# Exposure and Sensitivity of Terrestrial Vertebrates to Biological Invasions Worldwide

Clara Marino, Boris Leroy, Guillaume Latombe, Céline Bellard

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Clara Marino, Boris Leroy, Guillaume Latombe, Céline Bellard. Exposure and Sensitivity of Terrestrial Vertebrates to Biological Invasions Worldwide. Global Change Biology, 2024, 30 (12), pp.e17607. 10.1111/gcb.17607. hal-04842692

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1 **Title**

2 **Exposure and sensitivity of terrestrial vertebrates to biological invasions worldwide.**

3

4 **Running title**

5 Biological invasion vulnerability assessment.

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7 **Authors**

8 Clara Marino¹,²*, Boris Leroy³, Guillaume Latombe⁴, Céline Bellard¹

9

10 **Affiliations**

11 ¹Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution, 91190,

12 Gif-sur-Yvette, France

13 ²FRB – CESAB, 5 Rue de l’Ecole de Médecine, 34000 Montpellier, France

14 ³Unité Biologie des Organismes et Ecosystèmes Aquatiques (BOREA UMR 8067), Muséum

15 National d’Histoire Naturelle, Sorbonne Universités, Université de Caen Normandie, Université

16 des Antilles, CNRS, IRD, Paris, France.

17 ⁴Institute of Ecology and Evolution, The University of Edinburgh, King's Buildings, Edinburgh,

18 EH9 3FL, UK

19 Corresponding author: Clara Marino

20 Email: claramarino665@gmail.com

21
22 **Abstract**

While biological invasions continue to threaten biodiversity, most of current assessments focus on the sole exposure to invasive alien species (IAS), without considering native species' response to the threat. Here, we address this gap by assessing vertebrates' vulnerability to biological invasions, combining measures of both (i) exposure to 304 identified IAS and (ii) realized sensitivity of 1,600 native vertebrates to this threat. We used the IUCN Red List of Threatened Species to identify species threatened by IAS, their distribution, and the species' range characteristics of their associated IAS. We found that 38% of worldwide terrestrial lands are exposed to biological invasions, but exposure alone was insufficient to assess vulnerability since we further found that most of the world hosted native species sensitive to biological invasions. We delineated areas highly vulnerable to biological invasions, i.e., combining areas of high exposure and high sensitivity to IAS, located in Australia and coastal states of North America with a high confidence level, but also - depending on the group - in Pacific islands, Southern America, Western Europe, Southern Africa, Eastern Asia, and New-Zealand with a medium confidence level. Assessing the completeness in exposure data, we revealed strong biases in the global description of the well-known invasion hotspots, with limited areas being assessed with a medium to high confidence level. The completeness of sensitivity was overall very high, for the three studied taxonomic groups. We also demonstrated that coldspots of vulnerability to biological invasions were areas of low confidence in terms of data completeness, which coincided with biodiversity hotspots. There is thus a critical need to address these knowledge shortfalls which jeopardize efficient conservation initiatives, regarding the threats to well-known vertebrate taxa.
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**Introduction**

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Invasive alien species (hereafter, IAS) continue to threaten biodiversity (Purvis et al., 2019).

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More than a quarter of threatened terrestrial vertebrates assessed by the IUCN are in peril

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because of biological invasions (Bellard, Genovesi, et al., 2016), with over 3,500 IAS

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documented worldwide according to the last IPBES report (IPBES, 2023). During COP15 in

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2022, a new set of targets to reverse biodiversity loss by 2030 was promulgated, including a

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specific target dedicated to the mitigation of IAS (Secretariat of the United Nations Convention

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on Biological Diversity, 2021). Expressly, 196 countries agreed to decrease by 50% the

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introduction and establishment of IAS around the world and to reduce their impacts. To achieve

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this, there is a need to determine where the consequences of IAS could be the highest to identify

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spatial priorities.

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The potential spread of IAS and their associated anthropogenic causes has been explored already

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(Bellard, Leroy, et al., 2016; Bradley et al., 2019; Early et al., 2016; Latombe et al., 2023; Pyšek

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et al., 2010). For instance, Early et al. (2016) mapped different spatial indicators related to IAS,

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such as international trade or environmental disturbance, whilst other studies have documented

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the potential distribution of invasive and established alien species (Bellard et al., 2017; Dawson

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et al., 2017; Van Kleunen et al., 2015) and identified future climatic suitable areas of IAS

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(Bellard et al., 2013; Li et al., 2016; Pili et al., 2020; Polaina et al., 2021). Despite improvements

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in geographic predictions of invasions, those studies only indicate the potential exposure to IAS

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presence. However, the presence of an alien species in an area – even for species with a history

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of impacts elsewhere – does not necessarily mean impacts on native communities. In parallel,

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recent studies explored areas where native species are more sensitive to biological invasions,

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highlighting hotspots of species that are extinct or threatened because of biological invasions

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(Bellard, Cassey, et al., 2016; Bellard, Genovesi, et al., 2016; Evans et al., 2021; Harfoot et al.,
2021). Yet, efforts to assess the threat posed by IAS are currently limited either to the exposure of native communities to known IAS or to the sole sensitivity of native species to this threat, but fail to consider both components within the same spatial framework. Because biological invasion impacts are context-dependent, and depends on alien species characteristics but also native species features and environmental conditions (Pyšek et al., 2020), it is crucial to assess the threat posed by IAS accounting for both exposure to threat and sensitivity to the threat.

Vulnerability assessments (VA) integrate both intrinsic (species sensitivity) and extrinsic (species exposure) factors related to species vulnerability, permitting a detailed description of what could imperil a system facing a threat (Foden et al., 2019). VA allow to determine areas the most at risk of a given threat, enabling us to define priority zones for conservation purposes (Ameca y Juárez et al., 2012). Yet most existing VA have focused on climate change (Butt et al., 2016; de los Ríos et al., 2018; Foden et al., 2019; Leclerc et al., 2020), and none has been conducted specifically for biological invasions (but see (Butt et al., 2022) for multiple stressors).

Here, we introduce a biological invasion vulnerability assessment framework (BIVA framework) that combines measures of (i) exposure to IAS threats and (ii) realized sensitivity of native species to this threat (Fig. 1). Exposure describes the level of biophysical changes induced by the threat, while sensitivity represents the realized susceptibility of the system's components to harm. Inspired by Parker et al.'s impact formula (Parker et al., 1999), we define exposure to IAS threats as the combination of three metrics: (i) the documented taxonomic richness of alien species, (ii) the area of their global alien range, and (iii) their impact at a given location. We further define the realized sensitivity to IAS at a location as the number of native species that are documented to be affected by biological invasions. We apply the BIVA framework to global terrestrial vertebrates and we assessed areas vulnerable to biological invasions by combining the
distribution of 1,614 native birds, mammals, and reptiles with their associated 304 IAS. Finally, we assess the uncertainty of final vulnerability by evaluating the data completeness for exposure and sensitivity.

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## 96 Materials and Methods

### 97 Biological Invasion Vulnerability Assessment (BIVA) framework

The BIVA framework is designed to estimate the vulnerability associated with biological invasions worldwide (Fig. 1). Here, we define exposure to biological invasions as the level of IAS threat that may occur at a location, i.e., the potential impacts IAS may have at this location based on known impacts. To measure exposure, we summed three metrics reflecting the presence and characteristics of IAS in this location: IAS richness, median alien range size, and alien impact breadth (Fig. 1A). IAS richness scales with introduction effort and colonization pressure, thus reflecting how much an area is subject to alien introductions, but also has environmental conditions allowing introduced alien species to establish viable populations and spread (Blackburn et al., 2016; Dawson et al., 2017). Moreover, species with a larger global alien range would have greater invasive capacities and thus represent a greater threat to native systems than alien species established in a limited range of locations (Bernery et al., 2023). Finally, alien species differ in their ability and strength to impact native biota (Bradley et al., 2019; Doherty et al., 2016). Thus, the number of native species an alien species is known to impact globally informs on the possible alteration the IAS can have on a biotic system. Here, we consider alien species have an impact on native species if an impact is reported in the IUCN Red List, which indicates if they impair the way native species can thrive, causing a stress on the ecosystem, a

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direct stress (e.g., causing mortality), or an indirect stress (e.g., competition with the native species) on the species (IUCN & CMP, 2012).

In parallel, we defined realized sensitivity to IAS as the species' intrinsic response to the threat, using the number of species documented to have at least one population affected by one or several IAS according to the IUCN Red list (Fig. 1A). Native species are exposed to many different threats globally, however their response to threats depends on many factors, including intrinsic characteristics like traits (Chichorro et al., 2019). We considered that a location containing many species impacted by IAS somewhere in their range would likely show a high sensitivity to this threat. Sensitivity makes two major assumptions: (i) IAS-impacted species are sensitive in their whole range; (ii) only documented affected species by IAS are sensitive.

Finally, the overall vulnerability to biological invasions at a given location was the combination of both exposure and sensitivity to IAS, and defined in five categories: very low, low, medium, high, and very high (Fig. 1C).

## BIVA of terrestrial vertebrates

We performed the biological invasion vulnerability assessment of terrestrial vertebrates for units of equal-area cells of 110 km resolution (dimension of 1° cells at the equator) for terrestrial lands in the Mollweide system of projection (n = 17,257 cells in total). We chose this resolution as the best compromise between clarity of the visualization of results, computing time, and data quality (preliminary analyses at a finer resolution generated similar results).

*Studied native and alien species.* We identified all native birds, mammals, and reptiles that are known to be affected by IAS in at least some part of their distribution range thanks to the IUCN Threats Classification Scheme (version 3.2), as well as the specific IAS species involved when
available (IUCN, 2022). We extracted the list of IAS species, removing taxa not identified at the species level (e.g, "Unspecified Rattus" were not considered) and life-forms requiring a host to spread (i.e., removing bacteria, parasites, pathogenic fungi, and viruses). We collected the occurrences of the selected IAS from GBIF, using the *occ_download* function from the *rgbif* package (Chamberlain et al., 2023). Then, we cleaned the downloaded occurrences with the *clean_coordinates* function from the *CoordinateCleaner* package (Zizka et al., 2019). We removed occurrences that had no informed coordinate uncertainty, and kept all the occurrences with an uncertainty below 55 km, i.e., half the resolution of our study. Finally, we selected all the occurrences that fell into the non-native range of each IAS. To do so, for IAS with a native range described in the IUCN Red List, we removed occurrences falling into their native range. For IAS not assessed by the IUCN Red List, we kept all the occurrences occurring in the introduced areas, defined with alternative sources (details in Supplementary Material). We ended up with a total of 304 IAS with at least one occurrence outside their native range, with a coordinate uncertainty below 55km, and 1,614 native species for which IAS were cited as a threat.

*Exposure metrics.* To calculate the exposure to invasion threat, we combined information related to IAS occurrences and characteristics at the cell level. First, we calculated alien species richness as the number of alien species that have at least one exotic occurrence in the cell. Second, for each IAS in our list, we calculated its alien range size as the total number of equal-area cells where the alien species has at least one occurrence, out of its native range. Median alien range for a given cell was calculated as the median alien range size of all IAS occurring in the cell. Third, we extracted the total number of native species impacted by each IAS present in the cell from the IUCN Red List. This was compiled for each class separately (birds, mammals, and reptiles). We calculated median impact breadth as the median number of natives impacted by all IAS occurring in the cell.
161 *Sensitivity metric.* We defined the realized sensitivity to IAS of each cell as the number of native
162 species impacted by at least one of the 304 IAS, occurring in the cell. We collected the native
163 range of the 1,614 mammal, bird, and reptile species from Birdlife International for birds
164 (BirdLife International & Handbook of the Birds of the World, 2020) and from the IUCN for
165 mammals and reptiles (IUCN, 2022). Sensitivity was calculated for each taxonomic group
166 separately as the number of native species whose range intersected with each cell. Globally, 668
167 birds, 292 mammals, and 654 reptiles were impacted at least by one of the 304 IAS and occurred
168 in 16,187 out of the 17,257 cells evaluated.

169 *Vulnerability to biological invasions.* We normalized the exposure and sensitivity components
170 using three methods: max-min rescaling, natural log before max-min rescaling, and cumulative
171 ranking (Fig 1B, SI Text) (Leclerc et al., 2020). All normalizations resulted in metrics ranging
172 between 0 and 1, but the log transformation minimized the influence of extremely high values
173 while cumulative ranking resulted in a uniform number of values for any level of values. For
174 exposure, we normalized the three metrics, summed them, and normalized the final obtained
175 metric with a max-min rescaling. As sensitivity had only one component, we simply applied the
176 three normalizations. Robustness analyses demonstrated that exposure and sensitivity metrics
177 were consistent for the three alternative normalization methods (Spearman's coefficient > 0.60
178 for exposure and > 0.88 for sensitivity, Table S2, Fig. S6). We thus present results for the max-
179 min normalization (but see Figs. S7 to S10 for results with other normalization methods).

180 Exposure and sensitivity values were then separated into three quantiles (i.e., low, medium, and
181 high values) using the *bi_class* function with the "fisher" method of the *biscale* package (Prener
182 et al., 2022). Vulnerability to biological invasions is the result of the combination of exposure
183 and sensitivity and is defined by five classes ranging from very low to very high (Fig. 1C). Zones
184 of very high vulnerability are the cells of high value for both exposure and sensitivity, following
185 the conceptual model of (Ameca y Juárez et al., 2012).

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187 *Uncertainty assessment*
188 Both exposure and sensitivity were obtained by collecting data from large scale datasets (i.e.,
189 GBIF, IUCN Red List) that are spatially and taxonomically biased (Bachman et al., 2019;
190 Bowler et al., 2022). We propose a metric of completeness that considers, at the same time, the
191 *sampling effort* for detecting the presence of species and the *effective detectability* of IAS threat
192 (Fig. 1D), applicable to both alien and native species. *Sampling effort* depicts research effort,
193 while *effective detectability* accounts for the capacity to detect the threat caused by IAS in a
194 given place.

195 For exposure, we first extracted the density of GBIF records per cell of 110 km for terrestrial
196 vertebrates from (Meyer et al., 2015). Then, we log-transformed and normalized the density to
197 obtain the *sampling effort* in GBIF that ranged between 0 (no GBIF record for terrestrial
198 vertebrates in the cell) and 1 (maximum density of species records in the world). Second, we
199 used the Global Register of Introduced and Invasive Species (GRIIS) checklists to assess how
200 much our data effectively detect the presence of known alien species in a given country (Pagad
201 et al., 2022). Those checklists are based on regional or local inventories and should reference all
202 IAS present in a given administrative unit. For each administrative unit (n = 267), we calculated
203 (i) the number of species referenced in GRIIS but not in our database, (ii) the number of species
204 referenced in our database but not in GRIIS, and (iii) the number of species present in both
205 GRIIS and our database. *Effective detection* was the number of species present in both lists
206 divided by the total number of species in the two lists.
207 Sensitivity data were based on IUCN Red List assessments, containing some Data Deficient (DD) species for their conservation status, or lacking information regarding IAS threat.
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209 Completeness for sensitivity accounted for (i) the information on the conservation status by the IUCN and (ii) the ability to describe precisely the threat caused by IAS on native species. First, we collected the extinction risk and the native range of all birds (n = 10,965), mammals (n = 5,755) and reptiles (n = 9,776) from the IUCN and calculated *sampling effort* for each group at the cell level as the percentage of species with a documented conservation status. Second, *effective detection* was based on the 1,614 native species for which IAS were cited as a threat and corresponded to the proportion of species with documented information on the scope and severity of IAS threat.
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217 Final completeness was the product of *sampling effort* and *effective detection*. Completeness was calculated for all 304 IAS together for exposure, but separated by taxonomic group for sensitivity.
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220 Lastly, we applied cutoff values for completeness to display the final vulnerability results with increasing confidence levels. For each completeness (exposure and sensitivity), we defined a cutoff at 1/9, ensuring that *sampling effort* > 1/3 and *effective detection* > 1/3 (hereafter, medium confidence level) and a cutoff at 4/9 (hereafter, high confidence level) for which *sampling effort* > 2/3 and *effective detection* > 2/3. The confidence level for final vulnerability is medium (high) when both exposure and sensitivity have a medium (high) confidence level.
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226 All analyses were performed with R (R Core Team).
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## Results
We found that 38% of terrestrial surfaces with the studied native vertebrates were exposed to biological invasions worldwide. Native birds were exposed to 212 IAS while mammals were exposed to 75 IAS and reptiles to 119 IAS. Spatially, birds were slightly more exposed to IAS (36% of areas with native birds contained at least one alien occurrence) than reptiles (27%) and mammals (25%). Hotspots of exposure were spatially congruent across the three taxonomic groups, with North and Central America (including Hawaii Islands), Western Europe (including Azorean and Canary Islands), South-Eastern Africa, India, Eastern Australia, and New Zealand as the main areas (Fig. 2A). 94% of terrestrial lands contained at least one species qualified as sensitive to one of the 304 IAS (92% for birds, 76% for mammals, and 25% for reptiles). Most of the mainland coastal areas and islands showed high values of sensitivity, but there was a strong spatial divergence between taxa (Fig. 2B). For instance, highly sensitive areas for birds included most Pacific islands and coastal areas of Western North American and European countries, as well as coastal areas of South Africa, Australia, Tasmania, and New Zealand. By contrast, the highest sensitivity values for mammals were concentrated in coastal areas of Australia. For reptiles, Caribbean islands and Japan also showed high sensitivity values in addition to Northern and Eastern Australia. Sensitivity scaled with total native species richness for the three groups, but hotspots of sensitivity remained consistent while corrected by total species richness (Fig. S1, SI text).

By combining exposure and sensitivity (Fig. 1C), we assessed the overlap between both markers of vulnerability to biological invasions for each taxon (Fig. 2C). Very high vulnerability zones (i.e., overlap of high sensitivity and high exposure) for birds mostly occurred in coastal areas (e.g., North and South America, South Africa, Australia, and North-East Asia), in addition to
many oceanic islands, New-Zealand, and some places in Europe. Mammals and reptiles displayed similar patterns of vulnerability, with very high vulnerability in Eastern Australia, and high vulnerability in North America and Southeastern Asia. Mammals also had high vulnerability in Argentina, African rift, and Madagascar, while reptiles had high vulnerability in Pacific and Caribbean islands. Very low vulnerability zones (i.e., low sensitivity and low exposure), which also coincided with zones of low completeness (Fig. 3), were similar between the three taxa and included latitudes above both polar circles, the Amazonian region, most of Africa, and Central Asia.

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## Uncertainty in vulnerability components

We measured uncertainty separately for exposure and sensitivity by assessing data completeness from information on sampling effort and effective detectability of IAS threat. Exposure completeness ranged from 0 to 0.73, with sampling effort ranging from 0 to 1 and effective detection from 0 to 0.77. Locations with the highest completeness for exposure were Hawaii, the Galapagos islands, French Polynesia, North America, Western Europe, Australia, and New Zealand (Figs. 3A, S2). The lowest completeness values were located in North Andes, most of Africa, Central Asia, Siberia, and Antarctica, with more than 50% of areas with null completeness, and thus the highest uncertainty related to vulnerability to biological invasions (Fig. S2). Exposure was positively correlated with completeness (Pearson's correlation coefficient = 0.33, p < 0.001), especially for mammals and reptiles (Fig. 3B). Yet, we found zones of medium to very high vulnerability for birds in central Russia despite the very low completeness in this country (Figs. 2C, 3A).

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Sensitivity had a much higher completeness than exposure. Birds showed no spatial variation in sensitivity completeness (Fig. 3C), this group being almost completely assessed for conservation status (>99%) and IAS threat (>97%). The conservation status of mammals and reptiles was assessed for 86% and 85% of their respective groups, but only 43% and 69% were correctly informed regarding the IAS threat, respectively. Zones of low completeness for sensitivity included North-West America, the Amazonian region, Europe, Central Asia, India, and Indonesia for mammals, and Central and South America for reptiles (Figs. 3D, 3E). For reptiles those zones were associated with a low vulnerability, but, for mammals, areas like North America, Western Europe and India harbored a median to high vulnerability, mostly because of a high exposure (Fig. 2).

Applying thresholds of data completeness for both exposure and sensitivity revealed an important spatial variation of vulnerability values for the three taxa. For instance, we observed that vulnerability can be assessed with very high confidence only in few areas, including Southeastern Australia and Tasmania for the three taxa as well as some restricted coastal areas in North America for birds, mammals and reptiles (Fig. 4). A less conservative value for completeness cutoff suggested that most of North America, Central America, Europe, Australia, restricted areas of India and Eastern coasts of Africa were vulnerable to biological invasions (Fig. 4B). By contrast, most of the Pacific and Caribbean islands, as well as Africa and Asia tend to have completeness below the lower cutoff value. (Figs. 2A, 2B, 4A). Overall, confidence in vulnerability was limited by poor completeness in exposure (Fig. S3) while patterns of sensitivity were very similar even when removing areas of low confidence, especially for birds (Fig. S4).

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Discussion

We found that 38% of terrestrial surfaces with native vertebrates contained at least one of the 304 IAS known to impact them. This proportion is very high yet it remains a gross underestimate considering that we assessed exposure to less than 10% of the 3,500 documented IAS worldwide (IPBES, 2023). The vast majority of terrestrial lands contained vertebrate species qualified as sensitive to one of these 304 IAS. Note that species might not be sensitive to IAS in their whole range, but we were not able to distinguish, within the spatial range, which part was actually sensitive to IAS. For birds, we identified expected vulnerability hotspots on oceanic islands, and, notably, revealed previously overlooked high vulnerability of mainland coastal areas to IAS (Dawson et al., 2017). For mammals and reptiles, we found high vulnerability in East Australia and in multiple other continental and oceanic areas.

By contrast, we found that many tropical areas in South America, Africa, Central Asia, and Indonesia were areas of low vulnerability to biological invasions. However, this was most likely due to low completeness for both exposure and sensitivity, especially for mammals. These areas are potentially false negative because knowledge on IAS is insufficient (Bellard & Jeschke, 2015). They should therefore be considered as “Data Deficient” until further knowledge is acquired. In this regard, our global assessment is a conservative estimate of the overall vulnerability of the world to biological invasions, given the lack of data in inaccessible and understudied regions of the world (Feeley et al., 2017). This lack of data is likely reinforced by linguistic barriers: most of GBIF data rely on English language, and a broader inclusion of other languages would increase the scientific knowledge on biological invasions (Angulo et al., 2021).

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Assessing global exposure to IAS
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Patterns of exposure revealed that more than one quarter of lands containing native tetrapods were exposed to IAS threats. This proportion is likely underestimated given that many areas had incomplete data for IAS occurrences, leading to no exposure. Plus, among areas with exposure values, many had a low completeness, inducing only a few areas like North America, Argentina, Western Europe, Southeastern Africa, Australia, and New Zealand with medium to high confidence in their exposure (Fig. S3). Overall, hotspots of exposure complemented previous studies on global alien distribution (not only restricted to IAS). Invasion hotspots, identified based on alien species richness and drivers of IAS richness, are mainly located on oceanic islands and continental coastal countries, with a predominance of North and Central America, Europe, South Africa, South-East Asia, and Oceania (Dawson et al., 2017; Early et al., 2016). Yet, those identified hotspots of invasions also suffer from low completeness (Fig S3), and only small areas in Australia and North America had very high completeness values. This result revealed important biases in our knowledge of global exposure to biological invasions due to the lack of data. However, because we included additional information on IAS besides their richness, our exposure index accounts for the impacts of IAS present at a location and their ability to spread (Fig. S5), and enabled us to also identify inland North America or India as zones exposed to IAS threats. This supports the importance of integrating multiple metrics for assessing exposure to threats in vulnerability assessments, as already applied for climate change (Leclerc et al., 2020) or advocated to assess IAS impact (Parker et al., 1999). For instance, Latombe et al. (2022) proposed a way to measure IAS impacts for multiple species on a system by generalizing the impact formula designed by Parker et al. (1999), where the total impact of a given IAS would be the product of its range size, local abundance, and per-unit effect. However, such a framework is not yet feasible at the global scale because of knowledge shortfalls and is more applicable at a local scale. Our BIVA framework follows the same principles as the
345 original impact formula, but it is more easily applicable at larger scales, therefore providing an
346 overall picture of vulnerability to biological invasions.

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348 *Sensitivity unveiled previously unidentified hotspots of vulnerability to invasions*

349 Considering each taxon separately revealed sensitivity hotspots never identified before (Bellard,
350 Genovesi, et al., 2016). Our results confirmed the previous geographic patterns of native species
351 impacted by IAS, with islands being more sensitive than mainland, especially for birds (Russell
352 et al., 2017). However, we found that coastal areas had high sensitivity and thus high
353 vulnerability for native birds, with a high confidence given the good data completeness for bird
354 sensitivity (Fig. S4). Although it is acknowledged that coastal areas are more prone to species
355 introductions than interior mainland locations (Van Kleunen et al., 2015; Xu et al., 2012), the
356 global geographic distribution of native species associated with IAS threat confirms that these
357 introductions induce strong ecological impacts on avian communities.

358 Further, we found that mammal sensitivity was highest in South-Eastern Australia, supported by
359 a high completeness, thus a high confidence level (Fig. S4). This contrasts with spatial patterns
360 of globally endangered mammals, which are concentrated in tropical areas (Ceballos & Ehrlich,
361 2006; Jenkins et al., 2013). Tropical areas (including the Amazonian basin, Central Africa, and
362 Southeast Asia) were even invasion sensitivity coldspots in our study (Figs. 2B, S1). However,
363 the tropics also suffer from low completeness except for African mammals, possibly blurring the
364 real sensitivity of mammals to IAS in the tropics (Chong et al., 2021). Australia was also an area
365 of high sensitivity for reptiles facing IAS, again contrasting with hotspots of globally threatened
366 reptiles that include the Andes, the Caribbean, West Africa, Madagascar, and Southeastern Asia
367 (Cox et al., 2022). Contrary to mammals, these regions were of high completeness for reptiles
368 (Figs. 3E, S4), thus confirming these discrepancies are not due to a lack of data but rather to the
369 specificity of IAS threat compared to other global change anthropogenic pressures.

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371 Assessing uncertainty is as important as assessing vulnerability

372 Exposure completeness revealed that well-known hotspots of invasions are spatially biased.
373 Current exposure highlights wealthier countries of the globe, where intensive research programs
374 have been conducted for a long time and where the threat caused by IAS motivates stakeholders
375 to act to improve knowledge (Chiu et al., 2023). This is even more evident when applying
376 cutoffs on data completeness, as the only zones with medium to high confidence levels were
377 North America including Hawaii islands, Western Europe and North Atlantic islands, localized
378 areas in South America and Asia, South Africa, the East African rift, Australia, Tasmania, and
379 New Zealand (Fig. S3). Yet, even for those areas, we could not reach a very high confidence
380 level in the results due to a lack of data for exposure. Alien occurrences are still hardly collected
381 and standardized, remaining scarce for most taxa, in spite of recent advances on a global scale
382 (Seebens & Kaplan, 2022). Thus, the Wallacean shortfall (i.e., lack of knowledge about species
383 distribution (Hortal et al., 2015)) is also verified for alien species. It is even amplified, because in
384 addition to bias in survey effort, interest in biological invasion threat is country-dependent. For
385 example, Australia and New-Zealand were the hotspots of highest exposure and also the regions
386 of highest completeness. This is not surprising given the tremendous research efforts on threats
387 to Australian and Novozealandic biota, ranking alien species as the top cause of population
388 declines (Kearney et al., 2019; Simberloff, 2019). Interestingly, the regions of highest
389 completeness for sensitivity differed from regions of high exposure completeness, pointing out
390 the differences in current knowledge and research effort between native and alien species (Figs 391 S3, S4).

392 Besides the lack of completeness in IAS distribution data, BIVA also faces biases regarding 393 interactions between IAS and native species. This could refer to the Eltonian shortfall, i.e., the 394 lack of knowledge about interactions among species (Hortal et al., 2015). Compared to terrestrial 395 vertebrates, whose threats are well recorded in the IUCN Red List, information is scarce for 396 invertebrates or plants (Bachman et al., 2019), which are nonetheless highly impacted by 397 biological invasions (C. Bellard, Cassey, et al., 2016). Moreover, the IAS mentioned in the 398 IUCN Red List are only a subsample of all the IAS that effectively affect native species, leading 399 to an underestimation of total exposure and sensitivity. Including other databases containing IAS 400 impacts on a global scale, such as the Environmental Classification of Alien Taxa (EICAT) 401 (Kumschick et al., 2023), could help in identifying the native species associated with specific 402 IAS. However, existing EICAT assessments are far from complete, even for major alien taxa, 403 and require large data and human resources to be implemented (Volery et al., 2020), limiting 404 their use in global BIVA. Profiling potential sensitive species using known characteristics that 405 make them more vulnerable to IAS such as an herbivorous diet for mammals, an animal-based 406 diet for birds, or a ground-level foraging strategy for insular birds, mammals and reptiles 407 (Marino et al., 2022), could be a way to identify potential vulnerability. Thus, as for climate 408 change vulnerability assessments, a mechanistic BIVA framework would benefit from the 409 inclusion of species traits (Butt et al., 2022; Pacifici et al., 2017). Although data quality might be 410 lacking at global scale, BIVA can also be conducted at much lower resolution on specific 411 systems.
Finally, the measure of completeness itself was also biased by data availability and knowledge on biological invasions. We advocate for a better consideration of data completeness and associated uncertainty in global vulnerability assessments, which obviously suffer from biodiversity knowledge shortfalls. Although impossible to avoid when conducting global scale studies, bias in data must be identified, if possible measured, and used to correct or at least interpret results. The implementation of cutoffs for defining confidence levels could provide conservative results, useful for conservation stakeholders or decision-makers. This approach allowed us to quantify the potential data gaps and ensure that our exposure and sensitivity metrics are used with caution, preventing incorrect interpretations in areas where data are lacking. We emphasize the urgency to gather data on understudied systems and taxa, especially in biodiversity hotspots that are data deficient regarding the IAS threat, even for well-known vertebrate groups.

424
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639
640

641 Acknowledgments

642 Many thanks to Lise Comte, Wilfried Thuiller, and Ana Rodrigues for interesting discussions on
643 the project. CM was supported by a PhD grant from the ENS-PSL and, lately, by the RIVAGE
644 project funded by the CESAB of the French Foundation for Research on Biodiversity (FRB). CB
645 and BL were funded by their salaries as French public servants.

646

647 Author contributions:

648 Conceptualization: CM, CB
649 Methodology: CM, BL, GL, CB
650 Investigation: CM

651 Visualization: CM

652 Supervision: CB

653 Writing—original draft: CM

654 Writing—review & editing: CM, BL, GL, CB

655

656 **Competing interests:** Authors declare that they have no competing interests.

657

658 **Data and materials availability:** The derived data that support the findings of this study are
659 openly available from Zenodo at https://doi.org/10.5281/zenodo.14235025. Alien occurrences
660 data were downloaded from the Global Biodiversity Information Facility (www.gbif.org). All
661 DOIs associated with download requests on GBIF can be found in the Zenodo repository
662 (GBIF_download_doi.xlsx file). The exotic and native ranges of alien species used to filter the
663 occurrences were derived from multiple sources (see Table S1) including the IUCN Red List of
664 threatened species (Version 2022-1, https://www.iucnredlist.org/resources/spatial-data-
665 download), the Global Register of Introduced and Invasive Alien Species (www.griis.org), the
666 Global Assessment of Reptile Distributions (Version 1.1, https://doi.org/10.5061/dryad.83s7k),
667 Birdlife International (Version 2020.1, https://datazone.birdlife.org/species/requestdis), and the
668 CABI compendium in Invasive Species (https://www.cabidigitallibrary.org/product/qi). Details
669 on the sources associated with each alien species are provided in the Zenodo repository
670 (Alien Species range sources.xlsx file). Native species ranges were accessed using the IUCN
671 Red List of threatened species (Version 2022-1, https://www.iucnredlist.org/resources/spatial-
672 data-download) for mammals and reptiles, and Birdlife International (Version 2020.1,
673 https://datazone.birdlife.org/species/requestdis) for birds.
A. Definition of the components

<table><thead><tr><th></th><th>Metric & definition</th><th>Hypothesis</th></tr></thead><tbody><tr><td rowspan="3">Exposure</td><td><strong>Alien species richness</strong><br>Total number of alien species occurring in a location</td><td>An area containing a high number of alien species is more exposed to new species introductions and to alien species future impacts.</td></tr><tr><td><strong>Median alien range</strong><br>Alien range size: surface area of invaded range by an IAS<br>Metric: median alien range size of all IAS occurring in a location</td><td>Species with a larger alien range have higher invasive capacities and can represent a bigger threat to native systems than alien species established in a limited range of locations.</td></tr><tr><td><strong>Median alien impact breadth</strong><br>Alien impact breadth: number of native species impacted by an IAS<br>Metric: median impact breadth of all IAS occurring in a location</td><td>A system exposed to an IAS which impacts a large number of species may have a higher exposure to this threat, compared to a system exposed to an IAS that impacts only a few species.</td></tr><tr><td>Sensitivity</td><td><strong>Native species impacted by IAS</strong><br>Total number of IAS-affected native species (per taxonomic group) in a location</td><td>A system containing many species already threatened by IAS is likely to be more sensitive to IAS than a system where native species are currently not known to be threatened by IAS.</td></tr></tbody></table>

B. Metrics calculation and scaling

<figure><img src="image_3.png" /></figure>

C. Vulnerability to BI

<figure><img src="image_4.png" /></figure>

D. Uncertainty assessment

<table><tr><td>Completeness =</td><td colspan="2">sampling effort x effective detection</td></tr><tr><td>Exposure</td><td>Density of GBIF records for vertebrates</td><td>% of alien species detected in GBIF compared to GRIIS</td></tr><tr><td>Sensitivity</td><td>% of species with informed conservation status in the IUCN Red List</td><td>% of species fully informed regarding IAS-threat in the IUCN Red List</td></tr></table>

675

**Figure 1. Biological invasion vulnerability assessment (BIVA) framework.** (A) Description of the vulnerability components, rationale, and hypotheses behind them. (B) Exposure is the sum of three metrics related to IAS occurrences and characteristics, while Sensitivity is composed of

676

677

678
679 one metric related to native species. (C) Final vulnerability is the combination of exposure and sensitivity, ranging from very low to very high according to the values of each component. (D)
680
681 Uncertainty related to metric calculation was assessed, to account for biases in large scale datasets we used. See Table S1 for the sources associated with data used for calculating the metrics.
682
683

684
<figure><img src="image_5.png" /><figcaption>Figure 2. Actual (A) exposure, (B) realized sensitivity, and (C) vulnerability of terrestrial vertebrates to biological invasions. Each row corresponds to the taxonomic class of native species (from top to bottom: Aves, Mammalia, and Reptilia).</figcaption></figure>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 3.** Completeness of exposure and sensitivity to biological invasions for terrestrial vertebrates. (A) Cartogram of total exposure to IAS (color scale) given the completeness (size of polygons). Exposure completeness is the product of the current knowledge of exotic species occurrences per administrative unit and the sampling effort. The larger the polygon, the higher the completeness. Note that maximal completeness is around 0.8 and never reaches one. (B) Correlation between exposure and completeness for the three different classes of native species. Cartogram of total sensitivity to IAS (color scale) given the completeness (size of polygons) for</figcaption></figure>
699 (C) birds, (D) mammals, and (E) reptiles. Sensitivity completeness is the product of the sampling
effort and the effective detection of IAS threat. The larger the polygon, the higher the
completeness.

702

703
<figure><img src="image_10.png" /><figcaption>**Figure 4.** Vulnerability of terrestrial vertebrates to biological invasions according to **confidence level**. Vulnerability ranges from very low (light red) to very high (dark red), with a (A) low, (B) medium, and (C) high confidence level. Gradual red colors depict values for which completeness is above the cutoff value for both exposure and sensitivity; grey cells represent zones with a null exposure or sensitivity as well as zones for which completeness is below the cutoff value for exposure, sensitivity, or both of them.</figcaption></figure>