Can protected areas and habitats preserve the vulnerable Predatory bush cricket Saga pedo?

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Leonardo Ancillotto, Rocco Labadessa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2608539/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jun, 2023 Read the published version in Journal of Insect Conservation → Version 1 posted 3 You are reading this latest preprint version Abstract Inconspicuous species challenge conservationists when it comes to delineate long-term conservation planning or assess their status, particularly when their actual distribution is poorly known. Invertebrates in particular feature among the less represented taxa in conservation assessments. Here we follow a multidisciplinary approach for assessing the conservation coverage and address future management of the threatened orthopteran Saga pedo across Europe, shedding light on its ecological preferences and associations with protected habitats at continental and regional scales. By developing species distribution models and assessing coverage by Natura2000 and Nationally Protected Areas networks, we found that only 31% of suitable areas is currently protected across Europe, a proportion significantly higher when using occurrences instead of potential range. At regional scale, we disclose that different legally-protected dry grassland habitats increase more the species’ suitability than non-protected grasslands, yet differently-listed habitats do not equally contribute to such increase i.e., not all habitats represent an effective tool for the species’ conservation. Taken together, our results provide an effective framework for addressing knowledge gaps and evaluate the conservation coverage not only of our target species, but more in general of poorly investigated species, at the same time pointing at the urgent need of transnational, coordinated, and increased efforts in monitoring and conserving insects, particularly in the case of threatened species. gap analysis habitats directive insect conservation Orthoptera Saga pedo species distribution modeling Figures Figure 1 Figure 2 Figure 3 Introduction Protected areas (PAs, hereafter) are a key instrument for conserving nature, and are in fact one of the main pillars of both global and continental-scale conservation tools. Europe stands out, in terms of wildlife conservation, by being the world area with the highest numbers of PAs (Gaston et al. 2008). Such primate strongly relies on the Natura 2000 network (N2K, hereafter), one of the two main tools, together with species protection regime, implemented by EU countries in application of the Habitats Directive (HD, hereafter). The latter is also paired by the network of Nationally protected areas (NPAs, hereafter) such as National Parks, Nature reserves and so on. Evaluating the success of PAs in guaranteeing species’ and habitats’ long-term persistence is the main objective of the HD reporting (art. 17), as well as of PAs in general (Geldmann et al. 2013). The effectiveness of the network of PAs in preserving nature worldwide has been widely assessed in terms of safeguarding i) habitats’ loss or degradation, ii) present and future species distribution coverage, as well as iii) evidence of concrete effects on demography, abundance, and extinction risk trends of wildlife (Gray et al. 2016), although with inconsistent results across taxa and geographical contexts (Rodrigues et al. 2004). Moreover, the efforts spent in evaluating the appropriateness of PAs in conserving species have been taxonomically and geographically biased (Godet and Devictor 2018), and so far insufficient to evaluate the status of biodiversity as a whole. Namely, very few studies to date explored the conservation status and representation of specific taxonomic groups, invertebrates above all, within European PAs, if compared to the wide literature on birds and mammals (Samways 1993, Boitani et al. 2011, Hernàndez‐Manrique et al. 2012, de Carvalho et al. 2017). Insects represent a major part of animal biodiversity, and their role in ecosystem functioning has long been acknowledged (Yang and Gratton 2014). Insects in fact play key roles in all ecosystems by being a major component of the environmental biomass, and by occupying virtually all the possible ecological positions within trophic networks i.e., being either consumers, predators, prey, or pollinators (Kehoe et al. 2021). Nonetheless, and despite a wide consensus of an “insect conservation crisis” (Goulson 2019) insect conservation is still far from being an exhaustive and spread discipline, possibly due to the taxonomic instability and identification challenges that most groups pose to conservationists, besides their low appeal as perceived by the wider public (Hart and Sumner 2020). Even within the relatively few works dealing with insect conservation assessments, taxonomic biases are still clearly evident, with a strong prevalence of studies focusing on more charismatic and aesthetically appreciated groups such as diurnal Lepidoptera (Burton 2001, Piccini et al. 2022), or on speciose taxa with a long history of studies dealing with their taxonomy, ecology and distributions, such as Coleoptera (e.g., saproxylic beetles: D’Amen et al., 2013). Orthoptera, i.e. crickets, grasshoppers and katydids, are a very diverse and yet understudied and underrepresented insect group, with about 1,082 species of orthopterans occurring in Europe, but only 11 species featuring within the HD, and none being considered as a conservation priority. Among European orthopterans, the genus Saga includes 17 species of large-sized carnivorous bush crickets that inhabit grasslands and semi-open habitats, one of which – S. pedo , also known as the Predatory bush cricket or Spiked magician – is featured within Annex IV of the HD, where identified threats are habitat encroachment by afforestation and pasture abandonment, the use of pesticides, and overgrazing (Lemonnier-Darcemont et al. 2009). The species is in fact considered as associated with natural and semi-natural grasslands, yet its actual ecological requirements are poorly known, as very common for insects, probably due to the species’ low detectability, semi-nocturnal habits, and short life-span, that make it hard to conduct exhaustive field studies (Holuša et al. 2013). Consequently, the actual extent of occurrence of the species has been probably underestimated so far, and new observations keep on being recorded at novel locations (Nerozzi, et al. 2022). As such, assessing its potential distribution by species distribution models (SDMs) instead of record-based evaluations may prove more efficient in targeting its conservation at wide geographical scales (Herkt et al. 2017), an approach though never attempted to date. An alternative for guaranteeing the conservation of poorly detectable species in absence of detailed field data may also be the use of surrogate taxa or entities (e.g., habitats) that are presumably easier to locate and thus conserve, and to which the target species may be associated (Mumby et al. 2008). Such a strategy may include the granting of protection to habitats representing highly suitable areas to the species, as implemented for some saproxylic beetles whose conservation is guaranteed by conserving old-growth forests with specific characteristics (Parisi et al. 2019). Even though S. pedo is considered as associated with specific grassland types (e.g., dry grasslands on calcareous substrates, Kaltenbach, 1990), no quantitative assessment of presumed association with legally protected grassland habitats, i.e. HD habitats, has ever been tested on the species to date. Here we develop SDMs of S. pedo across Europe in order to achieve the following goals: i) map the species’ potential distribution and determine the main ecological factors that drive its occurrence at range-wide scale, and ii) quantify the species’ representation within the network of PAs across Europe by following two alternative approaches. Moreover, we conducted a detailed regional-scale analysis focusing on a testing area in order to iii) assess whether the legally protected grassland habitats listed in the Annex I of the HD may actually sustain S. pedo populations by featuring higher suitability in comparison to non-protected habitats. Materials and methods Study area and record collection We defined the study area as the one encompassing all European countries where S. pedo is known to occur with >1 location. This resulted in the inclusion of the area ranging from Portugal to western Siberia to the east, and from Sicily to Czech Republic and Slovakia to the north (Figure 1). Occurrence records were retrieved from different sources, including gbif database (via the rgbif package: Chamberlain et al., 2017), authors’ own data from field surveys, and published references (see reference list in Supplementary materials). Records were included in the data collection only if georeferenced with <1 km accuracy. Additional records were also collected from iNaturalist (www.inaturalist.org). We then removed duplicated records, and used the spThin package (Aiello-Lammens et al. 2015) to thin data at 5 km distance, i.e. reducing multiple records within such distance to a single one, in order to avoid spatial autocorrelation and overestimating the importance of environmental variables’ from over-sampled geographical areas. Such a procedure led to the inclusion of 3,283 independent records. For assessing the species’ association to protected grassland habitats as listed in the HD, we focused on Apulia – southern Italy (Figure 1b) – a region where the species is relatively common and frequently recorded (33.3% of Italian records) and for which detailed and exhaustive mapping of listed grassland habitats is available i.e., also including areas outside of the N2K network. Species Distribution Models We downloaded 19 bioclimatic variables as descriptors of climatic conditions from Worldclim2 (Fick and Hijmans 2017), with a 10 km resolution. We controlled for multicollinearity among variables by running a Variance Inflation Factor (vif) analysis, retaining only variables with vif values <10 (Curto and Pinto 2011). This procedure led us to maintain 6 independent bioclimatic variables (bio2, bio6, bio8, bio15, bio18, and bio19). We also included, as predictor variable, the most recent layer of grassland cover at European scale available (https://land.copernicus.eu/pan-european/high-resolution-layers/grassland/status-maps/grassland-2018), which features a 10 m resolution raster mapping of natural and semi-natural grasslands (e.g., including heathlands, sparsely vegetated grasslands, semi-arid steppes, and meadows), all known to potentially host S. pedo . We built species distribution models (SDMs) based on a bioclimatic envelope concept (Pearson and Dawson 2005), and by adopting an ensemble forecasting approach as implemented in the sdm R package (Naimi and Araùjo 2016), a well-established procedure that reduces uncertainty of predictions by single model algorithms (Watling et al. 2015). We considered three modelling techniques: Generalized Linear Models (GLMs), Random Forests (RFs), and Maximum Entropy Models (Maxent), performing 10 runs for each technique, for RFs and GLMs, we generated pseudo-absences (background data, n=10,000) by adopting a randomization approach (Barve et al. 2011). The combination of these algorithms is considered among the best performing ones, providing robust and reliable prediction when used in an ensemble (Kaky et al. 2020). For model training, we randomly selected 70% of occurrence data, using the remaining 30% for model performance testing. Model performance was assessed by inspecting the values of the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS), two validation methods widely used in sdms (Araùjo and New 2007) and that evaluate model discrimination abilities (AUC) and the ratio of correct predictions and randomly corrected ones, a recommended approach when assessing the performance of predictive models (Allouche et al. 2008). The effect of each environmental predictor on the probability of occurrence of S. pedo was assessed by inspecting the response curves, while each variable’s relative importance was calculated by the specifically devoted function in the sdm package ( getVarImp ), which determines the change in AUC values due to the inclusion of each target variable. Conservation Gap Analysis We assessed the degree of protection granted to S. pedo by PAs in Europe by carrying out a conservation spatial gap analysis, based on both the full occurrences dataset and the binarized potential distribution map, by overlaying each with the shapefiles defining boundaries of both N2K and NPAs for Europe, as downloaded from the websites of the European Environmental Agency and UNEP’s World Conservation Monitoring Centre, respectively. We conducted this analysis both at the entire range and single-country scales. We excluded from this analysis those countries with n records <5, those not being within the EU area, and those which did not feature suitable habitat as predicted by our SDM. We thus calculated the percent of records and of suitable area to S. pedo overlapping with either N2K, NPA, and PA (N2k+NPA) networks of protected areas. To assess whether the two types of protected areas differed in their efficacy in preserving the species, we ran a repeated measure two-way ANOVA test upon the percent coverage values of each class of protected areas and for type of data (records vs suitable range), using Tukey’s post hoc tests for assessing significance in coverage between each compared pair of values. Association to habitats We assessed whether protected habitats as listed in Annex I of the Habitats Directive provide an effective surrogate of S. pedo ’s ecological needs for conservation, by focusing on a more local (regional) scale. The grassland layer used for the models, and the habitat suitability raster were first clipped to the regional boundaries’ geographical extent. We then selected three grassland habitats listed in Annex I of the HD and occurring widely across the region and namely i) semi-natural dry grasslands on calcareous substrates (HD code: 6210), ii) pseudo-steppe with grasses and annuals of the Thero-Brachypodietea (6220), and iii) Eastern sub-Mediterranean dry grasslands (62A0). These grassland types encompass most of the protected dry grassland habitats occurring in low- to mid-altitudes across Southern Europe, and are considered as priority habitats to conservation due to the high-diversity of both plants and invertebrates they host (Valkó et al. 2016). Habitat layers, that have been mapped within the entire regional territory by 2018, were provided by the regional authority as vector polygons (https://pugliacon.regione.puglia.it/). In order to separately consider grassland surfaces as listed and non-listed habitats, we first excluded all portions of the grassland layer overlapping with any habitat polygons, i.e. leaving only grassland areas listed as “Non-habitat grassland”. To assess the importance of different kinds of grasslands in fostering the occurrence of S. pedo by boosting habitat suitability, we calculated the percent amounts of all habitat and on-habitat extents within each suitable grid cell across the region. Subsequently, we quantified the relationship between suitability values and grassland composition by running a Generalized Linear Model (GLM), using suitability values as response variable, and the percent amounts of all habitats and non-habitat grasslands as predictors, considering significant those effects with p<0.05 and whose confidence intervals did not encompass 0. Results Species distribution models Our model reached a satisfactory level of predictive performance at both present and future time, as evaluated by AUC and TSS values (>0.90 and >0.85, respectively). Saga pedo potential distribution is apparently spread across the known range of the species, with large portions of Mediterranean and continental Europe being classified as potentially suitable, particularly in the eastern Iberian Peninsula, southern France, and peninsular Italy, as well as the Balkan and Carpathian regions (Figure 2). The main drivers of S. pedo suitability in our models were the mean temperature of the coldest month (bio6, 18.5% of explained variance), and precipitation seasonality (bio15, 14.5%), followed by the presence of grasslands (11.9%), and precipitation of the warmest quarter (bio18, 10.4%). Overall, the species seems associated with grassland areas characterized by mild winter and dry summer, with predictable rainfall patterns throughout the year (i.e., low precipitation seasonality). Minor but significant role in influencing the probability of occurrence of the species in our models was also covered by precipitation of coldest quarter (bio19, 6.0%), suggesting that S. pedo also favors relatively wet winter months. Conservation gap analysis The overall network of protected areas, comprising both NPA and N2K, shows relatively low degree of overlap (31%) with the potential distribution of S. pedo across the EU in which the species occurs. The N2K network currently protects 18% of the species’ potential range, a value comparable to that offered by NPA (25%, p>0.05). When considering countries separately (Table 1), the highest percent of overall protected range is located in Hungary (38%) and Croatia (25%), while all other countries ranged between 13-22%. When running the same analysis on exact occurrence records, values were, on average, significantly higher (ANOVA F 1,23 =109.9, p<0.001), ranging from 24 to 99%, with 79% of overall observations falling within the N2K/NPA networks across EU. There was no significant difference between the coverage provided by N2K and NPAs, nor any interaction between the type of network and the type or data used (all p>0.05). Table 1. Conservation coverage for Saga pedo by the networks of protected areas in Europe, expressed as percent per country/area, based on predicted suitable range (Range) and exact records (Occurrences). N2K=Natura2000 network, NPA=Nationally protected areas. Suitable habitat was mapped through species distribution models (SDMs) binarized maps. N2K NPA N2K+NPA Country /Area (n records) Range Occurrences Range Occurrences Range Occurrences Spain (91) 12% 93% 11% 38% 14% 95% France (2,400) 16% 22% 18% 25% 22% 24% Italy (84) 11% 88% 11% 58% 13% 90% Croatia (20) 24% 75% 22% 75% 25% 75% Austria (122) 33% 99% 25% 90% 33% 99% Hungary (15) 35% 60% 21% 55% 38% 60% Bulgaria (32) 21% 72% 11% 68% 24% 75% Romania (40) 15% 55% 12% 50% 15% 55% Europe (3,278) 18% 72% 25% 58% 31% 73% Association to habitats A total of 348 grid cells are environmentally suitable to S. pedo in Apulia according to our model, mostly falling within the Murgia plateau and its immediate surroundings, besides some suitable spots located on the grassy slopes of the Gargano massif. Probability of occurrence of S. pedo within cells predicted to be suitable significantly varied according to grassland composition, with a positive effect of the amounts of 62A0 (Eastern sub-Mediterranean dry grasslands) only, and a negative effect of non-habitat grasslands (Table 2, Figure 3). Table 2. Effects of grassland habitats on the environmental suitability to Saga pedo obtained species distribution models, estimated by generalized linear models. Habitat 6210, Habitat 6220 and Habitat 62A0 correspond to protected grasslands listed within the Annex I of the EU Habitats Directive). Predictors are quantified as percent amount of habitat within 1x1 km grid cells. Suitability Predictors Estimates std. Error 95% CI p Non-habitat grassland -0.004 0.001 -0.006 – -0.002 <0.001 Habitat 6210 -0.003 0.002 -0.008 – 0.001 0.119 Habitat 62A0 0.001 0.000 0.001 – 0.002 0.009 Habitat 6220 0.001 0.001 -0.001 – 0.004 0.297 Observations 348 R 2 0.442 Discussion We provide the first assessment of the bioclimatic niche and conservation coverage of the vulnerable predatory bush cricket S. pedo , by predicting its potential distribution across the European continent, and highlighting the species’ relationship with both climate and land cover at two spatial scales. Saga pedo seems to be associated with grassland areas characterized by mild colder seasons, and relatively low but highly predictable precipitation in summer. Such preferences are also reflected by the abundance of the species in southern European regions with typically Mediterranean climate such as southern France and Apulia (Labadessa 2014, Labadessa et al. 2015), and by the isolated populations found in the Alpine and continental regions, usually restricted to relict xerothermic grasslands (Anselmo, 2019, Maioglio & Repetto, 2022). Being a widely distributed species across Europe (Kaltenbach 1990), the conservation of S. pedo strongly relies on transnational efforts and coordinated conservation planning that require a large-scale assessment as the one we conducted. Its current conservation status within the EU (according to the HD Report 2018) is rather inconsistent among countries, with 10% of the available national reports classify the species as in a “good” conservation status, and only within the Pannonian biogeographical region. Such uncertainty may surely be due to the lack of specific monitoring campaigns and difficulties in detecting the species in the field (Campanaro et al. 2017), yet is also likely paired to a currently insufficient coverage within European PAs. Even though NPA and N2K both provide comparable protection to S. pedo , only one third of the species’ suitable range is currently protected, suggesting that the available network of protected areas may need to be expanded in order to secure its conservation, similar to other insect taxa (Bosso et al. 2018), particularly in the case of countries with large amounts of suitable habitats paired to low degree of protection (e.g., Italy) . Moreover, our analysis highlights that different proxies of species’ distribution, namely occurrences vs potential range, provide very different pictures of S. pedo conservation coverage. Namely, when using presence records we obtained, as predicted, significantly better coverage values for S. pedo . Nonetheless, and as also highlighted for other similarly rare species (Jeliazkov et al. 2022), we stress that species’ records are frequently spatially-biased, due to unbalanced survey efforts in and out of protected areas (e.g. due to HD reporting needs). As such, we suggest caution when assessing the conservation coverage by any type of protected areas if using presence records only, particularly in the case of poorly detectable species whose occurrence may easily pass unnoticed, as in the case of our study species (Herkt et al. 2017). Insects may benefit from conservation actions and protection regimes that target other species or habitats, that may indirectly foster their conservation. Yet, evidence suggests that only species with clear and strong ecological relationships with habitats and/or other species – or endemic to small well-targeted areas – may benefit from such indirect conservation efforts (Samways 2007). Such an approach has in fact raised concerns among conservationists, since even syntopic species may actually diverge in their small-scale ecological needs, so that actions tackling one may prove useless to the others (Andelman and Fagan 2000, Labadessa and Ancillotto 2022). Our results when testing the regional-level relationship between S. pedo habitat suitability and the occurrence of protected habitats also confirm that non-specialist taxa, as S. pedo , do not necessarily benefit from conservation of other biological entities such as species or habitats (sensu HD). Nonetheless, one of the EU-listed grassland habitats of EU interest resulted significant in increasing habitat suitability to the species in southern Italy, while non-protected grasslands resulted as poorly profitable to S. pedo . Such result highlights that at least some habitats of conservation concern such as the high diversity grasslands we focused on may provide particularly favorable conditions to non-target taxa such as S. pedo , e.g. by preserving well-structured plant assemblages that in turn foster richer orthopteran communities (Labadessa et al. 2015), a key food resource to the Predatory bush cricket. Interestingly, the only habitat whose extent favored S. pedo – Eastern sub-Mediterranean dry grasslands – is considered a late-successional stage of natural dry grasslands, characterized by higher presence of perennial herbaceous species and a well-structured vegetation that may need longer times to recover after impacts such as wildfire, overgrazing and agricultural reclamation (Forte et al. 2005, Perrino and Wagensommer 2013). Conclusions By assessing the degree of protection provided to S. pedo ’s occurrences and suitable range we provide clear indications for its long-term conservation, and possibly monitoring, across Europe. Namely, we shed light on the species’ needs in terms of ecological requirements, identifying important conservation areas that may significantly benefit from an increase in extent of protected areas. Moreover, our results highlight that habitat protection, exemplified by habitats listed within the HD do not represent an efficient surrogate to preserve the Predatory bush cricket per se . Nonetheless, HD-listed habitats may actually increase local suitability of grasslands to S. pedo , and may thus be important to preserve, even as small patches in modified landscapes. Our work on S. pedo also represents a potential framework to be applied to other poorly-known species that share a similar conservation status, besides several orthopterans in urgent need of conservation assessment across Europe. The last Red List assessment of European Orthopterans in fact highlights a very bleak scenario for this group of insects, indicating that most species currently lack sufficient information for properly assessing their status, beside one third of the species being currently listed as threatened and/or demographically declining (Hochkirch et al. 2016). As such, SDM-based assessments may represent a timely and cost-efficient first step for preliminary evaluations of species hard to detect, also addressing future research and field monitoring efforts, and fostering the identification of key conservation areas for insects and, more in general, poorly known species. Declarations Competing interests and funding The authors declare they have no competing interest. 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Ecosphere 13:1–15. https://doi.org/10.1002/ecs2.4026 Rodrigues ASL, Andelman SJ, Bakarr MI, et al (2004) Effectiveness of the global protected area network in representing species diversity. Nature 428:640–643 Samways MJ (1993) Insects in biodiversity conservation: some perspectives and directives. Biodivers Conserv 2:258–282 Valkó O, Zmihorski M, Biurrun I, et al (2016) Ecology and conservation of steppes and semi-natural grasslands. Hacquetia 15:5–14 Watling JI, Brandt LA, Bucklin DN, et al (2015) Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models. Ecol Modell 309:48–59 Yang LH, Gratton C (2014) Insects as drivers of ecosystem processes. Curr Opin Insect Sci 2:26–32 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jun, 2023 Read the published version in Journal of Insect Conservation → Version 1 posted Editor assigned by journal 21 Feb, 2023 Submission checks completed at journal 21 Feb, 2023 First submitted to journal 20 Feb, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2608539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":177851617,"identity":"4d89fa29-cb6d-4256-93be-85a8f074bfe0","order_by":0,"name":"Leonardo Ancillotto","email":"data:image/png;base64,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","orcid":"","institution":"National Research Council","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"","lastName":"Ancillotto","suffix":""},{"id":177851618,"identity":"f5353d0a-b4ef-4714-9e2c-adecb0f05e78","order_by":1,"name":"Rocco Labadessa","email":"","orcid":"","institution":"National Research Council","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rocco","middleName":"","lastName":"Labadessa","suffix":""}],"badges":[],"createdAt":"2023-02-20 15:14:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2608539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2608539/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10841-023-00484-w","type":"published","date":"2023-06-09T21:06:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":33352083,"identity":"7a9a8267-8ba3-47b7-b630-51973bee8899","added_by":"auto","created_at":"2023-02-23 15:34:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":794017,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of occurrence records of \u003cem\u003eSaga pedo\u003c/em\u003e(before thinning) across its entire European range (A) and in Apulia (southern Italy – B). Inset shows the location of the regional focus within Italy\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2608539/v1/61c03d58abd8460d1c1a57ff.png"},{"id":33351215,"identity":"9cb61196-2adb-4070-9996-bab95e39d0e6","added_by":"auto","created_at":"2023-02-23 15:26:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3208316,"visible":true,"origin":"","legend":"\u003cp\u003eMap of potential suitable range (in red, only suitability values \u0026gt;0.5 are shown) to \u003cem\u003eSaga pedo\u003c/em\u003e in Europe, according to ensemble Species Distribution Models\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2608539/v1/a1ffe7e4ea62d2fc7f191ee3.png"},{"id":33351213,"identity":"f18b162b-4a14-4899-a232-66f11e220e3d","added_by":"auto","created_at":"2023-02-23 15:26:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35019,"visible":true,"origin":"","legend":"\u003cp\u003eHabitat suitability to \u003cem\u003eSaga pedo\u003c/em\u003e in Apulia (Southern Italy) in relation to the percent amounts of different grassland habitats within suitable grid cells (n=348) identified by ensemble species distribution modeling. Habitats are classified as either protected habitats listed within Annex I of the EU Habitats Directive (Eastern sub Mediterranean dry grasslands (a), pseudo-steppe with grasses and annuals of the \u003cem\u003eThero-Brachypodietea\u003c/em\u003e (b), semi-natural dry grasslands on calcareous substrates (c)) or as non-protected grasslands (d), *=p\u0026lt;0.05, ***=p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2608539/v1/49e4f91b188bc596c5737d8f.png"},{"id":44730600,"identity":"6b9792f5-ce16-4a15-b946-8e4a5720f758","added_by":"auto","created_at":"2023-10-16 21:32:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1205165,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2608539/v1/6cf507a6-d52e-4ea6-a5e1-7684b9485997.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Can protected areas and habitats preserve the vulnerable Predatory bush cricket Saga pedo?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProtected areas (PAs, hereafter) are a key instrument for conserving nature, and are in fact one of the main pillars of both global and continental-scale conservation tools. Europe stands out, in terms of wildlife conservation, by being the world area with the highest numbers of PAs\u0026nbsp;(Gaston et al. 2008). Such primate strongly relies on the Natura 2000 network (N2K, hereafter), one of the two main tools, together with species protection regime, implemented by EU countries in application of the Habitats Directive (HD, hereafter). The latter is also paired by the network of Nationally protected areas (NPAs, hereafter) such as National Parks, Nature reserves and so on. Evaluating the success of PAs in guaranteeing species\u0026rsquo; and habitats\u0026rsquo; long-term persistence is the main objective of the HD reporting (art. 17), as well as of PAs in general\u0026nbsp;(Geldmann et al. 2013). The effectiveness of the network of PAs in preserving nature worldwide has been widely assessed in terms of safeguarding i) habitats\u0026rsquo; loss or degradation, ii) present and future species distribution coverage, as well as iii) evidence of concrete effects on demography, abundance, and extinction risk trends of wildlife\u0026nbsp;(Gray et al. 2016), although with inconsistent results across taxa and geographical contexts\u0026nbsp;(Rodrigues et al. 2004). Moreover, the efforts spent in evaluating the appropriateness of PAs in conserving species have been taxonomically and geographically biased\u0026nbsp;(Godet and Devictor 2018), and so far insufficient to evaluate the status of biodiversity as a whole. Namely, very few studies to date explored the conservation status and representation of specific taxonomic groups, invertebrates above all, within European PAs, if compared to the wide literature on birds and mammals\u0026nbsp;(Samways 1993, Boitani et al. 2011, Hern\u0026agrave;ndez‐Manrique et al. 2012, de Carvalho et al. 2017).\u003c/p\u003e\n\u003cp\u003eInsects represent a major part of animal biodiversity, and their role in ecosystem functioning has long been acknowledged\u0026nbsp;(Yang and Gratton 2014). Insects in fact play key roles in all ecosystems by being a major component of the environmental biomass, and by occupying virtually all the possible ecological positions within trophic networks i.e., being either consumers, predators, prey, or pollinators\u0026nbsp;(Kehoe et al. 2021). Nonetheless, and despite a wide consensus of an \u0026ldquo;insect conservation crisis\u0026rdquo;\u0026nbsp;(Goulson 2019)\u0026nbsp;insect conservation is still far from being an exhaustive and spread discipline, possibly due to the taxonomic instability and identification challenges that most groups pose to conservationists, besides their low appeal as perceived by the wider public\u0026nbsp;(Hart and Sumner 2020). Even within the relatively few works dealing with insect conservation assessments, taxonomic biases are still clearly evident, with a strong prevalence of studies focusing on more charismatic and aesthetically appreciated groups such as diurnal Lepidoptera\u0026nbsp;(Burton 2001, Piccini et al. 2022), or on speciose taxa with a long history of studies dealing with their taxonomy, ecology and distributions, such as Coleoptera (e.g., saproxylic beetles:\u0026nbsp;D\u0026rsquo;Amen et al., 2013). Orthoptera, i.e. crickets, grasshoppers and katydids, are a very diverse and yet understudied and underrepresented insect group, with about 1,082 species of orthopterans occurring in Europe, but only 11 species featuring within the HD, and none being considered as a conservation priority. Among European orthopterans, the genus \u003cem\u003eSaga\u003c/em\u003e includes 17 species of large-sized carnivorous bush crickets that inhabit grasslands and semi-open habitats, one of which \u0026ndash; \u003cem\u003eS. pedo\u003c/em\u003e, also known as the Predatory bush cricket or Spiked magician \u0026ndash; is featured within Annex IV of the HD, where identified threats are habitat encroachment by afforestation and pasture abandonment, the use of pesticides, and overgrazing\u0026nbsp;(Lemonnier-Darcemont et al. 2009). The species is in fact considered as associated with natural and semi-natural grasslands, yet its actual ecological requirements are poorly known, as very common for insects, probably due to the species\u0026rsquo; low detectability, semi-nocturnal habits, and short life-span, that make it hard to conduct exhaustive field studies\u0026nbsp;(Holu\u0026scaron;a et al. 2013). Consequently, the actual extent of occurrence of the species has been probably underestimated so far, and new observations keep on being recorded at novel locations (Nerozzi, et al. 2022). As such, assessing its potential distribution by species distribution models (SDMs) instead of record-based evaluations may prove more efficient in targeting its conservation at wide geographical scales\u0026nbsp;(Herkt et al. 2017), an approach though never attempted to date.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn alternative for guaranteeing the conservation of poorly detectable species in absence of detailed field data may also be the use of surrogate taxa or entities (e.g., habitats) that are presumably easier to locate and thus conserve, and to which the target species may be associated\u0026nbsp;(Mumby et al. 2008). Such a strategy may include the granting of protection to habitats representing highly suitable areas to the species, as implemented for some saproxylic beetles whose conservation is guaranteed by conserving old-growth forests with specific characteristics\u0026nbsp;(Parisi et al. 2019). Even though \u003cem\u003eS. pedo\u003c/em\u003e is considered as associated with specific grassland types\u0026nbsp;(e.g., dry grasslands on calcareous substrates, Kaltenbach, 1990), no quantitative assessment of presumed association with legally protected grassland habitats, i.e. HD habitats, has ever been tested on the species to date.\u003c/p\u003e\n\u003cp\u003eHere we develop SDMs of \u003cem\u003eS. pedo\u003c/em\u003e across Europe in order to achieve the following goals: i) map the species\u0026rsquo; potential distribution and determine the main ecological factors that drive its occurrence at range-wide scale, and ii) quantify the species\u0026rsquo; representation within the network of PAs across Europe by following two alternative approaches. Moreover, we conducted a detailed regional-scale analysis focusing on a testing area in order to iii) assess whether the legally protected grassland habitats listed in the Annex I of the HD may actually sustain \u003cem\u003eS. pedo\u003c/em\u003e populations by featuring higher suitability in comparison to non-protected habitats.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cem\u003eStudy area and record collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe defined the study area as the one encompassing all European countries where \u003cem\u003eS. pedo\u003c/em\u003e is known to occur with \u0026gt;1 location. This resulted in the inclusion of the area ranging from Portugal to western Siberia to the east, and from Sicily to Czech Republic and Slovakia to the north (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOccurrence records were retrieved from different sources, including gbif database (via the \u003cem\u003ergbif\u003c/em\u003e package: Chamberlain et al., 2017), authors\u0026rsquo; own data from field surveys, and published references (see reference list in Supplementary materials). Records were included in the data collection only if georeferenced with \u0026lt;1 km accuracy. Additional records were also collected from iNaturalist (www.inaturalist.org). We then removed duplicated records, and used the \u003cem\u003espThin\u003c/em\u003e package (Aiello-Lammens et al. 2015) to thin data at 5 km distance, i.e. reducing multiple records within such distance to a single one, in order to avoid spatial autocorrelation and overestimating the importance of environmental variables\u0026rsquo; from over-sampled geographical areas. Such a procedure led to the inclusion of 3,283 independent records.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor assessing the species\u0026rsquo; association to protected grassland habitats as listed in the HD, we focused on Apulia \u0026ndash; southern Italy (Figure 1b) \u0026ndash; a region where the species is relatively common and frequently recorded (33.3% of Italian records) and for which detailed and exhaustive mapping of listed grassland habitats is available i.e., also including areas outside of the N2K network. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSpecies Distribution Models\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe downloaded 19 bioclimatic variables as descriptors of climatic conditions from Worldclim2\u0026nbsp;(Fick and Hijmans 2017), with a 10 km resolution. We controlled for multicollinearity among variables by running a Variance Inflation Factor (vif) analysis, retaining only variables with vif values \u0026lt;10\u0026nbsp;(Curto and Pinto 2011). This procedure led us to maintain 6 independent bioclimatic variables (bio2, bio6, bio8, bio15, bio18, and bio19). We also included, as predictor variable, the most recent layer of grassland cover at European scale available (https://land.copernicus.eu/pan-european/high-resolution-layers/grassland/status-maps/grassland-2018), which features a 10 m resolution raster mapping of natural and semi-natural grasslands (e.g., including heathlands, sparsely vegetated grasslands, semi-arid steppes, and meadows), all known to potentially host \u003cem\u003eS. pedo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eWe built species distribution models (SDMs) based on a bioclimatic envelope concept (Pearson and Dawson 2005), and by adopting an ensemble forecasting approach as implemented in the \u003cem\u003esdm\u003c/em\u003e R package (Naimi and Ara\u0026ugrave;jo 2016), a well-established procedure that reduces uncertainty of predictions by single model algorithms (Watling et al. 2015). We considered three modelling techniques: Generalized Linear Models (GLMs), Random Forests (RFs), and Maximum Entropy Models (Maxent), performing 10 runs for each technique, for RFs and GLMs, we generated pseudo-absences (background data, n=10,000) by adopting a randomization approach (Barve et al. 2011). The combination of these algorithms is considered among the best performing ones, providing robust and reliable prediction when used in an ensemble (Kaky et al. 2020). For model training, we randomly selected 70% of occurrence data, using the remaining 30% for model performance testing. Model performance was assessed by inspecting the values of the area under the receiver operating characteristic curve (AUC) and the True Skill Statistics (TSS), two validation methods widely used in sdms (Ara\u0026ugrave;jo and New 2007) and that evaluate model discrimination abilities \u0026nbsp;(AUC) and the ratio of correct predictions and randomly corrected ones, a recommended approach when assessing the performance of predictive models (Allouche et al. 2008).\u003c/p\u003e\n\u003cp\u003eThe effect of each environmental predictor on the probability of occurrence of \u003cem\u003eS. pedo\u003c/em\u003e was assessed by inspecting the response curves, while each variable\u0026rsquo;s relative importance was calculated by the specifically devoted function in the \u003cem\u003esdm\u003c/em\u003e package (\u003cem\u003egetVarImp\u003c/em\u003e), which determines the change in AUC values due to the inclusion of each target variable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConservation Gap Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed the degree of protection granted to \u003cem\u003eS. pedo\u003c/em\u003e by PAs in Europe by carrying out a conservation spatial gap analysis, based on both the full occurrences dataset and the binarized potential distribution map, by overlaying each with the shapefiles defining boundaries of both N2K and NPAs for Europe, as downloaded from the websites of the European Environmental Agency and UNEP\u0026rsquo;s World Conservation Monitoring Centre, respectively. We conducted this analysis both at the entire range and single-country scales. We excluded from this analysis those countries with n records \u0026lt;5, those not being within the EU area, and those which did not feature suitable habitat as predicted by our SDM. We thus calculated the percent of records and of suitable area to \u003cem\u003eS. pedo\u003c/em\u003e overlapping with either N2K, NPA, and PA (N2k+NPA) networks of protected areas. To assess whether the two types of protected areas differed in their efficacy in preserving the species, we ran a repeated measure two-way ANOVA test upon the percent coverage values of each class of protected areas and for type of data (records vs suitable range), using Tukey\u0026rsquo;s \u003cem\u003epost hoc\u003c/em\u003e tests for assessing significance in coverage between each compared pair of values. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAssociation to habitats\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed whether protected habitats as listed in Annex I of the Habitats Directive provide an effective surrogate of \u003cem\u003eS. pedo\u003c/em\u003e\u0026rsquo;s ecological needs for conservation, by focusing on a more local (regional) scale. The grassland layer used for the models, and the habitat suitability raster were first clipped to the regional boundaries\u0026rsquo; geographical extent. We then selected three grassland habitats listed in Annex I of the HD and occurring widely across the region and namely i) semi-natural dry grasslands on calcareous substrates (HD code: 6210), ii) pseudo-steppe with grasses and annuals of the \u003cem\u003eThero-Brachypodietea\u003c/em\u003e (6220), and iii) Eastern sub-Mediterranean dry grasslands (62A0). These grassland types encompass most of the protected dry grassland habitats occurring in low- to mid-altitudes across Southern Europe, and are considered as priority habitats to conservation due to the high-diversity of both plants and invertebrates they host (Valk\u0026oacute; et al. 2016). Habitat layers, that have been mapped within the entire regional territory by 2018, were provided by the regional authority as vector polygons (https://pugliacon.regione.puglia.it/). In order to separately consider grassland surfaces as listed and non-listed habitats, we first excluded all portions of the grassland layer overlapping with any habitat polygons, i.e. leaving only grassland areas listed as \u0026ldquo;Non-habitat grassland\u0026rdquo;. To assess the importance of different kinds of grasslands in fostering the occurrence of \u003cem\u003eS. pedo\u003c/em\u003e by boosting habitat suitability, we calculated the percent amounts of all habitat and on-habitat extents within each suitable grid cell across the region. Subsequently, we quantified the relationship between suitability values and grassland composition by running a Generalized Linear Model (GLM), using suitability values as response variable, and the percent amounts of all habitats and non-habitat grasslands as predictors, considering significant those effects with p\u0026lt;0.05 and whose confidence intervals did not encompass 0.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eSpecies distribution models\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur model reached a satisfactory level of predictive performance at both present and future time, as evaluated by AUC and TSS values (\u0026gt;0.90 and \u0026gt;0.85, respectively). \u003cem\u003eSaga pedo\u003c/em\u003e potential distribution is apparently spread across the known range of the species, with large portions of Mediterranean and continental Europe being classified as potentially suitable, particularly in the eastern Iberian Peninsula, southern France, and peninsular Italy, as well as the Balkan and Carpathian regions (Figure 2). The main drivers of \u003cem\u003eS. pedo\u003c/em\u003e suitability in our models were the mean temperature of the coldest month (bio6, 18.5% of explained variance), and precipitation seasonality (bio15, 14.5%), followed by the presence of grasslands (11.9%), and precipitation of the warmest quarter (bio18, 10.4%). Overall, the species seems associated with grassland areas characterized by mild winter and dry summer, with predictable rainfall patterns throughout the year (i.e., low precipitation seasonality). Minor but significant role in influencing the probability of occurrence of the species in our models was also covered by precipitation of coldest quarter (bio19, 6.0%), suggesting that \u003cem\u003eS. pedo\u003c/em\u003e also favors relatively wet winter months.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConservation gap analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe overall network of protected areas, comprising both NPA and N2K, shows relatively low degree of overlap (31%) with the potential distribution of \u003cem\u003eS. pedo\u003c/em\u003e across the EU in which the species occurs. The N2K network currently protects 18% of the species\u0026rsquo; potential range, a value comparable to that offered by NPA (25%, p\u0026gt;0.05). When considering countries separately (Table 1), the highest percent of overall protected range is located in Hungary (38%) and Croatia (25%), while all other countries ranged between 13-22%. When running the same analysis on exact occurrence records, values were, on average, significantly higher (ANOVA F\u003csub\u003e1,23\u003c/sub\u003e=109.9, p\u0026lt;0.001), ranging from 24 to 99%, with 79% of overall observations falling within the N2K/NPA networks across EU. There was no significant difference between the coverage provided by N2K and NPAs, nor any interaction between the type of network and the type or data used (all p\u0026gt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Conservation coverage for \u003cem\u003eSaga pedo\u003c/em\u003e by the networks of protected areas in Europe, expressed as percent per country/area, based on predicted suitable range (Range) and exact records (Occurrences). N2K=Natura2000 network, NPA=Nationally protected areas. Suitable habitat was mapped through species distribution models (SDMs) binarized maps.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003eN2K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003eN2K+NPA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eCountry /Area\u003cbr\u003e\u0026nbsp;(n records)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eRange\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eOccurrences\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eRange\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eOccurrences\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eOccurrences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003cp\u003e(91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eFrance (2,400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003cp\u003e(84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eCroatia\u003c/p\u003e\n \u003cp\u003e(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eAustria\u003cbr\u003e\u0026nbsp;(122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eHungary\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eBulgaria\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eRomania\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e12%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eEurope (3,278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e73%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAssociation to habitats\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 348 grid cells are environmentally suitable to \u003cem\u003eS. pedo\u003c/em\u003e in Apulia according to our model, mostly falling within the Murgia plateau and its immediate surroundings, besides some suitable spots located on the grassy slopes of the Gargano massif. Probability of occurrence of \u003cem\u003eS. pedo\u003c/em\u003e within cells predicted to be suitable significantly varied according to grassland composition, with a positive effect of the amounts of 62A0 (Eastern sub-Mediterranean dry grasslands) only, and a negative effect of non-habitat grasslands (Table 2, Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Effects of grassland habitats on the environmental suitability to\u0026nbsp;\u003cem\u003eSaga pedo\u003c/em\u003e obtained species distribution models, estimated by generalized linear models. Habitat 6210, Habitat 6220 and Habitat 62A0 correspond to protected grasslands listed within the Annex I of the EU Habitats Directive). Predictors are quantified as percent amount of habitat within 1x1 km grid cells.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"91%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"73.73737373737374%\"\u003e\n \u003cp\u003eSuitability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003estd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eNon-habitat grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.612244897959183%\"\u003e\n \u003cp\u003e-0.006\u0026nbsp;\u0026ndash;\u0026nbsp;-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eHabitat 6210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.612244897959183%\"\u003e\n \u003cp\u003e-0.008\u0026nbsp;\u0026ndash;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eHabitat 62A0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.612244897959183%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u0026ndash;\u0026nbsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u003cem\u003e0.009\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eHabitat 6220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.612244897959183%\"\u003e\n \u003cp\u003e-0.001\u0026nbsp;\u0026ndash;\u0026nbsp;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.262626262626263%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"73.73737373737374%\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.262626262626263%\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"73.73737373737374%\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe provide the first assessment of the bioclimatic niche and conservation coverage of the vulnerable predatory bush cricket \u003cem\u003eS. pedo\u003c/em\u003e, by predicting its potential distribution across the European continent, and highlighting the species\u0026rsquo; relationship with both climate and land cover at two spatial scales. \u003cem\u003eSaga pedo\u003c/em\u003e seems to be associated with grassland areas characterized by mild colder seasons, and relatively low but highly predictable precipitation in summer. Such preferences are also reflected by the abundance of the species in southern European regions with typically Mediterranean climate such as southern France and Apulia\u0026nbsp;(Labadessa 2014, Labadessa et al. 2015), and by the isolated populations found in the Alpine and continental regions, usually restricted to relict xerothermic grasslands\u0026nbsp;(Anselmo, 2019, Maioglio \u0026amp; Repetto, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeing a widely distributed species across Europe\u0026nbsp;(Kaltenbach 1990), the conservation of \u003cem\u003eS. pedo\u003c/em\u003e strongly relies on transnational efforts and coordinated conservation planning that require a large-scale assessment as the one we conducted. Its current conservation status within the EU (according to the HD Report 2018) is rather inconsistent among countries, with 10% of the available national reports classify the species as in a \u0026ldquo;good\u0026rdquo; conservation status, and only within the Pannonian biogeographical region. Such uncertainty may surely be due to the lack of specific monitoring campaigns and difficulties in detecting the species in the field\u0026nbsp;(Campanaro et al. 2017), yet is also likely paired to a currently insufficient coverage within European PAs. Even though NPA and N2K both provide comparable protection to \u003cem\u003eS. pedo\u003c/em\u003e, only one third of the species\u0026rsquo; suitable range is currently protected, suggesting that the available network of protected areas may need to be expanded in order to secure its conservation, similar to other insect taxa\u0026nbsp;(Bosso et al. 2018), particularly in the case of countries with large amounts of suitable habitats paired to low degree of protection (e.g., Italy) . Moreover, our analysis highlights that different proxies of species\u0026rsquo; distribution, namely occurrences vs potential range, provide very different pictures of\u003cem\u003e\u0026nbsp;S. pedo\u0026nbsp;\u003c/em\u003econservation coverage. Namely, when using presence records we obtained, as predicted, significantly better coverage values for \u003cem\u003eS. pedo\u003c/em\u003e. Nonetheless, and as also highlighted for other similarly rare species\u0026nbsp;(Jeliazkov et al. 2022), we stress that species\u0026rsquo; records are frequently spatially-biased, due to unbalanced survey efforts in and out of protected areas (e.g. due to HD reporting needs). As such, we suggest caution when assessing the conservation coverage by any type of protected areas if using presence records only, particularly in the case of poorly detectable species whose occurrence may easily pass unnoticed, as in the case of our study species\u0026nbsp;(Herkt et al. 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsects may benefit from conservation actions and protection regimes that target other species or habitats, that may indirectly foster their conservation. Yet, evidence suggests that only species with clear and strong ecological relationships with habitats and/or other species \u0026ndash; or endemic to small well-targeted areas \u0026ndash; may benefit from such indirect conservation efforts (Samways 2007). Such an approach has in fact raised concerns among conservationists, since even syntopic species may actually diverge in their small-scale ecological needs, so that actions tackling one may prove useless to the others\u0026nbsp;(Andelman and Fagan 2000, Labadessa and Ancillotto 2022). Our results when testing the regional-level relationship between \u003cem\u003eS. pedo\u003c/em\u003e habitat suitability and the occurrence of protected habitats also confirm that non-specialist taxa, as \u003cem\u003eS. pedo\u003c/em\u003e, do not necessarily benefit from conservation of other biological entities such as species or habitats (sensu HD). Nonetheless, one of the EU-listed grassland habitats of EU interest resulted significant in increasing habitat suitability to the species in southern Italy, while non-protected grasslands resulted as poorly profitable to \u003cem\u003eS. pedo\u003c/em\u003e. Such result highlights that at least some habitats of conservation concern such as the high diversity grasslands we focused on may provide particularly favorable conditions to non-target taxa such as \u003cem\u003eS. pedo\u003c/em\u003e, e.g. by preserving well-structured plant assemblages that in turn foster richer orthopteran communities\u0026nbsp;(Labadessa et al. 2015), a key food resource to the Predatory bush cricket. Interestingly, the only habitat whose extent favored \u003cem\u003eS. pedo\u003c/em\u003e \u0026ndash; Eastern sub-Mediterranean dry grasslands \u0026ndash; is considered a late-successional stage of natural dry grasslands, characterized by higher presence of perennial herbaceous species and a well-structured vegetation that may need longer times to recover after impacts such as wildfire, overgrazing and agricultural reclamation (Forte et al. 2005, Perrino and Wagensommer 2013).\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy assessing the degree of protection provided to \u003cem\u003eS. pedo\u003c/em\u003e\u0026rsquo;s occurrences and suitable range we provide clear indications for its long-term conservation, and possibly monitoring, across Europe. Namely, we shed light on the species\u0026rsquo; needs in terms of ecological requirements, identifying important conservation areas that may significantly benefit from an increase in extent of protected areas. Moreover, our results highlight that habitat protection, exemplified by habitats listed within the HD do not represent an efficient surrogate to preserve the Predatory bush cricket \u003cem\u003eper se\u003c/em\u003e. Nonetheless, HD-listed habitats may actually increase local suitability of grasslands to \u003cem\u003eS. pedo\u003c/em\u003e, and may thus be important to preserve, even as small patches in modified landscapes. Our work on \u003cem\u003eS. pedo\u003c/em\u003e also represents a potential framework to be applied to other poorly-known species that share a similar conservation status, besides several orthopterans in urgent need of conservation assessment across Europe. The last Red List assessment of European Orthopterans in fact highlights a very bleak scenario for this group of insects, indicating that most species currently lack sufficient information for properly assessing their status, beside one third of the species being currently listed as threatened and/or demographically declining (Hochkirch et al. 2016). As such, SDM-based assessments may represent a timely and cost-efficient first step for preliminary evaluations of species hard to detect, also addressing future research and field monitoring efforts, and fostering the identification of key conservation areas for insects and, more in general, poorly known species.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests and funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interest. No funding was received for conducting this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth authors contributed to the study conception and design, and to data collection and curation. The first draft of the manuscript was written by Leonardo Ancillotto, and both authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAiello-Lammens ME, Boria RA, Radosavljevic A, et al (2015) spThin: an R package for spatial thinning of species occurrence records for use in ecological nche models. Ecography (Cop) 38:541\u0026ndash;545\u003c/li\u003e\n\u003cli\u003eAllouche O, Steinitz O, Rotem D, et al (2008) Incorporating distance constraints into species distribution models. 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Curr Opin Insect Sci 2:26\u0026ndash;32\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-insect-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jico","sideBox":"Learn more about [Journal of Insect Conservation](http://link.springer.com/journal/10841)","snPcode":"10841","submissionUrl":"https://submission.nature.com/new-submission/10841/3","title":"Journal of Insect Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"gap analysis, habitats directive, insect conservation, Orthoptera, Saga pedo, species distribution modeling","lastPublishedDoi":"10.21203/rs.3.rs-2608539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2608539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInconspicuous species challenge conservationists when it comes to delineate long-term conservation planning or assess their status, particularly when their actual distribution is poorly known. Invertebrates in particular feature among the less represented taxa in conservation assessments. Here we follow a multidisciplinary approach for assessing the conservation coverage and address future management of the threatened orthopteran \u003cem\u003eSaga pedo\u003c/em\u003e across Europe, shedding light on its ecological preferences and associations with protected habitats at continental and regional scales. By developing species distribution models and assessing coverage by Natura2000 and Nationally Protected Areas networks, we found that only 31% of suitable areas is currently protected across Europe, a proportion significantly higher when using occurrences instead of potential range. At regional scale, we disclose that different legally-protected dry grassland habitats increase more the species\u0026rsquo; suitability than non-protected grasslands, yet differently-listed habitats do not equally contribute to such increase i.e., not all habitats represent an effective tool for the species\u0026rsquo; conservation. Taken together, our results provide an effective framework for addressing knowledge gaps and evaluate the conservation coverage not only of our target species, but more in general of poorly investigated species, at the same time pointing at the urgent need of transnational, coordinated, and increased efforts in monitoring and conserving insects, particularly in the case of threatened species.\u003c/p\u003e","manuscriptTitle":"Can protected areas and habitats preserve the vulnerable Predatory bush cricket Saga pedo?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-23 15:26:51","doi":"10.21203/rs.3.rs-2608539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2023-02-21T17:00:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-02-21T17:00:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Insect Conservation","date":"2023-02-20T15:07:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-insect-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jico","sideBox":"Learn more about [Journal of Insect Conservation](http://link.springer.com/journal/10841)","snPcode":"10841","submissionUrl":"https://submission.nature.com/new-submission/10841/3","title":"Journal of Insect Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ec2c16dc-b50b-4276-bdb6-2a3e8b73cd94","owner":[],"postedDate":"February 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:16:28+00:00","versionOfRecord":{"articleIdentity":"rs-2608539","link":"https://doi.org/10.1007/s10841-023-00484-w","journal":{"identity":"journal-of-insect-conservation","isVorOnly":false,"title":"Journal of Insect Conservation"},"publishedOn":"2023-06-09 21:06:41","publishedOnDateReadable":"June 9th, 2023"},"versionCreatedAt":"2023-02-23 15:26:51","video":"","vorDoi":"10.1007/s10841-023-00484-w","vorDoiUrl":"https://doi.org/10.1007/s10841-023-00484-w","workflowStages":[]},"version":"v1","identity":"rs-2608539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2608539","identity":"rs-2608539","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}