Background & Summary

Over the past millennia, roughly 75% of the Earth’s land surface has been subject to human overexploitation of space and natural resources1,2. One of the most prominent outcomes of this is that biodiversity is undergoing significant declines as a result of the multifaceted impacts of human activities, including habitat conversion, degradation, fragmentation, and loss, as well as climate change, overharvesting of natural resources, and pollution3,4,5. These relatively novel pressures on natural dynamics have led many wildlife species to the brink of extinction, with increasing reports of populations and natural communities declining and simplifying worldwide6,7,8.

The European Union has adopted biodiversity policies to protect and restore the continent’s natural ecosystems and species, such as the Habitats Directive 92/43/EEC9 and the European Biodiversity Strategy10. The Habitats Directive establishes Special Areas of Conservation within the Natura 2000 protected areas network to maintain vital ecosystems and species of community interest. Member States are mandated to maintain both species and their habitats in a good conservation status for the long term, by implementing measures of conservation or restoration as outlined in the Directive’s annexes. The European Biodiversity Strategy for 2030 focuses on even more ambitious targets, such as the expansion of protected areas by 2030 ensuring 30% of the land and 30% of the seas of Europe covered, with 10% of these territories regulated under criteria of strict protection10. Moreover conservation and restoration interventions should aim at achieving that at least 30% of priority habitats and species listed in the Habitat Directive reach their Favourable Conservation status.

According to the Article 1 of the Habitat Directive9, the Favourable Conservation Status for a species is achieved when the temporally-dynamic population data show that the species can thrive in the long term as a viable component of their natural habitats; their geographical ranges are neither in reduction nor are likely to be reduced for the future and they have, and will probably continue to have, a sufficiently large realised habitat. Thus, to assess the Favourable Conservation Status, each European Member State must compile periodic reports by specifying the population and range size, habitat occupied by the species and their future prospects at the Member State and biogeographic region scale (European Environment Agency 2020). Population and range status are assessed against benchmarks, the so-called Favourable Reference Values for population and distribution size9.

Distance from the Favourable Conservation Status can be used to guide conservation action through the formulation of conservation targets. However, the distinction between establishing favourable reference values and setting concrete conservation targets should be kept in mind11. Setting targets requires the reference values to be translated into feasible and time-bound objectives. These targets should be achievable in the short, medium and long term. This requires an understanding of spatial scales and historical impacts to determine what is ecologically and technically feasible. To set targets, it is necessary to consider the historical processes and major impacts that have shaped current distributions and populations. This includes understanding large-scale development, infrastructure, urban development and socio-economic changes11.

The most recent European Member States reports compiled for the period 2013–2018 about the conservation status of habitat and species, highlights that more than 60% of the non-bird species, including plants, were still in an inadequate conservation status12. Mammals are one the most endangered taxa with 84 species (34%) listed in the Habitat Directive’s Annex II and IV, which list species requiring special conservation areas or needing strict protection12,13. According to the Report, less than 25% of assessed mammals are in good conservation status, while 55% are in poor or bad conservation status. Furthermore, for the remaining 20% the conservation status is unknown12. The reports have limitations in defining the Favourable Conservation Status and in translating favourable reference values into conservation targets. Indeed, each Member State used different methodologies, sometimes not providing known reference values or tending to produce qualitative rather than quantitative ones. This makes it difficult to translate them into conservation targets to address conservation needs and to plan specific actions.

Here, we formulated quantitative conservation targets based on the Favourable Conservation Status concept, following a similar approach to that used by Bijlsma et al.11 to define favourable reference values starting with the collection of information on species biology, population and range trends, and threats. We provided a framework for setting population and range size targets, taking into account the feasibility of achieving them. We used several methods, including models of population growth and range expansion to 2030, and a reference-based approach, to provide targets that can overcome the use of qualitative values and be useful in planning conservation actions for European mammal species.

Materials and Methods

Study area

This research focused on the 27 Member States of the European Union, according to the most updated countries profiles of the Europe. We derived the European Member State map from the World Administrative Boundaries map14, selecting the 27 countries of interest. Then, we downloaded the Biogeographic Regions map, provided by the European Environment Agency15. Biogeographic Regions are defined as areas of similar biota, flora and fauna, and divided into the Anatolian, Alpine, Arctic, Atlantic, Black Sea, Boreal, Continental, Macaronesia, Mediterranean, Pannonian and Steppic regions (European Environment Agency 2016). We overlapped the above maps to obtain a basemap with intersections between Member States and Biogeographic regions. Maps presented in this work have been elaborated in GRASS GIS (vers 7.8.2) at 0,009 degrees spatial resolution, which corresponds to ca. 100 m, and in the World Geodetic System 1984 (WGS84) (Fig. 1).

Fig. 1
figure 1

Basemap created by overlapping the Bioegeographic regions map and the Member State map, with each combination of country and biogeographic region has a univocal value. Biogeographic regions are depicted with different colors.

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General framework

We formulated conservation targets for 81 terrestrial mammal species occurring in the study area that are listed in Annex II and IV of the Habitats Directive9 and/or listed as Near Threatened, Vulnerable, Endangered or Critically Endangered by the IUCN Red List of threatened species13. To obtain quantitative targets for species range and population, we adopted a multi-step approach (Fig. 2):

Fig. 2
figure 2

Flow chart of the general framework used to formulate the targets (T). First, (1) we gathered information on species’ biology, historic trends and threats to (2) decide whether target (T) is equal to or greater than the current value (1). Then, when T> CV we estimated the targets, following a hierarchical process (3a), using the model based approaches (i) Population Growth models (PGM) and (ii) Range Expansion model, (iii) reference-based approaches and (iv) gap-filling; otherwise, when T = CV, we set the targets based on the current population or range size (3b). (4) We then scaled up T to the level of Member State (MS) and Biogeographic region (BIOGEO).

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In Step 1 (Fig. 2) we performed a literature search to gather information about species biology, functional traits, habitat use, current distribution and population, and to assess both historical and current threats, and the conditions of the population and range before the threats. All information comes from relevant scientific papers, from the assessments of the IUCN Red List of Threatened species13 and from national or regional reports made by ministries and official environmental institutions. In the absence of other sources, we used the State of Nature Report (2013–2018) provided by Member States12,16. All the literature for each species is listed in Data sources17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186. We used this information to divide species in two sets: those that needed targets greater than the current population abundance and distributional range extent (hereafter: current values) based on threats and temporal trends, and those whose targets should be equal to the current values (Fig. 2).

We set targets equal to the current values (Step 3b in Fig. 2) when:

  • A species is Least Concern according to the IUCN Red List and has a stable or increasing population trend and never experienced a decline in the past. Thus, its current status may be already considered as favourable;

  • The population has experienced a decline that is also projected for the future and when threats to the species are irreversible; therefore, the range and the population size can not be greater than the current values since it is not feasible for the species to reach a higher target in the short term. To define an irreversible threat, we followed the definition given in the guidelines for Using the IUCN Red List Categories and Criteria (2012)187. According to the guidelines, an irreversible threat occurs when a population size decreases so significantly that recovery becomes impossible or unlikely due to factors such as the Allee effect, i.e. when the populations are so small that their growth rate is reduced, also causing a reduction in individual fitness188. They are also defined as those threats that are associated with profound and unrecoverable habitat loss; for instance, sometimes, urban sprawl and deforestation may cause the loss of species’ habitat which is not possible to restore, not even by changing management practices187. Also the already-ongoing impacts of climate change on species may be irreversible, due to the time lag between greenhouse gas emissions and the resulting climate and biological changes189,190.

Otherwise, when a species has experienced a decline in the past and has been threatened by reversible threats, it means it has the potential to recover and the target should be greater than the current values (Step 3a).

In Step 3b (species whose Range and Population Targets are equal to the current value), we calculated Range Target from the area of the most recent IUCN species’ range maps13 within the boundaries of the Member States, while Population Target was set equal to the latest population estimate available in the literature.

In Step 3a (species whose Range and Population Targets are larger than the current value), the targets were set through different hierarchical approaches based on population trends and mainly on data availability. For species with increasing trend and demographic data availability, i.e. typically large mammals, we adopted Population Growth Models. For populations with stable or decreasing trends but potentially increasing in the future, i.e. some populations of large carnivores, or species with different trends but with only information about dispersal and distribution available, such as bats and rodents, we adopted the Range Expansion models. For other species and populations with stable or decreasing trends, we adopted literature information to derive the targets, i.e. the reference-based approach, in alternative or together with the Population Growth Model or the Range Expansion.

For all other cases, when no information was available, we set the target using the gap-filling approach (Fig. 3).

Fig. 3
figure 3

Flow chart of the step 3a. Additional decisional steps to decide the methodology to adopt according to the population trends and the data availability. The more intense the color of the boxes, the more preferred the option method is in the workflow.

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Specifically, targets matched the current value for 49 species, while the Range Expansion approach was applied to 32 species. Population Growth models were implemented for 6 species, and for a single species, Plecotus gaisleri, we adopted the Gap-filling approach. For some species we involved more than one method. The methods adopted for each of them are reported in DATA AND SOURCES of the dataset.

Overall, when available in the literature, we used the minimum and maximum species density to obtain maximum and minimum targets. When no data were available to calculate both targets for population and range size, we derived the missing value from the other either dividing or multiplying by the mean population density of the species.

After that, Step 4 consists in scaling up the Ts obtained at country level to the level of Member State and Biogeographic region, using the species’ habitat as a proxy of population to translate Population Targets, or through the base-map to translate Range Targets (further details are described below).

Model based approaches

Population growth model

For large mammals with increasing trends, we regard this upward trend as crucial for achieving a favourable conservation status. This consideration is based on their recovery following a significant decline in Europe191 and the expectation that their populations will continue to grow through the next few years. Thus, we calculated the rate of population increase and projected it to 2030, based on the observed population growth through time collected from the literature. In particular, this was possible for populatons of Bison bonasus, Canis lupus, Gulo gulo, Lynx lynx, Lynx pardinus and Ursus arctos, whose demographic information was available. We performed generalised linear models in R software (R v4.3.0, R Core team 2021)192 including estimates of annual population size at the Member State level as the explanatory variable and the year in which the population estimate was used as the predictor. We used Poisson regression, or a negative binomial regression when an overdispersion of variance was detected in preliminary analyses. The coefficient of the model can be considered as the instantaneous rate of increase (r), which can be translated into the finite rate of increase (λ) of the population ‘Eq. (1)’:

$$\lambda ={{\rm{e}}}^{{\rm{r}}}$$
(1)

We used different approaches according to the availability of input data (that is, estimates of population size through time):

  1. 1)

    When the input data were overall estimates of population size in the Member State, we extrapolated population size at 2030 based on the computed regression.

  2. 2)

    When the input data were partial (i.e., we found only the number of females with cubs or the minimum verified numbers through the year) or when we knew the trend of transboundary populations but not the trend of the single population in the Member State, we extrapolated the projected population size. We considered the most recent and complete population estimate in the Member State and used rate of increase obtained from the model. Taking into account the number of years from the most recent estimation to 2030, the population size by 2030 was calculated through the formula ‘Eq. (2)’:

    $${{\rm{population}}}_{2030}={\rm{current\; population}}\ast {{\rm{rate\; of\; increase}}}^{{\rm{number\; of\; years\; to\; reach}}20330}$$
    (2)
  3. 3)

    The third case was applied when the population trend was known but the rate of increase could not be calculated because of the lack of a temporal series of population data. In this case, we used an average overall populations’ rate of increase value taken from the literature.

  4. 4)

    Eventually, when the rate of increase was not even found in the literature, we used the mean rate of increase derived from the known rates of increase of populations of the same species in other combinations of Member States-biogeographic regions, bootstrapping the values to optimise the computed mean in light of potential outliers. We reported the λ obtained through the literature, the models or the bootstrapping in the Data sources.

Potential range expansion model

We adopted the Potential Range Expansion Model for species or populations which have experienced a decline but whose trends of distribution and population may potentially increase, e.g. large carnivores with current decreasing or stable trends, and in absence of demographic information, e.g. for Chiroptera species. We elaborated the expanded range starting from the most recent IUCN Red List species’ range map and taking into account the dispersal capacity in time by each species, based on the following relationship ‘Eq. (3)’:

$${\rm{dispersal\; in\; time}}={\rm{dispersal}}\,\left({\rm{km}}\right)\ast {\rm{number\; of\; years\; to\; reach}}\,2030/{\rm{generation\; length}}\,({\rm{years}})$$
(3)

We used the IUCN Red List species ranges maps13 and, ensuring they have remained unchanged since 2022, we projected the range maps 8 years into the future, creating a dispersal distance buffer around the IUCN range using the GRASS function “r.buffer”193. For example, if a species has a dispersal distance, i.e. the distance from the born population that individual or multiple individuals reach, toward another location, or population, where they will settle and reproduce, of 45 km and a generation length of 10 years it could potentially disperse 36 kilometres by 2030. In this case, the RT was set equal to the species range extension obtained by expanding the previous range of 36 kilometres radius. The dispersal distance and generation length information came from the COMBINE database24. In absence of information in COMBINE, we consulted the IUCN Red List Assessments of the species13. For Chiroptera species, when available, we preferred to use data from the most updated database of bats traits in Europe by Froidevaux et al.105.

Reference-based approach

The reference-based approach involves consulting published studies to formulate targets which take into account the habitat suitability models or the population viability analysis. We adopted this approach in alternative or in combination to the population growth or the range expansion models, in case of stable or decreasing population trend, to obtain a minimum target achievable. In contrast to the other model-based methods, it was difficult to obtain 2030-based targets because it was not possible to identify the year for which the models were run in each study.

For instance, the population viability analysis is a quantitative model-based technique that estimates the likelihood of extinction and/or loss of genetic variation by utilising species-specific genomic, demographic, and abundance data. It also accounts for recognized threats to population survival and the growth population model. This method provides the minimum viable population, which is an estimate of the minimum number of individuals194. When we use published population viability analysis to formulate the targets, we provide them per each intersection between Member State and Biogeographic region, taking into account the number and the distribution of the extant populations11. In this case, the Population Target value was found by multiplying the minimum viable population size by the number of extant populations, using the number of populations found in the literature, when available, or assuming that in each Member State-Biogeographic region combination a single population occurs.

Gap-filling method

We applied this method in case of lack of data, when a species is not reported in the IUCN Red List and we do not have information about its presence in Europe, its trends and threats. To find the regional target value, we followed a similar approach to that of Pacifici et al.195 and set the target equal to the average target values obtained for other species belonging to the same Genus and the same Member State-Biogeographic region combination.

Scaling up targets to the level of Member State and biogeographic region

To convert Population Targets obtained from the population model from national to regional values, we used the percentage of species’ habitat within each Member State-Biogeographic region combination as a proxy of population11,196. We assumed that populations are distributed based on available habitat. Thus, we overlapped the maps of available habitat within the geographic ranges of species (Area of Habitat197) with the study area map. We used the percentage of habitat for each biogeographic region out of the total habitat area in each Member State to partition the Population Target values from the population models into values at the biogeographic region scale. Population Target was then transformed into Range Target, using gathered information on mean species population density. In particular, we divided the Population Target by the density value to obtained the Range Target.

To translate the Range Targets coming from the range expansion map into Member State-Biogeographic region values, the expanded ranges were overlapped with the basemap, and the area in square kilometres per each combination was calculated using “r.stats” in GRASS (version 7.8.2)193 to obtain regional targets were then calculated using the density. The same approach was used to obtain Range Target values when the targets were supposed to be equal to the current values, by calculating the IUCN species range area within each combination of the basemap and translated Range Targets into Population Targets through the density.

Data Records

We provide a dataset “EU MAMMALS TARGETS” that includes the targets for population (PT) and range size (RT) of 81 terrestrial mammals listed in Annex II and Annex IV of the Habitats Directive9, in the “TARGETS” sheet, and the data used and the relative resources, in “DATA AND SOURCES” sheet, depositing them in Zenodo198.

RTs are expressed as km2, and PTs as number of individuals. It should be noticed that PTs are not available for all species, because of the lack of demographic information and density data needed to translate RTs into PTs. Each value is reported for each species (using IUCN Red List taxonomy; “Species” column) in each intersection between Member State (“Member state” column) and Biogeographic region (“Biogeo” column). In some cases, we used different population density information obtained from the literature, providing also minimum and maximum values.

PTs and RTs are splitted then in columns based on the approach used and the population density information adopted to translate PT and RT into each other as follows:

PT_CV: population target that is equal to the current population size.

PT_PGM_min: minimum population target obtained using the population growth model (PGM). Minimum refers to the minimum rate of increase obtained from the confidence interval of the model coefficients.

PT_PGM_mean: mean population target obtained using the population growth model(PGM). Mean refers to the rate of increase obtained from the model.

PT_PGM_max: maximum population target obtained using the population growth model (PGM). Maximum refers to the maximum rate of increase obtained from the confidence interval of the model coefficients.

PT_refbased_min: minimum population target obtained using the reference-based approach. Minimum refers to the population value calculated using the lowest population density found in the literature.

PT_refbased_mean: mean population target obtained using the reference-based approach. Mean refers to the population value calculated using the average population density value.

PT_refbased_max: maximum population target obtained using the reference-based approach. Maximum refers to the range value calculated using the highest population density value found in the literature.

PT_rangexp_min: population target obtained from the RT_rangexp value multiplied by the lowest population density of species found in the literature.

PT_rangexp_mean: population target obtained from the RT_rangexp value multiplied by the average population density of species.

PT_rangexp_max: population target obtained from the RT_rangexp value multiplied by the highest population density of species found in the literature.

RT_CV: range target that is equal to the current range size, where CV means “current value”.

RT_PGM_min: minimum range target obtained using the population growth model (PGM). Minimum refers to the range values calculated from the minimum population target (PT_PGM_min).

RT_PGM_mean: mean range target values obtained using the population growth model (PGM). Mean refers to the range values calculated from the average population target (PT_PGM_mean).

RT_PGM_max: maximum range target values obtained using the population growth model (PGM). Maximum refers to the range values calculated from the maximum population target (PT_PGM_max).

RT_refbased_min: minimum range target obtained using the reference-based approach. Minimum refers to the range value calculated using the highest population density value found in literature.

RT_refbased_mean: mean range target obtained using the reference-based approach. Mean refers to the range value calculated using the average population density value.

RT_refbased_max: maximum range target obtained using the reference-based approach. Maximum refers to the range value calculated using the lowest population density value found in the literature.

RT_rangexp: range target obtained using the range expansion method.

RT_gapfilling: range target obtained using the gap-filling approach.

“DATA AND SOURCES”198 sheet includes information on population density, dispersal and generation length. For some species, such as large mammals, for which we performed the population growth models and collected demographic data by country, we also reported the rate of increase obtained from the literature (with the proper reference), from the models or the bootstrapping and provided specific references by each Member State.

Technical Validation

Firstly, we qualitatively validated the data used to set targets through the reference-based approach or to perform the population growth models, excluding approximate estimates simply based on experts’ opinions or observations not obtained by reliable methods. Specifically, we selected data coming from official Environmental Institutions or Ministries or from scientific publications, which was obtained through statistical validation and provided with confidence intervals.

The population growth models were validated mainly using the coefficient of determination (R-square), which determines the proportion of variance in the dependent variable (population size) that can be explained by the independent variable (years). Before regression, we perfomed an overdispersion test. Overdispersion may occur when the observed variance is higher than the variance of a theoretical model. As the data was number of individuals, i.e. count, we adopted Poisson regression as first option. For Poisson models variance increases with the mean and, therefore, variance is approximately equal to the mean value. If the variance was much higher, the data was “overdispersed” and we used the negative binomial regression model. We checked the models for over-dispersion (and under-dispersion) using the function “check_overdispersion” of the “performance” package in R199.The chosen models were reported in Tables 1–5.

Table 1 Results of the statistics of the best population growth models regression between Poisson and Negative binomial for the brown bear (Ursus arctos).
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Table 2 Results of the statistics of the best population growth models regression between Poisson and Negative binomial for the Iberian lynx (Lynx pardinus).
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Table 3 Results of the statistics of the best population growth models regression between Poisson and Negative binomial for the wolverine (Gulo gulo).
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Table 4 Results of the statistics of the best population growth models regression between Poisson and Negative binomial for the wolf (Canis lupus).
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Table 5 Results of the statistics of the best population growth models regression between Poisson and Negative binomial for the Euroasian lynx (Lynx lynx).
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The average R-square resulted for the chosen models for Eurasian lynx (Lynx lynx) is 77%, for wolverine (Gulo gulo) it is 81%, for brown bear (Ursus arctos) it is 84%, for wolf (Canis lupus) it is 85% and for Iberian lynx (Lynx pardinus) it is 97%.

After choosing the best models, we extrapolated the confidence intervals of the models coefficients using the function “confint” and predicted the population size in 2030 (Tables 1–5). In this way, we were able to get confidence intervals and therefore a range of Population Targets based on minimum and maximum rates of increase. Also in the case of increasing population trends but unknown rates of increase, when we bootstrapped the rates of increases from the average values obtained from the other populations, we calculated the standard errors (Δz) (Table 6) associated with the value predicted by 2030 (z) using the ‘Eq. (4)’:

$${\Delta {\rm{z}}}^{2}={({\rm{N}}0/{\rm{N}}0)}^{2}+{{\rm{y}}{\rm{e}}{\rm{a}}{\rm{r}}{\rm{s}}}^{\ast }{(\Delta \lambda /\lambda )}^{2}\ast {\rm{z}}$$
(4)

Where N0 is the current population size with the associated standard error (ΔN0), and \(\lambda \) is the bootstrapped finite rate of increase with the associated error (Δ\(\lambda \)).

Table 6 Results of the bootstrapped λ values with the associated standard errors for the brown bear (Ursus arctos), wolf (Canis lupus) and Eurasian lynx (Lynx lynx).
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In addition, we provided a validation of our estimates by comparing our targets obtained with different methods. The comparison has been done only for the species for which we were able to apply more than one method. For each method, we calculated the mean of target values obtained by each Member State and Biogeographic region combinations and we then compared the average values derived from different methods. As our data did not meet the assumption of normal distribution required for parametric tests such as the t-test, we used the Wilcoxon signed-rank test200.The null hypothesis tested is that the median difference between the mean targets obtained from different methods is zero. It was accepted when the p-value was greater than the critical value. We excluded Population Targets comparisons because the samples (i.e. average Population Targets for Member State-Biogeographic region obtained from the population growth model, from the range expansion model and from the reference-based approach) were too small and the test could not compute exact p-value with ties.

We found that the average Range Targets obtained with different methods were not significantly different, as we obtained p-values > critical value (0,05) from the comparison between Range Targets from the range expansion and the Population Growth models (0,07) and between Range Targets from the range expansion model and the reference-based approach (0,14) (Table 7).

Table 7 Results of the Wilcoxon test, comparing the Range Target (RT) values obtained between the range expansion and the Population Growth models (PGM) and between the range expansion and the reference-based approach.
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Practical use and implications

We provided targets that can be interpreted as the ideally achievable population and range size, in most cases by 2030.Through the Population Growth and Range Expansion models, we established the population and range values that could be reached by 2030 based on projections of exponential population growth and geographical range expansion. However, accurate population projections should also consider demographic details, such as fertility rates, mortality rate, ages structure, and environmental carrying capacity201, while distribution projections should account for future environmental changes202. Therefore, these methods can be applied for 2030 but may not be reliable for long-term predictions.

We applied the Population Growth model to a few species, generally large mammals, for which population size monitoring and demographic data were available for at least five years, as they are considered more charismatic and emblematic, and therefore more studied203. Very often, this information is missing for other species, such as Chiroptera and Rodentia204, for which we were generally able to apply the Range Expansion model. However, the scarcity of population and distribution data, along with limited historical information for these species, made the choice of methods for formulating targets very limited. Alternatively, or in combination with these methods, we used the reference-based approach to provide targets. These targets are based on habitat suitability, potential range, or population viability analysis and can be interpreted as the overall favorable population or range required to sustain the species in the long term, without specifying a target year.

The targets formulated with the above-mentioned methods imply an increase in population and/or range size, setting the foundation for several conservation measures that align with the National and EU Biodiversity Strategy objectives by 2030. These targets may guide conservation efforts focusing on the mitigation of threats or strengthening national and international laws to preserve species and/or limit their harvesting and the exploitation of their habitats. For instance, for large mammals, these targets may be useful in reinforcing policies aimed at combating illegal killings and mitigating human-wildlife conflicts. Moreover, these targets may address the restoration of endangered species’ habitats and other measures to reduce disturbances. For Chiroptera species, this may involve restoring native trees that provide foraging and roosting opportunities, maintaining green spaces, hedgerows, and dark corridors in cities to facilitate bat movement, or reducing light pollution and pesticide use. The targets may also contribute to the expansion of protected areas. They can be expressed as a percentage increase in population or range size, which should then be translated into a corresponding percentage of protected habitat for the species.

On the other hand, when we set the targets equal to the current values (i.e., in cases where a species is already in favorable conditions or where improvements are infeasible due to ecological, social, or economic constraints), this implies that conservation efforts should focus on maintaining the species’ current population and range size. In these cases, the goal shifts from increasing numbers or expanding range to stabilizing the existing conditions and preventing any decline. For instance, in terms of protection targets, this would mean that the proportion species’ range that is currently preserved within the protected areas network should remain constant over time. Ensuring the persistence of these habitats may require continuous monitoring and adaptive management to account for potential threats or changes in environmental conditions that could affect the species’ stability. In practice, such efforts may involve maintaining current habitat quality, preventing habitat fragmentation, controlling invasive species, or managing human activities that could negatively impact the species’ survival.

Further remarks refer to both the use of different approaches and methods, as well as the inclusion of species not listed in the Habitats Directive. By adopting different methods and varying population density data for the same species, we were able to provide a range of targets, making them flexible enough to meet different policy and societal needs. This flexibility makes conservation goals more adaptable and socially acceptable, increasing the likelihood of support from both the public and policymakers. This approach is particularly useful for balancing conflicts between human activities and nature, especially for species that pose significant challenges, such as large carnivores27.

Moreover, in this study, we not only included species listed in the Habitats Directive but also those at risk of extinction, such as certain species in the genus Lepus. While these species may not yet be conservation priorities according to the Habitats Directive, they have the potential to become key targets in the future. By setting specific targets for these species, we emphasize the need to integrate them into conservation strategies, proactively addressing potential risks and strengthening their protection moving forward.