveness of hunts during emergency
situations, such as during times of overpopulation of ungu-
lates, and in particularly adverse climatic situations (e.g.
severe drought). Only some regions recorded information
on these exceptional cases. Exceptions to the prohibition by
law are managed by each region individually. Because of the
ambiguity between the interpretation of terminology regard-
ing artificial feeding (supplementary vs baiting), a stand-
ardised collection of information on artificial feeding must
be solved legislatively. One existing proposal is to regulate
a maximum feeding quantity (kg of feed) per time and area,
as in the “Strategy approach to the management of African
Swine Fever for the EU” (SANTE/7113/2015-Rev12).
Game animals andhunting events
Here we refer to the total number of animals hunted and
the number of animals hunted relative to the number of
animals observed during hunting activities as the hunting
effectiveness variables (see glossary). The number of ani-
mals hunted was a variable collected by all regions, and in
most cases, at the best possible temporal resolution (collec-
tive hunting event, 11 regions; see Table3). However, the
number of animals observed per event was less frequently
reported (collected only in 7 regions). These two variables
can be used to precisely estimate abundance, applying the
driven count method when the beaten surface area is also
recorded (ENETWILD-consortium etal. 2019). However,
the beaten area (see below) is only collected in nine regions
and these regions are not always the same as the regions
that record the number of animals sighted during collective
hunts. It is important to note that in most regions, reporting
hunting results at the event level is not obligatory. Conse-
quently, despite providing the option to collect information
at the event level in their data collection forms, the data
received is frequently aggregated by season. Therefore, the
Fig. 2 Top: Hierarchical clustering analysis dendrogram. The vertical
axis represents the distance or difference between ACs or provinces.
The horizontal axis represents all ACs and provinces. Bottom: map
A of mainland Spain differentiates regions according to the number
of variables collected at the event level, from 0 to 6 variables, regions
in red did not collect any variables at the event level, and regions in
yellow were where all six variables were collected at the event level.
Map A also differentiates regions that collect data about the number
of animals observed as well as the hunting area at the event level
with (*). On the left bottom, map B of mainland Spain differenti-
ates regions according to the variable “number of hunted animals”:
in grey, this variable is collected at the event level, and in red, at the
season level. In Catalonia, the Monitoring Program of Wild Boar
populations in Catalonia (Rosell etal. 2021) collects data at the event
level in a network of 22 sites. On the right bottom, map C of main-
land Spain, showing regions according to cluster (regions belonging
to cluster 1 in grey, and cluster 2 regions in yellow)
◂
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European Journal of Wildlife Research (2023) 69:122
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questionnaire results presented here represent, at best, the
data collected by each region. The hunting modalities of
“drive hunting with dogs” and “battue” are predominantly
used for harvesting wild boar, as well as other species
such as red deer, roe deer (in Northern Spain), fallow deer,
and mouflon. In these modalities, the number of animals
observed during hunting events is frequently recorded
by hunters. It is noteworthy that in most regions, the
recorded observations typically refer only to individuals
of the same species being hunted. Only a few regions have
implemented the practice of annotating observed animals
of non-target species as well. Extending this annotation
to include observed individuals of other big game species
in these types of hunts (driven with dogs or battue), and
correlating them with the hunting area’s surface, could be
an improvement applicable to other regions. This enhance-
ment would aid in estimating the overall abundances of
several species and provide a more comprehensive under-
standing of their populations. The ideal situation would be
to have the three variables (no. of hunted animals, no. of
observed animals, and the area of the hunted surface) avail-
able at the event level resolution for all regions to allow
for abundance estimations. Also, statistical inference with
these variables at this resolution guarantees greater objec-
tivity and robustness of spatial predictive models (Aubry
etal. 2020; ENETWILD-consortium etal. 2021b).
Information concerning hunting effort (hunting modal-
ity, number of hunters and beaten area) was collected at the
event level in nine regions, and the number of dogs used
was collected in eight regions (Table3; Fig.1). Recording
these data at this resolution is relevant for characterising
the hunting pressure and to evaluate hunting effectiveness.
Several studies consider the relative abundance of hunting
species by relating the total number of animals hunted to
spatial variables, such as province, municipality, or hunting
area ( Acevedo etal. 2014; Bleier etal. 2012; Bosch etal.
2012; Imperio etal. 2010; Pittiglio etal. 2018). In Spain,
all governmental hunting agencies in the surveyed regions
have geospatial information on hunting grounds, which,
together with the total number of animals hunted per hunt-
ing ground and hunting season, can be used for modelling
relative abundances of big game species (Ruiz-Rodríguez
etal. 2022). However, data on hunted animals at the high-
est temporal (hunting event) and spatial (area hunted per
hunting event or battue) resolution would allow for better
predictions, as hunting effort variables influence the num-
ber of animals successfully hunted (see Segura etal. 2014;
Vajas etal. 2020).
Data on big game fertility and body weights (Table3;
Fig.1) were rarely collected (four and three regions respec-
tively, out of 17). However, this information is relevant to
understand population dynamics (Clutton-Brock etal. 1997;
ENETWILD-consortium etal. 2022) and management, such
as quotas. The collection of this data at the individual level
could be costly and labour-intensive and require certain
expertise, but it should be considered a goal for a stand-
ardised methodology. The number of individual samples
required to describe the reproductive status of the population
may depend on the expected pregnancy rate. For example,
according to Mayor etal. (2017), the minimum sample size
required for a 10% confidence limit in the order Artiodac-
tyla, assuming an expected pregnancy rate of 42% and an
unlimited population size, would be 94 sampled individuals.
In the case of the target ungulate populations in our study,
we could propose collecting data for 20–30 individuals to
describe the reproductive status at the hunting ground level,
at a selection of 10% of the hunting grounds. In order to
account for the regional variability (i.e. food availability,
different management techniques, and diverse environmental
conditions) it may be necessary to describe the performance
at the regional level (Jovani and Tella 2006).
The Monitoring Program of Wild Boar populations in
Catalonia merits special discussion (Rosell etal. 2021).
This program has monitored wild boar populations for
more than 20 years at the regional scale. The objective
Table 4 Outcomes of the principal component analysis (PCA) per-
formed on the hunting-related effort variables obtained for each
region
The table provides the correlation coefficients, standard deviation,
proportion of variance, and cumulative proportion for each compo-
nent (PC1 and PC2)
Variable PC1 PC2
HuntGrounds − 0.41 0.14
HuntArea − 0.40 0.00
PrivGrounds − 0.18 0.30
HuntLicenses − 0.30 0.38
HunLinArea − 0.10 0.60
EaArea 0.26 0.49
TsArea 0.36 0.09
HuntBG − 0.40 0.07
GDPpp − 0.31 − 0.30
GDPpc 0.30 0.21
 Standard deviation 2.29 1.45
 Proportion of variance 0.52 0.21
 Cumulative proportion 0.52 0.73
Table 5 Results of the logistic regression model, including coeffi-
cients, standard error odds ratio, and 95% confidence intervals for the
odds ratio
Coefficient (β) Standard error Odds ratio (CI 95%)
Intercept 2.41 1.42 11.11 (1.33–489)
PC1 − 1.80 0.99 0.16 (0.01–0.69)
PC2 1.20 0.84 3.34 (0.84–33.1)
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European Journal of Wildlife Research (2023) 69:122
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is to quantify the demographic trends of wild boar
populations utilising the expertise of a network of
collaborators consisting of managers, technicians, and
hunters. The program consists of a network of 22 sites
(observatories) which represent different bioclimatic
regions of Catalonia. Each observatory collects detailed
data on all wild boar hunts. The same methodology has
been maintained over time, allowing for comparison
of results and the exploration of population parameters
through time. Data collected are of high temporal
(collected for each wild boar drive hunt or event) and
spatial resolutions with registered information about:
date, hunting area, the number of hunters and dogs, the
number of wild boars observed and hunted, and the sex
and weight of each hunted animal. Thanks to the quality
and long-term maintenance of the data collected by this
program, it is possible to estimate important parameters
for wild boar management, such as hunting effectiveness,
abundance estimates, and population characterisation in
the studied areas. Its representative design also allows for
inference at the regional level to inform policies and apply
adaptative management.
Continuing along the lines of this program, the
“European Wildlife Observatory” initiative (EOW, www.
wildl ifeob serva tory. org) has recently been established as a
network of wildlife observation and monitoring points at
the European level. The aim of this project is to include
different study areas representing all European countries
and bioregions that collect data on wildlife (including high-
quality hunting data) in order to monitor wildlife population
trends. The EOW provides guidelines and tools for density
estimation of wild terrestrial mammals such as ungulates
(ENETWILD-consortium etal. 2018c,2021a), support, and
training for survey design and data analysis; facilitates data
exchange; and generates information necessary to support
monitoring of European wildlife populations.
Carcasses
We identified a low collection rate of variables related to
the search and count of carcasses found in hunting grounds
(Table3; Fig.1). This information is key to improving our
knowledge on mortality and the epidemiology of diseases,
as well as to generating risk analyses and the application of
prevention and eradication protocols for diseases (Gervasi
and Gubertì 2022; Lim etal. 2021; Morelle etal. 2019).
Carcass finding is essential for early detection of diseases
and preventing the spread of outbreaks, such as in the case
of ASF in wild boar.
Data management
The hunting data collected in Spain are compiled by the
responsible administrations of each region and are available
upon request at the hunting ground resolution for scientific
purposes. This accessibility contrasts with other existing
models in Europe, where data are usually collected and
archived by hunting associations, and the availability of this
information is restricted or in many cases inaccessible at a
spatial resolution suitable for incorporation into models of
good spatial resolution (see ENETWILD-consortium etal
2021). Furthermore, in terms of accessibility, only 7 out
Table 6 Big game statistics (no. of hunted animals/species) openly shared by certain regions, and the link to the website or open document
(access in October 2023)
AC Spatial resolution Time resolution Link
Andalucía Provinces Hunting season https:// porta lredi am. cica. es/ desca rgas? path=% 2F16_ INDIC ADORES_
ESTAD ISTIC AS% 2F01_ IMA% 2FIMA_ 2020% 2FEst adist icas_ indic adores%
2F08_ Espac ios_ fores tales% 2F08. 07_ Caza_y_ pesca
Castilla La-Mancha Provinces Hunting season https:// www. casti llala mancha. es/ sites/ defau lt/ files/ docum entos/ pdf/ 20210 813/
memor ia_ anual_ caza__ clm_ 2020. pdf
Castilla y León Provinces Hunting season https:// medio ambie nte. jcyl. es/ web/ es/ caza- pesca/ resul tados- tempo radas-
cineg eticas. html
Catalonia Provinces Hunting season https:// www. idesc at. cat/ indic adors/? id= aec&n= 15201 &t= 2020& lang= es
Wild Boar Monitoring
Program (Catalonia) Hunting estate and
hunting reserve
Hunting season https:// agric ultura. gencat. cat/ web/. conte nt/ 06- medi- natur al/ caca/ enlla cos-
docum ents/ infor mes- tecni cs/ progr ama- segui ment- pobla cions- sengl ar- sus-
scrofa/ fitxe rs- binar is/ segui ment_ sengl ar_ cat_ 2020- 21. pdf
Gipuzkoa Management units Hunting season https:// www. gipuz koa. eus/ docum ents/ 29466 49/ 34957 019/ 2021- 22+ Ehiza+
larria. pdf/ ce499 399- 64f3- d2d1- aeeb- 8ed8c 999e3 fb
La Rioja Type hunting ground Hunting season https:// www. lario ja. org/ medio- ambie nte/ es/ estad istica/ mater ias/ estad istic as-
medio ambie ntales
Murcia Municipalities Hunting season https:// cazay pesca. carm. es/ docum ents/ 537485/ 15033 56/ Infor me+ de+
captu ras+ cineg% C3% A9tic as+ 2020- 2021/ 20f5a a78- be1f- 46f3- a4f2-
9d03f a6efd 37
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European Journal of Wildlife Research (2023) 69:122
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of 17 regions surveyed and the Wild Boar Monitoring Pro-
gram in Catalonia share records openly on big game harvest,
though at low spatial resolution (Table6). Currently, less
than half of the mainland regions have a platform for down-
loading hunting statistics, which makes it difficult for the
scientific community or other users to access the data. The
open (or under certain restrictions) availability of the hunt-
ing statistics of all regions at a high spatial resolution would
be an important advancement for scientific and technical use
(e.g. in case of ASF outbreak).
Although the reporting of hunting data is mandatory in
Spain in most cases, most regions are far from having com-
plete data collection. The availability of data at good spatial
and temporal resolutions relies on more than half (9 of 17
regions) of the regional governmental hunting agencies, hav-
ing a mobile or web-based application software, or an online
form for hunting statistics data collection (Table3; Fig.1).
In spite of several governmental hunting agencies intending
to develop an application software in the short term, the
use of paper forms is still the main method of hunting data
collection in Spain, which requires manual digitisation by
governmental hunting agencies staff. The general trend is an
increase in the development and implementation of infor-
mation technology tools (mobile or web-based software) to
facilitate the collection and management of hunting infor-
mation, as data can be digitised from the field. However, if
applications software are going to be truly useful in wildlife
monitoring, their design must follow an appropriate design
of monitoring system and objectives. Otherwise, there is a
risk that the collected information will not be standardised
and useful for wildlife management decision-making. The
structure and way of collecting the information must fol-
low scientific-technical standards that even allow for com-
parisons between territories (ENETWILD-consortium etal.
2020b). If hunting agencies wish to incorporate the use of
these applications software (which is highly recommenda-
ble), they should be designed to collect data on fine time and
spatial scales, such as the hunting event, as well as collect
data on hunting effort and effectiveness in a standardised
way, using similar fields and vocabulary.
Recommendations
The main recommendations for the standardised improve-
ment of hunting statistics include collecting variables at
the smallest spatial and temporal scale and focusing on a
few proposed variables presented below (see Table7). This
approach and data model can be adapted to other countries
across Europe, considering the context of each country,
which would notably improve the usefulness of hunting sta-
tistics data as a tool for sustainable management of big game
species at large-spatial scales. More specifically:
• It is essential to incorporate hunting effort-related vari-
ables of hunting statistics to allow for their use as indi-
cators of abundance. In the case of Spanish regions, the
most important variables are described in Table7. The
variables are related to the effectiveness and effort in
hunting activity, such as number of seen/hunted animals,
hunters, and dogs and the surface of the hunting area.
They should be mandatory to be included in the stand-
ardised protocol to record hunting statistics at the hunting
event level.
• It is recommended that regions increase the number of
variables collected in a standardised way. Therefore,
with the aim of being able to use high-quality data at
the national (and international) level, we propose a tem-
plate of a collection model for collective hunting, such
as drive hunting with dogs and battue. This model iden-
tifies the variables that should be collected at the event
level (Table8). According to our results, some Spanish
regions have a complete hunting data collection system;
their data are highly disaggregated spatially and tempo-
rally and have the potential for population monitoring
and modelling patterns of abundance of game species
(e.g. Ruiz-Rodríguez etal. 2022). A strategy for “dis-
seminating” these systems consists of promoting discus-
sion among regions, sharing practical information, such
as the effort required and best approaches (from sampling
design to data collection and analysis) to carry out and
maintain data collection at a high resolution. As an exam-
ple for a standardised improvement of a data model, we
discuss the proposed project ENETWILD, at the Euro-
pean level (ENETWILD-consortium etal. 2018b), avail-
able also in Excel format.
• The use of new information technology tools as a com-
plement to collect information from the field in a stand-
ardised way is recommended, which implies engaging
stakeholders as part of the monitoring process. Apps
are practical and useful tools for implementing system-
atic data collection programs, and therefore should be
designed intentionally for data collection, and not vice
Table 7 Number of regions (out of 17 included in the present study)
where hunting effort and efficiency variables should be collected to
achieve a complete standardisation of hunting statistics in mainland
Spain at the event level
Effort and efficiency variables No. of regions where
they should be
collected
No. of observed animals per event 10
No. of hunted animals per event 6
No. of hunters per event 8
Hunting area per event 7
No. of dogs per event 9
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European Journal of Wildlife Research (2023) 69:122
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Table 8 Proposed form to collect essential data during collective hunts
(hunting drives with dogs and battues) at the hunting event level to
achieve high quality, standardised data collection, capable of produc-
ing density estimates (doc available at http:// wildl ifeob serva tory. org/
wp- conte nt/ uploa ds/ 2022/ 01/ Form. docx)
FORM TO COLLECTDATA DURING COLLECTIVE HUNTING EVENTS (onedriveoneform)
Name andposion (organizer, ranger, etc.)ofcount coordinator:
/
E-mail:
Telephone:
Date:
Municipality:
Hunng ground ID:Hunng ground name:
Hunng drive(name of thepatchcaveredand/or consecuve numberwithin theseason):
Startme: Endme:
Nameand/or code of thestalking site:
Nº of hunters (stalking sites):Nº of beaters: Nº of dogs:
Didyoulook fortracks before?
Did youbaitthe hunted area?
Beaten area(has): Is thereGIS fileavailable? (yes/no):
TotalNºof sightedwild boar
(including those hunted):
TotalNºofhunted wild boar:
TotalNºof sighted reddeer
(including thosehunted):
TotalNºofhunted red deer:
TotalNºof sightedroe deer
(including thosehunted):
TotalNºofhunted roedeer:
TotalNºsightedother species
(including those hunted): indicate species andnº
TotalNºhuntedotherspecies:
TotalNºsighted otherspecies
(including those hunted): indicate
speciesand
nº
TotalNºhunted otherspecies:
TotalNºsightedother species
(including those hunted): indicate species andnº
TotalNºhuntedotherspecies:
INSTRUCTIONS TO FILL THIS FORM
•
Each stalkedhuntermustfill in this form forhis posion(fieldsindicated in grey).
•
Next,all data must be summarized in a singleformbythe coordinatorofthe drivecount,who will fillinthe form far
thetotal count of theevent.You should consider thepossibledouble counngbyneighbour hunng posions.
•
It is very importanttofill in theformevenifno piecehas been seenorhunted, inthiscaseinthe corresponding
boxesitwill be set 0.
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European Journal of Wildlife Research (2023) 69:122
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versa. One suggestion for the application software or
online form design is to incorporate an alert system for
missing data. In this way, if the hunting service identi-
fies any uncollected data, they can contact the user who
submitted the information and request they provide the
missing details. Furthermore, for hunting events where
the area can be delimited, such as driven hunts with
dogs and battues, integrating a Geographic Information
System (GIS) into the application would be beneficial.
This feature would allow users to indicate the specific
area where the hunting event is taking place. Lastly, we
highly recommend ensuring interoperability between
software application tools utilised by different institu-
tions and adhering to international ecological standards
(ENETWILD-consortium etal. 2020b).
• The open availability of data collected by governmental
hunting agencies once standardised will greatly benefit
the collaboration and transfer of information to the sci-
entific and professional sectors. The standardisation of
big game data collection systems would not be useful
if access to data is limited. We found that only a small
number of Spanish regions provide open hunting data
for download (Table6), the spatial-temporal resolution
of the data was limited, and data was in different forms
and resolutions, depending on the region.
• We propose the creation or improvement of a centralised
annual publication detailing the data collection frame-
works and statistics, providing demographic analyses and
analysing trends. Hunting statistics of all regions should
be adapted to a standardised data collection model pro-
gressively. This yearly book would be based on a national
database agreed on by regional and national authorities.
Concluding remarks
Spain has a relatively complete data collecting “landscape”
(as it is not a proper system developed for this purpose) for
hunting data when compared to other European countries
(ENETWILD-consortium et al. 2018b). However, it is
composed of several disconnected regional systems, each
using their own standards. The addition of several specific
variables collected following consistent methodologies
in alignment with international standards is required to
achieve standardisation for the variables most relevant to
the management of hunting effort in the short term. Namely,
the spatial and temporal resolution of hunting effort and
effectiveness data must increase. This would allow for the
use of hunting statistics as reliable indicators of abundance
of wild ungulates on a large scale. However, the feasibility of
these proposed improvements in the hunting data collection
systems requires further work on (i) determining the cost
of implementing the proposed enhancements, including
the increase in the number of variables and resolution, (ii)
identifying the barriers and difficulties of implementation
(including the social component), and (iii) to develop/
improve data collection applications capable of standardising
different regions while meeting each regions’ expectations,
specificities, and confidentiality issues. Whereas this case
study was focused on big game species in mainland Spanish
regions, the recommendations provided here have the
potential to be applied to other species and countries across
Europe. The standardisation of hunting data at the national
level, as a first step to achieve European standardisation,
is essential for wildlife monitoring and wildlife informed
management and conservation.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s10344- 023- 01746-3.
Acknowledgements We would like to thank the AC, the Spanish Min-
istry of Agriculture and their staff, and colleagues at the IREC for their
help with data collection. We would also like to express our gratitude to
the reviewers of this work for their dedication and interest in it. Their
contributions have been extremely valuable and have significantly
enhanced the quality of the paper.
Author contribution Conceptualisation: Joaquín Vicente, José A Blanco-
Aguiar, Pelayo Acevedo; methodology: Carmen Ruiz-Rodríguez,
Azahara Gómez-Molina, José A Blanco-Aguiar; formal analysis and
investigation: Carmen Ruiz-Rodríguez, José A Blanco-Aguiar, Joaquín
Vicente; writing — original draft preparation: Carmen Ruiz-Rodríguez;
writing — review and editing: José A Blanco-Aguiar, Azahara Gómez-
Molina, Sonia Illanas, Javier Fernández-López, Pelayo Acevedo, Joaquín
Vicente; funding acquisition: Pelayo Acevedo, Joaquín Vicente; supervi-
sion: Joaquín Vicente.
Funding Open Access funding provided thanks to the CRUE-CSIC
agreement with Springer Nature. Funding was provided by project
“HAWIPO: Armonización de los datos poblacionales de la fauna sil-
vestre en España: aplicaciones a la vigilancia sanitaria y control de
enfermedades compartidas con el ganado”, proyectos de I+D+i Retos
Investigación tipo B, ref. PID2019-111699RB-I00 (Ministerio de Ciencia
e Innovación) and the ENETWILD project (EFSA framework contract
“Wildlife: collecting and sharing data on wildlife populations, transmit-
ting animal disease agents”, OC/EFSA/ALPHA/2016/01 – 01). CRR
has a PhD contract from the University of Castilla-La Mancha (ref.
2018-PREDUCLM-7825). SI has a PHD contract funded by the Span-
ish Ministry of Science (MCI - PRE2020-095091). JF-L has a grant
from Margarita Salas from the European Union – NextGenerationEU
through the Complutense University of Madrid. JAB-A has a postdoc-
toral researcher contract for scientific excellence from the UCLM (Reso-
lution of 04/04/2022), co-financed by the European Social Fund Plus.
Declarations
Conflict of interest The authors declare no competing interests.
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