### III.3.3. Correlations with survey effort and habitat covariates

The correlation between the sampling coverage at each site and the value of the measured habitat covariates was analyzed for each of the two survey conditions (i.e. classical vs doubled) (Fig.4). A significant positive correlation was found between this sampling coverage in classical condition and the area of the surveyed site (Spearman's correlation; rs=0.49, P < 0.05). The average coverage also seemed positively correlated to the habitat diversity present on the site, but this correlation is not significant (Spearman's correlation; rs=0.36, P > 0.05). No correlation was found between the proportion of closed habitats at a site and the average sampling coverage (Spearman's correlation; rs=0.14, P > 0.05). Although conducting double visits increased the sampling coverage on each site, it did not lead to any change in the correlation between this coverage and habitat covariates (Fig.3.4b).

A significant correlation was also identified between the number of visits conducted on a site and the sampling coverage (Spearman's correlation; rs=0.81, P < 0.001). Estimates of species richness were more complete for sessions with higher number of visits, both in classical and doubled conditions (Fig.4). The number of visits performed to a site also showed a strong correlation with several habitat covariates of this site. The largest number of visits was made (following classical EIA requirements) to sites with the largest area (Spearman's correlation; rs=0.52, P > 0.05) and greatest diversity of natural habitats (Spearman's correlation; rs=0.54, P < 0.05) (Suppl.S3.5).

### III.3.4. Hyper-parameters of the community

When considering all insect species together at the community level, posterior hyper-parameters of occurrence and detection probabilities were globally low (mean value of 0.373 and 0.141 respectively) but very variable within the species pool (CI<sub>95%</sub> = 0.162 - 0.559 and 0.184 - 0.998 respectively) (Table 3.4). At the community level, the correlation between average species occurrence and habitat covariates was positive, generally suggesting that surveyed insects' species were more likely to occur on large sites with a large proportion of closed habitat. The confidence interval of the response to habitat diversity was distributed almost equally around 0 (CI<sub>95%</sub> = -0.22 - 0.255), minimizing the role of this covariate as a driver of species occurrence. In contrast, area and proportion of closed habitat did not include the 0 value in their confidence interval, indicating a strong effect of these covariates on
species occurrence (Table 3.4). We also noted the significance of the quadratic effect of the "closure" parameter, indicating an optimum of occurrence probabilities species for an intermediate closure value (Suppl.S3.6a).

<table><caption>**Table 3.4:** Community-level summaries of the hyper-parameters for the detection and occupancy covariates (mean, standard deviation (SD), CI<sub>95%</sub>). The direction (+ or -) of the effect of site and survey covariates was indicated for those showing a significant influence on occurrence and detection.</caption><thead><tr><th>Community level hyper-parameter</th><th></th><th>Mean</th><th>SD</th><th>CI 2.5%</th><th>CI 97.5%</th><th>Sign.</th></tr></thead><tbody><tr><td>ψ</td><td>Occurrence probability</td><td>0.373</td><td>0.103</td><td>0.162</td><td>0.559</td><td></td></tr><tr><td>μa1</td><td>Area</td><td>0.268</td><td>0.103</td><td>0.066</td><td>0.469</td><td>(+)</td></tr><tr><td>μa2</td><td>Habitat diversity</td><td>0.199</td><td>0.121</td><td>-0.220</td><td>0.255</td><td></td></tr><tr><td>μa3</td><td>Closure proportion (linear)</td><td>0.412</td><td>0.163</td><td>0.095</td><td>0.736</td><td>(+)</td></tr><tr><td>μa4</td><td>Closure proportion (quadratic)</td><td>-0.420</td><td>0.140</td><td>-0.701</td><td>-0.146</td><td>(-)</td></tr><tr><td>p</td><td>Detection probability</td><td>0.141</td><td>0.021</td><td>0.184</td><td>0.998</td><td></td></tr><tr><td>μb1</td><td>Date (linear)</td><td>-0.307</td><td>0.158</td><td>-0.619</td><td>0.005</td><td></td></tr><tr><td>μb2</td><td>Date (quadratic)</td><td>-1.367</td><td>0.105</td><td>-1.584</td><td>-1.168</td><td>(-)</td></tr><tr><td>μb3</td><td>Survey hour</td><td>-0.546</td><td>0.063</td><td>-0.672</td><td>-0.422</td><td>(-)</td></tr><tr><td>μb4</td><td>Survey length</td><td>0.023</td><td>0.037</td><td>-0.050</td><td>0.096</td><td></td></tr></tbody></table>

Among the covariates related to the visit conditions (date, time, length), the length of the visit showed an average positive correlation with detection probability, although it was not significant (CI<sub>95%</sub> = -0.05 - 0.096) (Table 3.4). The time at which the visit was started also strongly influenced the average detection probability, with a higher probability when the visit was started early (Suppl S3.6b). The date of the visit also had a negative but more variable influence on the average probability of detection of the community, reflecting the difference of activity period between species (Table 3.4). In addition, this covariate exhibited a significant quadratic effect (CI<sub>95%</sub> = -1.58 - -1.17), reflecting a detection optimum over the field season.

### III.3.5. Species-specific occurrence and detection

The naïve occurrence (proportion of sites where a given species was present) calculated from the observations made under classical EIA conditions varied according to the species considered, ranging from 0.05 for species located on a single site to 1 for species widespread on all sites (Suppl.S3.7). The value calculated after doubling the number of visits showed a similar range of variation between species. The observed occurrence of about 60% of the
species was higher under doubled EIA conditions, with an average of two new sites of
presence (species occurrence increase in average of 0.09). Mean probabilities of occurrence
estimated by hierarchical modelling varied widely among species, with values ranging from
0.07 (Decticus verrucivorus monspelliensis) to 0.98 (Euchorthippus elegantulus)
(Suppl.S3.7).

<figure><img src="image_48.png" /></figure>

**Figure 3.4:** Relationship between observed sampling coverage among sites and several site-level parameters, including habitat covariates included in the hierarchical model and the number of visits performed on the site. The upper line (a) correspond to sampling coverage observed under classical EIA conditions and the lower line (b) correspond to sampling coverage observed under doubled EIA conditions. P-values indicate the significance of a Spearman correlation test performed between the observed sampling coverage and each habitat covariate. The regression curve was adjusted through the function *geom.smooth* of the package *ggplot2* with argument *method* = 'lm'.
Absolute bias between naïve occupancy and the ones estimated with the MSOM ranged from 0.01 to -0.79 under classical EIA conditions and between 0.01 and -0.64 under double EIA conditions. The average bias in occurrence of the entire species pool was significantly lower under doubled conditions (Wilcoxon test, P < 0.001). Cicadas were the only species group without significant reduction in this bias when sampling effort was doubled (Fig.3.5). The absolute bias in occurrence differ significantly between the different taxonomic groups (Kruskall test, P < 0.05; Fig.3.5a), with a bias significantly higher for butterflies (-0.21 ± 0.14 under classical EIA, -0.16 ± 0.14 under doubled EIA) then dragonflies (-0.28 ± 0.17 under classical EIA, -0.22 ± 0.14 under doubled EIA) regardless of the sampling condition (i.e. classical or doubled). On the other hand, species covered by a conservation status (-0.15 ± 0.12 under classical EIA, -0.13 ± 0.10 under doubled EIA) showed a significantly lower bias than uncovered species (-0.27 ± 0.16 under classical EIA, -0.21 ± 0.13 under doubled EIA), under standard conditions and doubled conditions (Wilcoxon test, P < 0.05; Fig.3.5b).

<figure><img src="image_49.png" /></figure>

**Figure 3.5** : Absolute bias in occurrence among insects' species observed under classical (dark boxes) and doubled (light boxes) EIA conditions, depending on their taxonomic group (a) and to their conservation status (b). Absolute bias corresponded to the difference between mean species-specific occurrence probabilities and the observed occurrence in a given survey condition (classical or doubled). Taxonomic groups are categorized into butterflies (n = 102), cicadas (n = 8), dragonflies (n = 46) and grasshoppers (n = 71). Conservation status are categorized into covered species (n = 33) and uncovered species (n = 194). Asterisks indicates significant differences of sampling coverage between classical EIA and doubled EIA in each species group (under the line) and between these groups (above the line) (***: P < 0.001, *: P < 0.05, NS: P > 0.05, pairwise Wilcoxon tests).
Species-specific detection probabilities were mainly low, with greater than 80% of observed species having mean detection probabilities of less than 0.5 per visit in the study area (Suppl S3.7). This probability varied between 0.01 (*Callophrys rubi*) and 0.93 (*Decticus albifrons*). Comparison of the different taxonomic groups showed a significantly lower mean detection probability for butterflies (0.20 ± 0.17) than for dragonflies (0.33 ± 0.22) and grasshoppers (0.29 ± 0.21) (Wilcoxon test, P < 0.001; Fig.3.6a). In contrast, no significant difference was noticeable between species covered by conservation status and uncovered species (Wilcoxon test, P > 0.05; Fig.3.6b).

The estimation of the average posterior of the parameters *a* and *b* from the hierarchical model made it possible to evaluate the importance and the direction (positive or negative) of species response to the different habitat and visit covariates in terms of occurrence and detection (Suppl S3.8 and S3.9). The mean species-specific effect of site area (*a*1) on the probabilities of occurrence was positive for all species, varying from 0.12 (*Argynnis paphia*) to 0.42 (*Issoria lathonia*). Yet, the CI95% of this parameter for all species included the zero value, revealing an absence of strong significant effect. Species response in terms of occurrence was more variable for habitat diversity (*a*2). This parameter varied between -0.84 (*Dociostaurus jagoi*) and 0.90 (*Calliptamus wattenwylianus*) without any significant trend (almost 54% showed positive response and 46% showed negative response). The proportion of closed habitat (*a*3) had mostly a positive linear effect on species-specific occurrence probabilities, with a variation between -0.91 (*Libellula fulva*) and 1.93 (*Anthocharis euphenoides*) and 13 species showing a significant positive response. Moreover, all species were marked by a negative quadratic effect (*a*4) (without significant response), which indicated an occurrence maximized for intermediate values of closure.

The influence of visit date (*b*1) on species-specific detection probabilities was characterized by a high variability, going from -5.24 (*Melanargia occitanica*) to 4.58 (*Euchorthippus elegantulus*) and with 60% of the species showing a negative response. A high proportion of species (44%) had a CI95% that did not overlap zero, which highlighted the importance of planning visits for insect detection. The majority of these species (64%) decrease of their detection probability throughout the year. A negative quadratic response to this covariate (*b*2) was also revealed for all species, with a significant value for 61% of them. Detection probabilities of a proportion of species were then maximized for intermediate date values. These values were highly variable across species, in relation to their phenology and
the variability of their detection according to their life stage. Regarding their mean values, cicadas and grasshoppers were mostly detectable in summer (July-August) whereas butterflies and dragonflies showed higher detection probabilities earlier in the season (May-June) (Suppl S3.6c). The time were the visit was conducted (b3) had a negative effect on almost 93% of the species, with a significant response for 26% of them. The value this parameter varied between -1.41 (*Euchorthippus elegantulus*) and 0.68 (*Uromenus rugosicollis*). Probability of false-absence was globally higher during visits started in the afternoon or conducted during the night for the four studied taxonomic groups (Suppl S3.6c). Some species (7%) showed positive (but non-significant) response to this covariate. A large part of these species were grasshoppers characterized by mainly nocturnal activity, detectable through their mating calls. Finally, detection probabilities were positively correlated to the effect of visit length (b4) for almost 65% of the studied species. This parameter varied from -0.39 (*Platycleis acutipennis*) to 0.34 (*Leptidea sinapis*) and was significant for only two species. The value of the mean detection probability was almost constant after one to seven hours of survey for each taxonomic group (Suppl.S3.6c).

<figure><img src="image_50.png" /></figure>

**Figure 3.6** : Mean posterior estimates of detection probability among insects' species depending on their taxonomic group (a) and to their conservation status (b). Taxonomic groups are categorized into butterflies (n = 102), cicadas (n = 8), dragonflies (n = 46) and grasshoppers (n = 71). Conservation status are categorized into covered species (n = 33) and uncovered species (n = 194). Different letters refer to a significant difference (P < 0.05) between modalities according to pairwise Wilcoxon's signed-rank tests.
### III.3.6. Minimum survey effort requirement

The mean number of required minimum visits was 7.15 (± 9.94), with great variability across species, ranging from 0.2 (*Prionotropis azami*) to 72.9 (*Pieris manii*) visits (Suppl S3.7). The value estimated for several species showed a particularly large confidence interval (CI95%). This lack of precision could be linked to the method of modelling through data shrinkage, allowing estimating the detection probabilities of species for which very little field data has been collected. There was no significant difference in terms of minimum effort requirements between the surveyed taxonomic groups (Kruskall test, P > 0.05; Fig.3.7a). On the other hand, the average number of visits required to certify the absence of a species covered by a conservation status was significantly lower (2 ± 1.86 visits) than for uncovered species (7.39 ± 9.98 visits) (Wilcoxon test, P < 0.001; Fig.3.7b).

<figure><img src="image_51.png" /></figure>

**Figure 3.7:** Mean values (± SE) of minimum survey effort requirements among insect's species depending on their taxonomic group (a) and to their conservation status (b). These values were estimated on the base of species-specific detection probabilities obtained through the MSOM under optimal survey conditions (i.e. for values of survey covariates maximizing detection probability). Taxonomic groups are categorized into butterflies (n = 102), cicadas (n = 8), dragonflies (n = 46) and grasshoppers (n = 71). Conservation status are categorized into covered species (n = 33) and uncovered species (n = 194). Asterisks indicate significant differences of sampling coverage between classical EIA and doubled EIA in each species group (under the line) and between these groups (above the line) (***: P < 0.001, *: P < 0.05, NS: P > 0.05, pairwise Wilcoxon tests).
### III.4. Discussion

This study highlights the significant influence of the imperfection of insect detection on the quality of entomological surveys under EIA conditions. They reveal a major gap between survey results and the potential richness on a site, with in average one in two species which will remain undetected after the application of this type of classical effort. This bias remains significant when focusing on protected or vulnerable species (with a detection of less than 60% of the potential species), even if it was significantly fewer than for uncovered species. We showed that the doubling the sampling effort allowed a significant increase in survey completeness, but the average difference with the potential richness remains important. Given the estimated correlation between species-specific parameters and covariates links with survey conditions, it seems more relevant to fragment available resources into shorter visits, spread throughout the season and including at least a night visit, rather than spending more time on a site.

#### III.4.1. Representativeness of EIAs and sampling effort

We have been able to demonstrate the impossibility of establishing a complete list of the insect species present on a site from the number of visits defined under a classical EIA, whatever the taxonomic group considered. In the majority of cases (i.e. 15 out of 20 sites) the sampling design was limited to only 2 to 4 visits on the site during the year (Table 3.2). Although the richness was better covered on some sites than others (70% of the species detected for the most exhaustive, Table 3.2), one in two potential species will remain on average undetected after the application of this type of classical effort (Fig.3.1b). The lists of species established through the usual recommendations show a significant bias and represent only a truncated view of the diversity occupying a site, even though visits are made by experienced entomologists with a good knowledge of the area. This result is not totally unexpected with regard to knowledge about the richness and structure of insect communities. Many species occurred in low density, with populations established on relatively limited surface habitat areas (Païvinen et al., 2005; New, 2010). This relative rarity, coupled with cryptic habits and often short flight periods, could limit the detection probabilities of these species if they are not specifically researched (Mao & Colwell, 2005). This part of the community will remain difficult to observe, even in the case of abundant resources allocated to the realization of many visits (Kéry & Plattner, 2007). Estimating species richness within
diversified entomological communities will usually require a particularly costly effort (Foggo *et al.*, 2003; Coddington *et al.*, 2009). If we take the example of butterflies, except from results obtained for poorer assemblages in Northern Europe (Wikström *et al.*, 2009), a minimum of ten field surveys seems generally necessary to approach 90% of the richness of sites in temperate climates, under standardized sampling conditions (Dennis *et al.*, 1999; Jonason *et al.*, 2010; Hardersen & Corezzola, 2014). Considering the entomological diversity of the Mediterranean area and the lack of standardization in EIA surveys (research of different taxa during a same visit, priority research of threatened species habitats ...), it is likely that the effort need to achieve such completeness is even higher.

The significant increase in the proportion of species detected following the double visits shows the influence of sampling effort on the quality of the results. However, although species lists were more representative of the potential richness of the sites, the average difference with this richness remains important even after double visits (Fig. 3.1b). Given the cost of a field visit, this relatively limited benefit in terms of completeness questions the relevance of increasing the sampling effort to improve representativeness of EIA inventories. From a certain threshold, it may become difficult to increase the number of species detected, and the cost of extra effort is likely to exceed the gain from further counts (Heliölä & Kuussaari, 2005). Given that the effort available when conducting an EIA is highly constrained by developer's funding, the goal of complete sampling coverage seems unrealistic. The large difference between the observed richness in double condition and the estimated richness gives an idea of the investment that would then be needed. However EIA do not claim such a degree of completeness, but rather aim to obtain an estimate of the richness and originality of the species assemblages present on the site. Depending on the objective of the study, a certain degree of effort reduction, with non-detection of a part of the community, may be acceptable, as long as the significance of the desired trends can be observed (Loos *et al.*, 2014). Species subsets can provide a reliable surrogate for the full community and reduced effort can allow detecting a representative assemblage of species (Bried *et al.*, 2012b). In the case of EIAs, this quantification of the relationship between the intensity of the sampling effort and sampling coverage may allow the regulator to fix the minimum effort required to reach an acceptable representativeness threshold during design of the sampling plan, which could optimize the cost-quality balance (Garrard *et al.*, 2015).
### III.4.2. Going further asymptotic richness

Although the description of the main species assemblages is an important goal sought an EIA (in particular because their richness or their originality can guide the implementation of reduction measures if the project is accepted), the main objective remains the detection of species covered by conservation status. But the effort required for better sampling coverage or more optimal detection may be different depending on the status or rarity of species (MacKenzie & Royle, 2005; MacKenzie et al., 2005). The distinction of several groups of species (taxonomy, conservation issue) in the MSOM allowed us to estimate sampling coverage in terms of detected richness for each of them. Although the average sampling coverage was lower for dragonflies, the results did not show any significant difference between the surveyed taxonomic groups (Fig.3.3a). Conclusions were similar to the results for the total community, both for the importance of the bias and for the relative benefit of increasing sampling effort (Table 3.3). On the other hand, sampling coverage was significantly dependent on the status of the species (covered/uncovered). On average, the proportion of undetected species was greater for covered species and the impact of survey conditions (classical vs doubled), although significant, was more limited (Fig.3.3b). Less than 60% of covered species were detected on average at a site, even after double visits. However, we must note the great variability of this completeness, related to the low number of potential species present on average per site (7.44 species). Since the absolute difference was small (4.25 species observed under doubled conditions), the least new species observed may lead to a significant increase in sampling coverage.

The modeling framework used in the present study also provided the opportunity to go beyond the simple estimation of an asymptotic richness, giving access to the occupancy patterns of individual species driving species richness (Honkanen et al., 2011). The estimation of the difference between the occurrences observed during the visits and that ones estimated by the model allowed us to measure the intensity of the biases related to the heterogeneity of the probability of detection within the community. While the increase in sampling effort has improved to some extent the average detection of the different species groups, the importance of bias will vary depending on the taxonomic group and the species conservation status (Fig.3.5). This bias was significantly more important for butterflies and for uncovered species. Contrary to the general pattern known for threatened species (MacKenzie et al., 2005; Guillera-Arroita et al., 2010), they have been observed on a good proportion of the sites at
which they occurred. This result could be explained by the fact that their detectability was
higher on average than the uncovered species (although not significant) (Fig.3.6b). The EIA-
specific study design pays greater attention to covered species, preferably targeting their
period of activity and the habitats that are favorable to their presence on a site (Jay *et al.*,
2007). Our results thus demonstrate some effectiveness of the current conditions toward this
objective. However, some inaccuracy remains, and some covered species are characterized by
a particularly important absolute occupation bias (e.g. value of -0.5 for *Saga pedo*
(Orthoptera, Pallas 1771)). As with most sampled species, absolute detectability was still
relatively low with high variability across species (Fig.3.6). An observer has on average less
than one chance in two to detect a species occurring on a site, during a visit made for median
values of covariates. This potentially strong propensity for false absences is commonly
encountered in insects (Mata *et al.*, 2014) and has already been demonstrated for butterflies,
despite surveys focused solely on this taxon and more standardized conditions (Dorazio *et al.*,
2006; Kéry & Plattner, 2007). The risk of direct population destruction of these undetected
species increases the importance of considering these biases when assessing the impact of a
project. Although the increase in sampling effort has allowed a significant increase in the
average detection of these species at their sites of presence (Fig.3.5b), the benefit remains
limited for a significant proportion of covered species (i.e. no change in absolute bias
occurrence between classical and doubled EIA conditions for 80% of them). It therefore
seems necessary to adjust this increase in effort by a more cost-efficient allocation of
resources. Detection probability can be improved by adjusting the sampling conditions,
adapted to the local peculiarities of the sites. The study of the correlation between
probabilities of occurrence and detectability and the different covariates incorporated into the
hierarchical model allowed us to identify these optimal conditions.

### III.4.3. Influence of the local context of the sites

Our results show that larger sites host a larger number of species, and species detected during
our visits have a higher probability of being present on those sites (Table 3.4). This
relationship between area and species richness is already documented in some insect groups
surveyed in our study (Steffan-Dewenter & Tscharntke, 2000; Öckinger & Smith, 2006;
Ruggiero et al. 2008). A site with a large area is more likely to cover the home range of
several species and to include diverse natural habitats (as shown by the strong positive
correlation between these two variables in our study; Suppl.S3.5). Consistent with this
pattern, the greater the number of identified natural habitats, the higher the potential richness of a site estimated by the MSOM (Table 3.2, Suppl.S3.1). However, our results do not reveal any significant influence of this parameter on species occurrence (Table 3.4, Suppl.S3.6). The heterogeneity of the natural habitats on a site leads in the majority of the cases to the multiplication of the niches available for the invertebrates (Tew et al., 2003). The diversification of available resources, particularly plants communities (Haddad et al., 2009), allows the co-occurrence of a larger number of species within the same area. However, within this community each species will respond specifically to certain factors that will favor its presence (habitat, biotic interactions...) (Rosin et al., 2012), without necessarily depending on the absolute diversity of habitats on the site.

The degree of closure of the sites seems to be one of the habitat parameters which significantly influence the composition of the entomological community (Table 3.4, Fig.3.4). Each species has its own optimum of occurrence along this closure gradient linked to its specific requirements (e.g. *Aiolopus puissanti* (Orthoptera, Defaut 2005) linked to the most open sites, *Isophya pyrenaea* (Orthoptera, Audinet-Serville 1838) with a higher occurrence in sites dominated by woodland), and a large part of the community seems to be favored by intermediate values (Suppl S3.6). These intermediate conditions allow the coexistence of several stages of succession on the same locality. Because most species generally use several types of adjacent habitats, such coexistence gives them access to complementary resources or to micro-habitats required for some specific phases of their life cycle (e.g. dragonflies occur preferentially along edges during their maturation) (Van Halder *et al*., 2011 ; Ushida & Ushimaru, 2014).

In a rather counter-intuitive way, our results showed a significant positive correlation between site covariates that favor species richness and sampling coverage (Fig.3.4). Larger sites showing a greater diversity of habitats were, on average, surveyed with greater precision. But in the majority of cases, the more diverse a community will be, the more its real richness will be difficult to observed (Gotelli & Colwell, 2011). The relationship observed in our study seems to be related to the correlation between site area and sampling effort, with larger site receiving more visits (Suppl S3.5). Such increase in sampling effort could have increased the performance of the surveys, notably by providing better coverage of the time window favorable to the sampled groups.
### III.4.4. Adjustment of survey conditions to maximize species detection

Although the mean probability of detection was low for the majority of the species, this parameter shows a significant variation according to the conditions of each visit. The date and time at which the visits were carried out have the most significant influence on species detection and will therefore constitute important levers for improving the quality of EIA results (Table 3.4, Suppl S3.9). The influence of the visit's date showed in particular the greatest heterogeneity within the community. Species presented various detection optimum, with some threatened species almost perfectly detectable during precise periods of the year (i.e. *Zerynthia polyxena* (Rhopalocera, Denis & Schiffermüller 1775) for visits conducted in early April). Time of the year in which surveys will be undertaken constitute a major issue for the detection of species covered by conservation status and the spreading of visits throughout the season is necessary to improve the sampling coverage (Bell *et al.*, 2014; Petitot *et al.*, 2014).

The starting time of survey had a more homogeneous influence and visits started early in the day seemed to improve the detection of a high number of species, whatever the visit length. However, each species shows its own daily cycle of activity (Hassall & Thompson, 2008) and some threatened species will be much better detected in later passages (i.e. *Saga pedo*, with an optimal detection probability when visits were conducted at 24 pm). The realization of at least one nocturnal visit (as it is already the case in some EIAs conducted in the Mediterranean area) seems justified in relation to these issues, although this effort is not sufficient to ensure perfect detection.

On the other hand, visits length did not appear as a significant parameter for the quality of EIAs results. The majority of species did not show any particular response to this factor in terms of detection probability. The variations of the number of visits and of their length are generally two methods of resources allocation on which it is possible to play simultaneously (Bried *et al.*, 2012b; Hardersen & Corezzola, 2014). From our results, it seems more relevant to fragment available resources into shorter visits, spread throughout the season and including at least a night visit, rather than spending more time on a site (Dettmers *et al.*, 1999).
The estimation of the minimum survey effort required to attest the absence of each
species provides a first view of the investment required to obtain representative results, under
the assumption of visits made under optimal conditions for detecting target species. These
requirements seemed already important for a high number of species (Suppl S3.7). For many
species, these needs appeared unrealistic with respect to the constraints and objectives of an
EIA. However, the minimum requirements of most threatened species seem to fit within EIA
framework (Fig.3.7b).

### III.4.5. Hierarchical model as a decision-making tool for the regulator

Considering the results of our study, MSOMs seem to be a relevant tool for measuring the
completeness of biological surveys, allowing to consider the variability related to species-
specific response. The fact that the estimates fitted well to our data (Suppl S3.2) and the
consistency of the estimated richness values (given the potentialities of the studied
landscapes; Table 3.1), despite the relatively limited number of input data, shows their
potential for the valorization of incidence data collected through wandering surveys.
Moreover, analysis of multi-species data can be challenging because sampling techniques
often identify numerous species with few detections (Zipkin et al., 2010). Unlike less diverse
groups, the richness of insect communities makes it difficult to obtain accurate data for all
species in a single visit (Sewell et al., 2012; Petitot et al., 2014). The bayesian shrinkage
process allows overcoming this limit by going beyond a simple aggregation of individual
estimates, which can give biased results (Zipkin et al., 2009; Clark et al., 2014). Where
analyzes based on rarefaction curves have shown their limits (Kobitz et al., 2017), MOSM
could become a decision-making tool in the context of EIA and insect conservation (Sauer et
al., 2013; Mata et al., 2017).

Nevertheless, accuracy of species-specific estimates will always be limited by the
amount of available data (Zipkin *et al.*, 2009). In the present case, the low number of sites (i.e.
20 sites) linked to a heterogeneous number of visits should lead us to consider some
numerical results with caution, especially with regard to the estimation of the minimum effort
requirements (which show a very high standard deviation for some species sparsely
observed). Our work should be considered as a "preliminary" study, allowing to alert
regulators about the main biases of current EIA survey conditions and to extract valid
guidelines for the studied landscapes (i.e. Mediterranean area). This study could be improved
in the future by carrying out more investigations and declined over a wider variety of conditions through recent developments in MSOM (multi-region models, guild distinction ...) (Sutherland *et al.*, 2016; Tenan *et al.*, 2017).

### III.4.6. Conclusion

Our results emphasize the need for field protocols that accommodate estimation of detection error when conduction entomological multi-species surveys in the case of EIA. They highlight the benefit of using reference values to quantify the completeness of the results from these studies in order to obtain a more precise assessment of the risks associated with the project for the conservation of threatened insects. Given that the flexibility is particularly limited in the context of EIA due to the limited available resources, the establishment of guidelines for optimal allocation is essential in order to maximize the value of data from multi-taxa surveys (Field et al., 2005; Lovell et al., 2010). The number of visits, their spread over the year and the time at which they will be carried out will then constitute important parameters for the establishment of minimum standard guaranteeing the quality of studies.

### Acknowledgments

The present work was a collaboration between Biotope and the Centre d'Ecologie Fonctionnelle et Evolutive in the form of a PhD thesis funded by Biotope and the Association Nationale de la Recherche et de la Technologie (ANRT, N° 2014/1320). We thank the region Languedoc-Roussillon (N° GR/AF/2015-09-715) for financial support to GD. Many thanks to Thomas Menut (Biotope, France), who was implicated in the design of the survey methods, and to William Bernard (Biotope, France) who conducted a part of entomological field surveys in classical EIA conditions. We also like to thank the project managers from Biotope agencies of Languedoc-Roussillon and Provence-Alpes-Côte-d'Azur who allowed us to conduct doubled visits to the studied sites. Thanks to Michel-Ange Bouchet, Solenne Lejeune and Pascaline Vinet (Biotope, France) for providing us maps of natural habitats in the different surveyed sites. We thank Elise Zipkin (Michigan State University, USA) for providing us several scripts for the analysis and information on the modeling framework. Finally, we are grateful to Iris Le Roncé for helping us improve the structure of this paper.

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**Supplementary materials 3**

**Supplementary material S3.1:** Site description and sampling summaries.

**Supplementary material S3.2:** Illustration of the Multi-Species Occupancy Model framework.

**Supplementary material S3.3:** Goodness-of-fit test for single season multi-species occupancy models

**Supplementary material S3.4:** Estimated species richness.

**Supplementary material S3.5:** Correlation index between the habitat covariates measured at the scale of the sampled sites and the total number of visits made per site.

**Supplementary material S3.6:** Posterior estimates of community level occurrence (a) and detection (b) probabilities as a function of site (a) and survey (b) covariates.

**Supplementary material S3.7:** Posterior estimates of the species-specific occurrence and detection probabilities of insect species as estimated under the MSOM.

*(À consulter dans la partie “Annexes” du manuscript de thèse).*

**Supplementary material S3.8:** Posterior estimates for the effect of site covariates on observed insect species as estimated under the MSOM.

*(À consulter dans la partie “Annexes” du manuscript de thèse).*

**Supplementary material S3.9:** Posterior estimates for the effect of visit covariates on observed insect species as estimated under the MSOM.

*(À consulter dans la partie “Annexes” du manuscript de thèse).*
**Supplementary material S3.1:** Site description and sampling summaries. 'Habitat' indicates the number of natural habitats included in the limit of the site (described from field surveys using the CLC nomenclature), 'Closure' refers to the proportion of the site area occupied by closed habitat (dense shrubland, forest) and 'Surveys' to the number of visits conducted during the classical EIA.

<table><thead><tr><th>N°</th><th>Locality</th><th>Lat.</th><th>Long.</th><th>Administrative unit</th><th>Region</th><th>Area</th><th>Altitude</th><th>Habitat</th><th>Closure</th><th>Surveys</th><th>Year</th><th>Start date</th><th>End date</th></tr></thead><tbody><tr><td>1</td><td>Lieuran-lès-Béziers</td><td>N 43°25'08.4"</td><td>E 003°14'20.8"</td><td>Herault</td><td>Languedoc</td><td>950.09</td><td>40</td><td>33</td><td>4.36</td><td>5</td><td>2015</td><td>Apr 14th</td><td>Jun 29th</td></tr><tr><td>2</td><td>Mauguio</td><td>N 43°37'00.5"</td><td>E 004°00'34.2"</td><td>Herault</td><td>Languedoc</td><td>38.9</td><td>3</td><td>24</td><td>11.90</td><td>4</td><td>2015</td><td>Apr 10th</td><td>Jun 11th</td></tr><tr><td>3</td><td>Lespignan</td><td>N 43°16'22.4"</td><td>E 003°10'21.0"</td><td>Herault</td><td>Languedoc</td><td>43.79</td><td>20</td><td>6</td><td>0.27</td><td>4</td><td>2015</td><td>Apr 20th</td><td>Jul 2nd</td></tr><tr><td>4</td><td>Fabrègues</td><td>N 43°33'04.3"</td><td>E 003°46'30.0"</td><td>Herault</td><td>Languedoc</td><td>25.83</td><td>20</td><td>20</td><td>9.48</td><td>3</td><td>2015</td><td>Apr 9th</td><td>Jun 24th</td></tr><tr><td>5</td><td>Montdardier</td><td>N 43°55'38.6"</td><td>E 004°35'34.1"</td><td>Gard</td><td>Languedoc</td><td>59.31</td><td>642</td><td>6</td><td>13.05</td><td>4</td><td>2015</td><td>Apr 15th</td><td>Jul 19th</td></tr><tr><td>6</td><td>Rians</td><td>N 43°36'22.7"</td><td>E 005°45'22.3"</td><td>Var</td><td>Provence</td><td>63.63</td><td>390</td><td>11</td><td>62.42</td><td>3</td><td>2015</td><td>Apr 11th</td><td>Jul 23th</td></tr><tr><td>7</td><td>Gallargues-le-Montueux</td><td>N 43°43'09.1"</td><td>E 004°10'07.3"</td><td>Gard</td><td>Languedoc</td><td>58.37</td><td>15</td><td>17</td><td>1.90</td><td>2</td><td>2015</td><td>Apr 10th</td><td>Jul 8th</td></tr><tr><td>8</td><td>Pézenas</td><td>N 43°38'26.5"</td><td>E 003°25'26.0"</td><td>Herault</td><td>Languedoc</td><td>156.55</td><td>22</td><td>19</td><td>4.17</td><td>2</td><td>2015</td><td>Apr 13th</td><td>Jul 10th</td></tr><tr><td>9</td><td>Bessan</td><td>N 43°21'40.0"</td><td>E 003°25'36.5"</td><td>Herault</td><td>Languedoc</td><td>73.63</td><td>45</td><td>20</td><td>21.35</td><td>2</td><td>2016</td><td>Jun 30th</td><td>Aug 4th</td></tr><tr><td>10</td><td>Saint-Thibéry</td><td>N 43°23'42.7"</td><td>E 003°24'59.8"</td><td>Herault</td><td>Languedoc</td><td>486.83</td><td>35</td><td>30</td><td>3.77</td><td>2</td><td>2016</td><td>Apr 25th</td><td>Jul 4th</td></tr><tr><td>11</td><td>Pinet</td><td>N 43°24'15.8"</td><td>E 003°30'34.2"</td><td>Herault</td><td>Languedoc</td><td>541</td><td>50</td><td>39</td><td>14.32</td><td>9</td><td>2016</td><td>Apr 7th</td><td>Jul 25th</td></tr><tr><td>12</td><td>Poussan</td><td>N 43°29'18.2"</td><td>E 003°40'13.4"</td><td>Herault</td><td>Languedoc</td><td>443.69</td><td>80</td><td>38</td><td>5.37</td><td>9</td><td>2016</td><td>Apr 4th</td><td>Jul 8th</td></tr><tr><td>13</td><td>Murviel-lès-Béziers</td><td>N 43°26'23.3"</td><td>E 003°08'39.8"</td><td>Herault</td><td>Languedoc</td><td>685.44</td><td>47</td><td>65</td><td>23.95</td><td>5</td><td>2016</td><td>Apr 6th</td><td>Apr 27th</td></tr><tr><td>14</td><td>Saint-Pargoire</td><td>N 43°31'39.7"</td><td>E 003°31'12.0"</td><td>Herault</td><td>Languedoc</td><td>365.11</td><td>195</td><td>27</td><td>85.20</td><td>7</td><td>2016</td><td>Apr 4th</td><td>Jul 12th</td></tr><tr><td>15</td><td>Saint-Victor-la-Coste</td><td>N 44°03'52.2"</td><td>E 004°38'23.3"</td><td>Gard</td><td>Languedoc</td><td>102.67</td><td>248</td><td>5</td><td>98.28</td><td>4</td><td>2016</td><td>Apr 20th</td><td>Jul 13th</td></tr><tr><td>16</td><td>Lamanon</td><td>N 43°42'01.1"</td><td>E 005°05'25.8"</td><td>Bouches-du-Rhones</td><td>Provence</td><td>9.1</td><td>120</td><td>15</td><td>4.72</td><td>4</td><td>2016</td><td>May 20th</td><td>Jul 24th</td></tr><tr><td>17</td><td>Istres</td><td>N 43°29'56.0"</td><td>E 004°57'07.2"</td><td>Bouches-du-Rhones</td><td>Provence</td><td>16.18</td><td>10</td><td>8</td><td>28.12</td><td>3</td><td>2016</td><td>May 05th</td><td>Jul 22th</td></tr><tr><td>18</td><td>Valliguieres</td><td>N 44°00'31.7"</td><td>E 004°34'43.7"</td><td>Gard</td><td>Languedoc</td><td>85.78</td><td>235</td><td>5</td><td>97.61</td><td>2</td><td>2016</td><td>May 13th</td><td>Jul 14th</td></tr><tr><td>19</td><td>Saint-Hilaire-d'Ozilhan</td><td>N 43°58'10.2"</td><td>E 004°35'26.9"</td><td>Gard</td><td>Languedoc</td><td>136.01</td><td>190</td><td>6</td><td>95.99</td><td>2</td><td>2016</td><td>May 26th</td><td>Jul 14th</td></tr><tr><td>20</td><td>Vendargues</td><td>N 43°39'19.8"</td><td>E 003°58'06.2"</td><td>Herault</td><td>Languedoc</td><td>12.88</td><td>52</td><td>12</td><td>9.39</td><td>3</td><td>2016</td><td>Apr 18th</td><td>Jul 18th</td></tr></tbody></table>
<table><caption>**Supplementary material S3.2:** Illustration of the Multi-Species Occupancy Model framework.</caption><thead><tr><th rowspan="3">species <i>i</i></th><th colspan="10">Site <i>k</i></th></tr><tr><th colspan="4">Observed</th><th colspan="4">Partially observed</th><th rowspan="2">&omega;<sub>i</sub></th></tr><tr><th>1</th><th>2</th><th>...</th><th><i>J</i></th><th>1</th><th>2</th><th>...</th><th><i>J</i></th><th></th></tr></thead><tbody><tr><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2</td><td>X<sub>11</sub></td><td>X<sub>12</sub></td><td>...</td><td>X<sub>1,J</sub></td><td>Z<sub>11</sub></td><td>Z<sub>12</sub></td><td>...</td><td>Z<sub>1,J</sub></td><td>&omega;<sub>1</sub></td></tr><tr><td>...</td><td>X<sub>21</sub></td><td>X<sub>22</sub></td><td>...</td><td>X<sub>2,J</sub></td><td>Z<sub>21</sub></td><td>Z<sub>22</sub></td><td>...</td><td>Z<sub>2,J</sub></td><td>&omega;<sub>2</sub></td></tr><tr><td>n</td><td>X<sub>n1</sub></td><td>X<sub>n2</sub></td><td>...</td><td>X<sub>n,J</sub></td><td>Z<sub>n1</sub></td><td>Z<sub>n2</sub></td><td>...</td><td>Z<sub>nJ</sub></td><td>&omega;<sub>n</sub></td></tr><tr><td>n+1</td><td>0</td><td>0</td><td>...</td><td>0</td><td>Z<sub>n+1,1</sub></td><td>Z<sub>n+1,2</sub></td><td>...</td><td>Z<sub>n+1,J</sub></td><td>&omega;<sub>n+1</sub></td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>N</td><td>0</td><td>0</td><td>...</td><td>0</td><td>Z<sub>N1</sub></td><td>Z<sub>N2</sub></td><td>...</td><td>Z<sub>NJ</sub></td><td>&omega;<sub>N</sub></td></tr><tr><td>N+1</td><td>0</td><td>0</td><td>...</td><td>0</td><td>Z<sub>N+1,1</sub></td><td>Z<sub>N+1,2</sub></td><td>...</td><td>Z<sub>N+1,J</sub></td><td>&omega;<sub>N+1</sub></td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>M</td><td>0</td><td>0</td><td>...</td><td>0</td><td>Z<sub>M1</sub></td><td>Z<sub>M2</sub></td><td>...</td><td>Z<sub>MJ</sub></td><td>&omega;<sub>M</sub></td></tr></tbody></table>

<figure><img src="image_52.png" /><figcaption>**Fig.1.** Conceptualization of the super-community of *M* species used in data augmentation process of the Multi-Species Occupancy Model (MSOM) framework. *x* comprises a matrix of *n* rows of observed visit frequencies and *M-n* rows of unobserved (all-zero) visit frequencies (the detection process). *z* denotes a matrix of species- and site-specific occurrence parameters (the ecological process). *ω* denotes a vector of parameters that indicate membership in the community of *N* species available to capture (adapted from Dorazio *et al.*, 2011).</figcaption></figure>
**Fig.2.** Schematic representation of the hierarchical Multi-Species Occupancy Model (MSOM) framework. The *detection process* consists in an incidence data (*k* visits per site, *n* observed species) for a number of sites (four in this example), where 1 mean a species was detect during a visit at a site and 0 means that the species was not detected. This matrix was augmented with a virtual large number of zeros to produce a super-community of *M* fixed species. Such statistical trick allowed simplifying the estimation of the number of species that occupied the sites but never detected during visits. This estimation problem was converted to the distinction of augmented species that were a part of the real community and those that were just extra zero. The distinction was performed through the estimation of an inclusion probability (*ω*ᵢ). The MSOM estimates species detection and inclusion probabilities within a Bayesian framework using MCMC (Markov Chain Monte Carlo) algorithm. The *ecological process* was represented by a matrix of species presence/absence at each site. This matrix was only partly observed in the *detection process*, because of possible species non-detection. “Sampling” zeros (species undetected at each visit on a site) indicated in grey in the *detection process* may reflected a true absence (in red in the *ecological process*) or simply a non-detection of a present species (in green in the *ecological process*). The inclusion probability ωᵢ was the probability that a species from the super-community (*M*) forms a part of the regional community. The “true species richness” of the regional community was then estimated as a sum over the vector ω. In the present example, *n* = 5 species were detected, and the virtual richness of the super-community was fixed to *M* = 17 species by data augmentation. The model estimated the true species richness as *N* = 13 species (5 observed and 8 unobserved), based on the input incidence matrix.
**Supplementary material S3.3:** Goodness-of-fit test for single season multi-species occupancy models

We assessed model fit using a *Bayesian p-value* (Gelman *et al.*, 1996). We defined a discrepancy measure *D* following Zipkin *et al.*, 2009:

$$D = \sum_{i} (x_{i} - p_{i})^{2}$$

for the observations $x_i$ and their expected values $p_i$ under the MSOM. This discrepancy statistic was computed at each iteration of the MCMC algorithm. A reference distribution was computed by simulating data sets from the posterior distribution and computing the discrepancy measure, $D^{sim}$, for the simulated data set. The *Bayesian p-value* was calculated as the proportion of times that the deviance of the observed data, *D*, was greater than the deviance of detections $D^{sim}$ predicted from the model: Pr($D > D^{sim}$). Values larger than 0.95 or smaller than 0.05 indicate that the model did not fit well the data. We also calculated a “lack-of-fit” statistics $D/D^{sim}$ which is expected to be equal to 1 if the model fits the data perfectly (Kéry & Schaub, 2012).

For our dataset, the *Bayesian p-value* was estimated at 0.75 and the “lack-of-fit” statistic was estimated as 1.01. These values suggesting that the MSOM provided an adequate description of the input data.

<figure><img src="image_53.png" /></figure>
<figure><img src="image_54.png" /><figcaption>**Supplementary material S3.4:** Estimated species richness of the community (a), at the scale of each site (b), of butterflies (c), of dragonflies (d), of grasshoppers (e), of cicadas (f), of species uncovered by conservation status (g) and of species covered by conservation status (h). Red lines indicate the mean response and colored bars the posterior distribution (i.e., 100% credible interval).</figcaption></figure>
<figure><img src="image_55.png" /><figcaption>**Supplementary materials S3.5:** Correlation index between the habitat covariates measured at the scale of the sampled sites and the total number of visits made per site.</figcaption></figure>

<figure><img src="image_56.png" /></figure>
**Supplementary materials S3.6:** Posterior estimates of community level occurrence (a) and detection (b) probabilities as a function of site (a) and survey (b) covariates. Solid line represents the posterior mean community response and dotted lines represent the CI95% of the posterior mean. The mean detection probabilities estimated for different taxonomic groups, on the base of species-specific posterior estimates, were also presented as a function of survey covariates (c). Line types correspond to different taxonomic groups (dotdash: butterflies, solid: cicadas, dashed: dragonflies, dotted: grasshoppers).

<figure><img src="image_57.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_58.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_59.png" /></figure>
**Supplementary material S3.7** : Ce supplément volumineux n'étant pas indispensable à la compréhension du chapitre a été déplacé dans la partie « Annexes » du manuscrit de thèse par soucis de clarté.

Posterior estimates of the species-specific occurrence and detection probabilities of insect species as estimated under the MSOM (with mean, standard deviation, CI95%). All the species detected at least during a visit during the survey were listed and grouped according to their taxonomic group. 'Conservation status' refers to the three categories of specific-level conservation issues as described in Materials & methods. The number of observations (count) was also included for each species, under the classical and doubled surveys. For these both modalities, absolute bias in occurrence indicates the difference between the proportion of sites were each species was detected and its occurrence probability, as estimated through the MSOM. The minimum effort requirements to ensure species absence (with a 95% confidence index) were estimated for values of covariables allowing an optimal detectability.

**Supplementary material S3.8** : Ce supplément volumineux n'étant pas indispensable à la compréhension du chapitre a été déplacé dans la partie « Annexes » du manuscrit de thèse par soucis de clarté.

Posterior estimates for the effect of site covariates on observed insect species as estimated under the MSOM (with mean, standard deviation, CI95%): a1 – linear effect of the site area (ha); a2 – linear effect of habitat diversity; a3 – linear effect of the proportion of closed habitats (%); a4 – quadratic effect of the proportion of closed habitats (%). Grey cells indicate species with confidence interval not containing zero (considered as indicative of significant response to the parameter).

**Supplementary material S3.9** : Ce supplément volumineux n'étant pas indispensable à la compréhension du chapitre a été déplacé dans la partie « Annexes » du manuscrit de thèse par soucis de clarté.

Posterior estimates for the effect of visit covariates on observed insect species as estimated under the MSOM (with mean, standard deviation, CI95%): b1 – linear effect of the visit date; b2 – quadratic effect of the visit date; b3 – linear effect of the visit hour; b4 – linear effect of the visit length. Grey cells indicate species with confidence interval not containing zero (considered as indicative of significant response to the parameter).
Discussion générale
<figure><img src="image_60.png" /></figure>
L'accumulation de données de suivi à long terme et l'important développement des programmes de sciences participatives au cours des deux dernières décennies a permis un accroissement important du nombre de données sur la répartition et l'écologie des Rhopalocères et des Odonates à l'échelle de l'Europe (Lukyanenko et al., 2016). Ces deux groupes étant considérés comme des indicateurs écologiques pertinents, cette connaissance plus fine offre l'opportunité de dépasser certaines problématiques liées aux déficits de données qui limitent la construction de stratégies de conservation efficaces de l'entomofaune (Cardoso et al., 2011a). L'hétérogénéité de ces jeux de données, collectés via des protocoles différents ne visant pas les mêmes objectifs et à travers un effort d'inventaire très variable, a pu constituer une limite à leur pleine exploitation. Les récents développements techniques et méthodologiques offrent désormais l'opportunité de corriger ces biais afin d'analyser des patrons d'assemblage des communautés à des échelles spatiales et temporelles plus larges (Robertson et al., 2010 ; Gimenez et al., 2014).

Dans cette optique de valorisation des données naturalistes, ce travail de thèse a permis de décrire la dynamique récente des différentes espèces de Rhopalocères et d'Odonates à l'échelle de la France et d'identifier les principaux facteurs environnementaux qui la conditione. Il apporte ainsi des éléments en faveur d'une vision plus intégrée de la conservation de ces taxons, en prenant en compte l'hétérogénéité spatiale des enjeux de conservation et des pressions anthropiques. Á travers une perspective d'analyse « multi-espèces », il permet également de contribuer au changement de perspectives des programmes de conservation des insectes, nécessitant de se dégager d'une vision « espèce-centrée » et d'améliorer la prise en compte des interactions biotiques qui sous-tendent la structure des communautés (Harvey et al., 2017). Ce travail offre une vision plus large intégrant la variabilité de la réponse des espèces au sein des communautés, en lien avec leurs caractéristiques écologiques, et des liens indirects qu'elles peuvent entretenir à travers un recouvrement de leur niche trophique. Se positionnant à l'interface entre la recherche en écologie et la conservation appliquée, cette thèse contribue à l'articulation entre problématiques fondamentales et besoins opérationnels, en permettant à la fois une meilleure compréhension des mécanismes de structuration des communautés d'insectes et la formulation de recommandations pour une meilleure application locale des politiques de conservation. Le renforcement de ce lien constitue un enjeu majeur de la biologie de la conservation, pouvant permettre d'appuyer la mise en place de stratégies d'inventaire, d'optimiser la conservation en se basant sur une connaissance solide des systèmes biologiques
et de faciliter les retours d'expériences afin d'alimenter les développements méthodologiques
(Sunderland et al., 2009).

Changements de répartition des espèces et hétérogénéité des enjeux
de conservation

Le **Chapitre 1** a permis de décrire les patrons de changements de répartition des
Rhopalocères et des Odonates à l'échelle de la France au cours des 35 dernières années et
d'identifier les principaux processus qui les conditionnent, en lien avec les changements
environnementaux et l'écologie des espèces. L'application de modèles nuls à des données
diachroniques issues d'atlas de répartition a offert la possibilité de prendre en compte
l'hétérogénéité des assemblages d'espèces à l'échelle du territoire et la variabilité de la
réponse des différentes espèces au sein de ces assemblages (Schatz et al., 2016). Cette étude a
mis en évidence de forts contrastes spatiaux dans la dynamique de ces deux groupes, avec des
phénomènes de déclin particulièrement fort pour les Rhopalocères dans les départements du
nord-ouest de la France et des variations d'occurrences plus faibles et plus hétérogènes pour
les Odonates. Les résultats ont aussi révélé l'influence prépondérante des changements
d'occupation des sols sur l'appauvrissement des communautés de ces deux taxa. Longtemps
considéré comme un facteur ayant des impacts très localisés sur la diversité (Newbold et al.,
2015), cette modification des paysages est particulièrement profonde sur une large part du
territoire français et constitue une pression majeure pour l'entomofaune. L'urbanisation est
particulièrement impliquée dans le déclin des espèces, avec une réponse propre des
Rhopalocères à l'extension des secteurs d'agriculture intensive et des Odonates à la régression
des zones humides. Cette double réponse face à l'urbanisation montre l'intérêt de l'analyse
conjointe de deux taxa à l'écologie différente, permettant de couvrir les différentes
composantes du paysage affectées par les activités humaines (Cleary et al., 2004 ; Herrando et
al., 2016 ; Zeuss et al., 2017). L'influence du climat semble plus limitée ou masquée par ces
phénomènes à l'échelle de l'aire d'étude. Les espèces étendant leur aire vers le nord de
l'Europe doivent en effet s'appuyer sur l'existence d'habitats potentiellement favorables, que
l'artificialisation des paysages peut limiter. La majorité des espèces actuellement en
expansion sont des ubiquistes peu exigeantes dans le choix de leurs habitats (Parmesan et al.,
1999 ; Mattila et al., 2011).
La connaissance de cette hétérogénéité spatiale dans la dynamique des distributions d'espèces constitue un point important pour la planification de stratégies de conservation cohérentes, permettant l'allocation la plus pertinente des ressources humaines et financières. Les résultats issus de cette analyse montrent notamment une concentration progressive des enjeux de conservation sur certaines régions abritant encore des communautés diversifiées et la majorité des espèces menacées. C'est notamment le cas du pourtour méditerranéen et des secteurs d'altitude du sud et de l'est de la France. Ces régions sont actuellement soumises à des pressions susceptibles de s'accentuer dans les prochaines années. Sur un même territoire, l'artificialisation de certains secteurs s'intensifie, en lien avec une concentration de la population ou des activités récréatives), quand d'autres évoluent progressivement vers la fermeture du milieu. Parallèlement, du fait de l'accentuation des épisodes de sécheresse et de la pression de prélèvement pour les activités humaines, la ressource en eau tend à devenir limitante pour de nombreuses espèces notamment d'Odonates. Ce travail fourni ainsi des premiers éléments de priorisation des secteurs qui nécessitent une concentration des efforts de conservation. Ces dynamiques paysagères soulignent également la nécessité de mettre en place des mesures spécifiques pour tenter de contrôler ces pressions liées aux changements d'occupation des sols. Les listes d'espèces protégées constituent un des leviers d'actions principaux pour une prise en compte des enjeux liés à la conservation de la biodiversité dans politique d'aménagement. Á travers la procédure d'étude des impacts sur l'environnement, la détection d'espèces présentes sur ces listes constitueront un poids réglementaire pouvant conduire à la mise en place de procédures de réduction, d'évitement ou de compensation. La déclinaison de la liste nationale des insectes protégées en plusieurs listes régionales pourrait par exemple permettre de prendre en compte l'hétérogénéité des enjeux, en augmentant la liste initiale par un lot d'espèces constituant localement un enjeu important (Schatz et al., 2014).

La compilation d'une base de traits écologiques a également permis de révéler une hétérogénéité dans la réponse des espèces aux changements d'occupation des sols en lien avec les caractéristiques de leurs niches écologiques. Ces résultats permettent d'identifier les espèces présentant les taux de déclin les plus significatifs (principalement des spécialistes peu mobiles ou celles impliquées dans des interactions obligatoires) et fourni des éléments pouvant contribuer à une hiérarchisation plus cohérente des enjeux de conservation. Sur l'ensemble des espèces ayant enregistré un déclin significatif au cours des 35 dernières années, presque 80% ne sont couvertes par aucun statut de protection réglementaire. À
l'image des recherches récentes menées à l'échelle de l'Europe (Cardoso, 2012 ; Leandro et al., 2017), ces résultats plaident ainsi fortement en faveur d'une réévaluation des politiques publiques en matière de conservation mises en place en France et le développement d'une procédure facilitant la réévaluation de ces statuts à partir des résultats scientifiques d'identifications des vulnérabilités spécifiques et des menaces environnementales et humaines. La dernière mise à jour des listes de protection d'espèces date de la dernière décennie (2007), et elle ne constituait alors qu'une simple reprise de la première liste émise en 1993 pour les deux groupes étudiés. Bien que les Rhopalocères et les Odonates représentent l'essentiel des organismes couverts par ces listes, plusieurs espèces ont été sélectionnées pour leur aspect emblématique (par ex. l'Apollon, *Parnassius apollo* ; Samways, 1994) ou bien sur la base d'évaluation principalement valable pour le nord de l'Europe (par ex. l'Agrion de Mercure, *Coenagrion mercuriale*, Charpentier, 1840). Bien que certains projets sortant de ce cadre réglementaire et plus cohérents avec les évaluations des enjeux réels aient été développés avec succès (par ex. le PNA en faveurs des Odonates), la majeure partie des leviers institutionnels restent fortement liées à ces listes d'espèces protégées. Si d'espèces protégées par le droit français (ou de leurs habitats) constitue un des arguments les plus forts pour la définition des stratégies d'évitement, de réduction, et compensation dans le cadre des études d'impacts, la question de la représentativité de ces listes vis-à-vis de la vulnérabilité de la diversité entomologique devient critique.

Au-delà de ces conclusions, plusieurs biais liés au manque de résolution du jeu de données ont limité l'interprétation de certains résultats. L'absence d'information sur la répartition spatiale et temporelle de l'effort d'échantillonnage rend en effet difficile la quantification de l'exhaustivité et de la comparabilité des données. Plusieurs méthodes de correction, propres à ce type de bases de données hétérogènes, existent ou sont en cours de développement. Certaines se basent notamment sur l'utilisation d'indices non paramétriques de richesse asymptotique afin de calculer un nombre d'espèces potentiellement présente, servant de seuil pour évaluer l'exhaustivité de la connaissance de la maille (Romo et al., 2006 ; Bruno et al., 2012). Cependant, leur application nécessite d'avoir accès à des données brutes ou au moins à un nombre de données par unité de surface, utilisé comme proxy pour représenter l'effort d'échantillonnage (Lobo, 2008). Les atlas de répartition utilisés comme base du présent travail ne présentent qu'une agrégation de données à l'échelle départementale. Toutefois les programmes de centralisation des données naturalistes actuellement pilotés par le MNHN dans le cadre de l'INPN et du SINP pourraient offrir prochainement une alternative
intéressante pour affiner les résultats issus de cette étude à l'avenir. Actuellement très avancés pour ce qui est des Rhopalocères et des Odonates (P.Dupont, *comm.pers.*), ces programmes pourraient donner accès à une base plus complète des données ponctuelles ou maillées à l'échelle de la France, collectées à partir d'un réseau de référents répartis sur le territoire et via un contact avec les associations naturalistes locales. Cette base pourrait permettre d'affiner notre compréhension des variations de présence de ces taxa en intégrant dans les analyses statistiques plusieurs caractéristiques de ces inventaires comme l'effort d'échantillonnage par unité de surface (secteur ou département par exemple), la focalisation sur certaines espèces protégées en défaveur des espèces communes, la couverture spatiale des différents habitats, les conditions de réalisation de ces inventaires. Cette future base de données plus complète est donc prometteuse d'une meilleure connaissance et il faut espérer que les mesures de conservation associées soient à la hauteur de cette connaissance.

## Rôle de la spécificité des interactions biotiques dans la réponse des espèces

Cette première partie a mis en évidence l'influence significative de la position des espèces le long du continuum spécialistes-généralistes sur leurs capacités de réponse aux changements d'occupation du sol. Parmi les traits constitutifs de ce continuum, le degré de spécificité de la ressource trophique des espèces de Rhopalocères montre une variabilité importante, avec un déclin significativement plus fort des espèces monophages ou oligophages. Les plantes-nourricières sont en effet une des ressources majeures qui composent l'habitat des papillons de jour (Turlure, 2009). Afin de préciser les processus moteurs des patrons décrits dans ce premier chapitre, l'influence de cette part biotique de la niche des Rhopalocères sur leur état de conservation à l'échelle de la France a été explorée plus en détail dans le **Chapitre 2** à travers l'utilisation des métriques propres à la topologie des réseaux d'interactions.

Cette étude a permis de valoriser un important jeu de données (agrégé par P. Dupont du SNPN au MNHN) faisant la synthèse de toutes les plantes nourricières mentionnées pour chacun des Rhopalocères présents en France entre le 19ème siècle et la période actuelle. Cette synthèse a permis de construire un réseau d'interactions bipartite entre 241 papillons de jours et 527 plantes utilisées comme ressources trophiques par leurs chenilles. Elle a permis d'effectuer une première description des patrons d'association des espèces et des facteurs qui
les conditionnent (par ex. géographiques, écologiques, évolutifs, ...). Le réseau d'interactions s'est caractérisé par une structure significativement modulaire, qui semble principalement liée à l'histoire évolutives des espèces. En effet, les herbivores montrent une forte tendance à former des réseaux marqués un fort conservatisme phylogénétique, issus de phénomènes de co évolution et d'adaptation à l'exploitation de certaines ressources (Fontaine & Thébault, 2015). Dans le cas du réseau analysé durant cette thèse, les Rhopalocères appartenant à la même sous-famille ont tendance à s'associer préférentiellement au sein de modules composés de plantes généralement de la même famille. De plus, ce type de patrons et les relations spécifiques entre certaines sous-familles de Rhopalocères et familles de plantes-hôtes se révèlent extrêmement conservés à l'échelle du globe (Ferrer-Paris et al., 2013). Ils semblent conditionnés par une adaptation de certains Rhopalocères aux défenses chimiques développées spécifiquement par certaines espèces de plantes pour limiter l'impact de l'herbivorie (Muto-Fujita et al., 2017). La diversité de ces molécules de défense pourrait même constituer un trait beaucoup plus conservatif que la phylogénie des plantes (Endara et al., 2017). La forte similarité des résultats obtenus dans ce chapitre combinée aux données consultées dans des régions où la richesse et la composition de communautés de Rhopalocères est très différente pose des questions sur les processus évolutifs à large échelle qui ont conduit à l'émergence de ces patrons. Etant donné la faible proportion de familles de plantes consommées dans ces différentes régions, l'identification des caractéristiques communes des familles non exploitées et des facteurs susceptibles d'expliquer cette absence d'exploitation pourrait constituer une perspective de recherche fondamentale pertinente.

Du point de vue de la conservation des papillons de jour, cette thèse a permis de révéler un lien significatif entre le degré des Rhopalocères (le nombre de plantes qu'ils consomment) et leur risque d'extinction à l'échelle de la France. Les espèces qui sont intimement liées à une ou quelques plantes nourricières sont beaucoup plus vulnérables à l'évolution des paysages. Toutefois, la structure du réseau se caractérise par une absence de corrélation entre ce degré des Rhopalocères et le degré moyen des plantes-hôtes qu'ils consomment. La ressource indispensable aux espèces monophages ne leur est donc pas forcément exclusive et tous les papillons de jours associés à la même plante ne présenteront pas forcément le même déclin. Le réseau projeté des Rhopalocères a également été généré afin d'évaluer les possibilités de couverture indirectes des enjeux de conservation à travers les listes actuelles d'espèces protégées. En effet, les papillons de jour sont souvent considérés comme des espèces parapluies, dont la conservation permettrait d'assurer celles de tous les
groupes liés aux mêmes ressources (Braby & Williams, 2016). La perspective du réseau projeté permet d'évaluer le bénéfice potentiel de l'interdiction de destruction des habitats espèces protégées (incluant leurs plantes nourricières), telle que prévue par la loi française. Les papillons actuellement protégés se sont révélés peu reliés aux autres espèces à travers le réseau projeté. De plus, le développement d'un indice de spécificité des partenaires indirects n'a pas révélé de forte dépendance des Rhopalocères liés aux espèces protégées vis-à-vis des ressources qu'ils partagent avec celles-ci. Il s'agit principalement d'espèces oligophages ou polyphages, qui sont donc potentiellement susceptibles de répartir leurs effectifs sur plusieurs espèces de plantes nourricières. Sachant que ces partenaires indirects sont majoritairement peu menacés, la couverture indirecte des enjeux de conservation via le groupe d'espèces actuellement protégées parait limitée, et ces dernières ne semblent pas constituer des espèces parapluies pertinentes du simple point de vue de leurs ressources trophiques.

Cet axe de recherche a permis d'assembler un vaste jeu de données, incluant non seulement la synthèse des interactions papillons-plantes nourricières à l'échelle de la France mais également de nombreuses informations relatives à l'écologie, à la biologie, à la répartition ou au statut des espèces qui composent chacun de ces compartiments. Ces informations ont été synthétisées à partir d'atlas de répartition récents, de guides de terrain décrivant l'écologie des espèces, d'études scientifiques tentant de décrire les processus qui conditionnent l'assemblage des communautés de Rhopalocères (par ex. Bartonova et al., 2014), de référentiels taxonomiques, de politiques de conservation des insectes et des habitats naturels (par ex. listes de protection, plantes indicatrices pour la définition des habitats d'intérêts communautaires dans le cadre de Natura 2000, ...) ou encore de bases de données sur la chorologie et l'écologie des plantes (Julve, 2002), en s'attachant à rester proches des conditions de la zone d'étude. Cette synthèse pourra constituer une base de travail pour développer de nouveaux questionnements sur les facteurs qui structurent le réseau d'interactions, et le croisement de ces différentes sources pourrait permettre d'éclairer des processus plus évolutifs ou propres à l'écologie des communautés (Muto-Fujita et al., 2017 ; Nakadai et al., 2017).

Un travail préliminaire a notamment pu être engagé dans le cadre de cette thèse afin de mobiliser ce jeu de données pour décrire l'impact de certaines dynamiques paysagères sur les communautés de Rhopalocères, à travers le prisme des changements de structure engendrés au sein des assemblages de plantes qu'ils consomment (Welti et al., 2017). Des perspectives sur
l'étude de l'impact des successions secondaires liées à la déprise agricole, actuellement considérées comme un des facteurs de déclin et d'homogénéisation biotique pour les Rhopalocères en Europe (Stefanescu et al. 2011), ont notamment pu être explorées. En effet, contrairement aux impacts de l'intensification des pratiques agricoles, la réponse des invertébrés à la fermeture des milieux ouverts n'a été que peu étudiée en Europe (Cremene et al., 2005), et notamment sur le pourtour méditerranéen (Slancarova et al., 2016). Pour les Rhopalocères, ces travaux se basent principalement sur la comparaison de données d'inventaires d'imagos collectés sur des sites montrant divers degrés de fermeture paysagère (Stefanescu et al., 2009). Seule une étude menée en région tropicale sur la base d'un échantillonnage des chenilles a permis de comparer les paramètres descriptifs des réseaux Rhopalocères-plantes nourricières sur des parcelles à différent stades de succession et elle a montré une forte résilience du système (Villa-Galavitz et al., 2012). La base de données synthétisée dans le cadre de cette thèse pourrait fournir un point de départ pour répondre à ce type de problématiques. Elle permettrait de développer une approche plus holistique de ces dynamiques, en évaluant l'évolution de la structure du réseau et de la composition des communautés de Rhopalocères à travers la définition de plusieurs stades de successions potentiels.

D'un point de vue plus opérationnel, la connaissance précise des ressources qui composent l'habitat des espèces doit être un prérequis indispensable au développement de mesures de conservation et à la définition des protocoles qui visent à les détecter ou à les suivre dans le temps (Dennis, 2010). Les informations sur la liste des plantes-hôtes consommées et sur le degré de dépendance à chacune d'elle fournissent des éléments pour une meilleure localisation de ces habitats ou une meilleure évaluation de la capacité d'accueil d'un site. Dans le cadre de la politique européenne Natura 2000, le MNHN a récemment développé un programme d'évaluation de l'état de conservation des milieux ouverts agropastoraux, dont l'une des composantes passe par l'étude de l'intégrité des communautés de papillons de jour (Maciejewski et al., 2015). Cette intégrité est calculée à partir de la mesure de l'écart entre les communautés observées et les communautés potentielles sur le site. La composition des communautés potentielles sur un site est alors évaluée à partir de plusieurs filtres appliqués à un pool d'espèces connues à l'échelle du département où se trouve le site. Ces filtres fonctionnent sur la base du croisement de plusieurs traits propres aux espèces : habitats colonisés, phénologie, degré de sténoécie, capacités de dispersion, ... (Dupont, 2015). Cette base de traits peut également être utilisée pour la validation de données
d'inventaires, en lien avec la pression d'échantillonnage appliquée sur le terrain (Dupont, 2014b). De la même manière, la description actualisée du régime alimentaire des chenilles présentée dans le deuxième chapitre de cette thèse pourrait s'intégrer au sein de ce type de méthodes prédictives de la richesse ou de la composition des cortèges de Rhopalocères sur un site. Ce type d'outils pourrait permettre d'exploiter les résultats issus d'inventaires floristiques et la probabilité de cooccurrence de certaines espèces de Rhopalocères afin de contribuer à l'évaluation de l'exhaustivité des inventaires.

## Evaluation et amélioration des plans d'échantillonnage pour une mesure plus fiable des impacts sur l'environnement

Ces problématiques liées au manque et à la mesure de l'exhaustivité des inventaires biologiques représentent un des enjeux majeurs de la conservation appliquée, qui se développe en grande partie sur la base de données d'occurrence ou de mesures d'abondance d'espèces. Le **Chapitre 3** a permis de se placer en amont de la description des différents patrons à large échelle étudiés dans les chapitres précédents, en questionnant la représentativité des données d'occurrence collectées localement sur le terrain vis-à-vis de l'occupation réelle de l'espace par les insectes. S'intégrant dans une optique d'évaluation des politiques publiques, cette étude s'est ainsi attachée à quantifier l'exhaustivité des plans d'échantillonnage de l'entomofaune mis en place dans le cadre des études d'impacts. Ces plans pâtissent du poids des enjeux commerciaux dans la réalisation des études, lié aux objectifs de compétitivité des bureaux d'étude. Un équilibre est recherché entre une résolution suffisante des résultats de terrain pour une évaluation sérieuse des impacts et le maintien d'un coût le plus réduit possible. Toutefois cette qualité restait actuellement peu évaluée dans le cas particulier des insectes, qui constituent des groupes riches incluant de nombreuses espèces cryptiques et nécessitant le croisement de plusieurs méthodes d'échantillonnage.

Les visites de terrain qui ont permis de collecter les données analysées dans ce chapitre ont été réalisées sur la base de plans d'échantillonnage spécifiquement élaboré pour la réalisation d'études d'impacts. Le nombre de passages réalisés sur chaque site, la durée de ces passages, l'horaire auquel ils ont été conduits et les groupes d'insectes échantillonnés ont ainsi été définis conformément aux pratiques courantes du bureau d'étude Biotope en région Méditerranéenne. Les Orthoptères et les cigales sont ainsi venus s'ajouter aux deux taxa ciblés par la présente thèse (Rhopalocères et Odonates) afin de pas s'éloigner des standards de
ces études. Bien que ce prérequis ait amené une hétérogénéité importante dans la pression d'échantillonnage appliquée sur les différents sites, elle offre une vision de l'exhaustivité des inventaires entomologiques en conditions d'étude d'impact en prenant en compte les contraintes réelles auxquelles est soumis le bureau d'étude lors de la construction de ses offres.

<figure><img src="image_61.png" /><figcaption>**Figure 12 :** Probabilité de détection (a) et nombre minimum de passages nécessaire pour statuer sur l'absence (b) des différentes espèces protégées observées (associées aux différentes couleurs) au cours des inventaires présentés dans le chapitre 3 en fonction de la date. Réalisée à partir des estimations issues du MSOM.</figcaption></figure>

Le modèle d'occupation multi-espèces implémenté a permis d'obtenir des estimations de la richesse potentielle de la communauté, par site, par groupe taxonomique et en fonction du statut des espèces. Ces données ont été utilisées comme valeurs de références pour quantifier le degré d'exhaustivité moyen des relevés effectué sur un site dans le cadre d'une étude d'impact. La probabilité de détection et le biais absolu dans la connaissance de l'occupation ont pu être calculés pour chacune des espèces observées, et leur valeur moyenne a été comparée entre les différents taxa. Les résultats ont mis en évidence un décalage élevé entre les données collectées et la diversité entomologique potentiellement présente sur un site. Presque 50% de cette diversité est susceptible de ne pas être détectée en moyenne et une espèce présente sur un site aura en moyenne moins de 30% de chance d'être observée dans des conditions d'inventaire classique. L'estimation des probabilités d'occupation et de détection de chacune des espèces a également montré une différence de représentativité des données issues d'échantillonnages classiques en fonction de leur statut de conservation. Cette distinction est importante étant donné l'objectif particulier de détection des espèces protégées ou patrimoniales portés par les études d'impacts. Ce sont ces espèces qui constituent la
contrainte règlementaire majeure pour l'aménageur et qui conditionne l'importance des
investissements qu'il devra apporter pour éviter, réduire ou compenser les impacts de son
projet. L'estimation de leurs probabilités de détection via le modèle et l'étude de la variation
de ce paramètre en fonction des conditions de réalisation des relevés (par ex. date, heure,
durée, ...) apportent des éléments essentiels pour une répartition plus efficace de la pression
d'inventaire.

Ces résultats ont montré la nécessité de prendre en compte les biais liés à la détectabilité des espèces dans la procédure de définition des plans d'échantillonnages en condition d'étude d'impact. Les analyses réalisées dans ce chapitre présentent l'avantage de pouvoir conduire au développement d'outils opérationnels à la fois pour les bureaux d'études et pour les différentes structures chargées de poser un avis durant l'instruction des dossiers d'études d'impacts (DREAL, DDTM, CNPN, ...). La connaissance du pourcentage d'exhaustivité moyen d'une étude dans des conditions environnementales précises (par ex. l'aire du site, la diversité des habitats, ...) fourni une mesure du degré d'incertitude associé aux résultats des inventaires en fonction de l'effort appliqué. Elle permet de porter un regard plus critique sur la représentativité de la mesure des impacts associés à un projet. L'accès aux probabilités de détection de chaque espèce et la description de leur variation en fonction des conditions du relevé apportent des éléments intéressants pour une répartition plus efficace de l'effort lors de la construction des plans d'inventaires, en vue de maximiser la détection des enjeux pressenties. La connaissance des dates auxquelles les espèces protégées sont les plus détectables peut par exemple permettre une meilleure répartition des passages au cours de la saison, en fonction des potentialités d'accueil du site (Fig.12a). Ces probabilités spécifiques peuvent également être utilisées afin de déterminer un nombre minimum de passages nécessaires pour statuer sur l'absence d'une espèce avec un certain degré de confiance (Fig.12b) (Wintle et al., 2012). Ce type d'indice apporte des arguments pour fixer des conditions standard d'inventaire pour les espèces portant les enjeux les plus importants, ainsi que des éléments d'évaluation des inventaires réalisés avec un effort plus faible. In fine, l'interaction entre bureaux d'études et services instructeurs sur la base de ce type de travaux devrait aboutir à la définition de lignes directrices pour la planification et la réalisation des inventaires entomologiques dans le cadre des études d'impact, afin de fixer des conditions minimales en vue de garantir un certain degré de qualité. Les problématiques liées à la gestion de la superposition des préconisations pour les différentes espèces à enjeux potentielles sur un
même site nécessiteront d'être explorées dans le cadre du développement de ce type de lignes directrices.

Dans cette optique, les modèles de site-occupancy étendus à l'échelle multi-spécifique constituent un outil prometteur, offrant la possibilité de valoriser des données biologiques acquises suivant des plans d'échantillonnage parfois hétérogènes et pouvant comporter un nombre important d'espèces rares (notamment via les procédés de « shrinkage »). L'emboitement des différents niveaux hiérarchiques permet d'aller plus loin que l'estimation d'une simple richesse asymptotique en dégageant les biais sous-jacents liés à l'hétérogénéité de la probabilité de détection au sein de la communauté biologique. Les processus qui conditionnent cette hétérogénéité peuvent ensuite être décrits par l'intégration de covariables ou la distinction de groupes d'espèces. Les MSOM constituent ainsi une opportunité appropriée pour le cadre précis des études d'impacts, dans lequel les enjeux ciblés par site sont généralement multiples et couplé à une nécessité d'avoir une vision plus large (même sommaire) de la richesse des communautés présentes.

<figure><img src="image_62.png" /><figcaption>**Figure 13** : Exemple de grille d'estimation de la richesse potentielle (à gauche) d'un site en fonction d'une combinaison de valeurs de deux variables d'habitats. La grille de droite représente la précision des estimations pour les différentes combinaisons (dépendante des valeurs réelles des données en entrée). Réalisées à partir des résultats du MSOM issues du chapitre 3.</figcaption></figure>

L'intérêt croissant du cadre bayésien pour l'analyse des données en écologie a amené à une forte dynamique de recherche sur les MSOM au cours des 10 dernières années (Dorazio, 2016). Ces modèles sont applicables à des situations de plus en plus variées et incluent une diversité toujours plus grande de sources d'hétérogénéité de la détection des espèces. Ces
nouvelles techniques font apparaître plusieurs perspectives d'amélioration ou de précision des analyses d'exhaustivité menées dans le cadre de cette thèse. Á partir des probabilités d'occupation des espèces calculées sur les différents sites à travers le MSOM, il est notamment possible de prédire une richesse spécifique potentielle pour diverses combinaisons de covariables propres à ces sites (par exemple quelle est la richesse potentielle d'un site de 50ha composé à 50% de milieux fermés ?) (Mata et al., 2017) (Fig.13). Différents degrés de résolution peuvent être définis au niveau de l'échelle des covariables, mais la précision des estimations reste fortement dépendante de la richesse du jeu de données en entrée, notamment en termes de nombre de sites et de diversité des combinaisons de covariables (Zipkin et al., 2009). Dans le cadre des études d'impacts, la production de grilles de combinaisons plus exhaustive pourrait fournir des outils pour réaliser une évaluation grossière de la richesse potentielle des communautés d'insectes sur un site d'étude. Ce type d'information permettrait un meilleur ajustement des plans d'échantillonnages mais aussi une mesure de l'exhaustivité des données obtenues à l'issue des inventaires. D'autres développements récents mériteraient d'être étudiés pour l'amélioration de ce cadre d'analyse, notamment la possibilité de traiter dans un même modèle plusieurs régions aux caractéristiques différentes (par ex. recouvrant différents biomes ou habitats, avec des pools d'espèces différents) (Sutherland et al., 2016) ou la définition de groupes fonctionnels directement au sein de la structure hiérarchique du modèle (Tenan et al., 2017).

Cette étude peut être considérée comme une étape préliminaire qui pourrait déboucher sur le développement d'outils méthodologiques et commerciaux au sein de l'entreprise, pouvant être par la suite proposés aux services instructeurs en vue d'harmoniser les pratiques des bureaux d'études à l'échelle nationale. Le jeu de données collecté dans le cadre de cette thèse nécessiterait alors d'être enrichi en incluant de nouveaux sites afin d'obtenir des résultats plus robustes sur le plan statistique, que ce soit pour le calcul de la richesse des communautés ou pour l'estimation des paramètres spécifiques. Cet enrichissement pourrait notamment permettre d'améliorer l'estimation de l'effort minimum requis pour assurer un certain degré de détection de certaines espèces d'intérêts (par ex. les espèces protégées), en incluant un nombre plus important de covariables propres aux relevés et susceptible de l'influencer (par ex. paramètres météorologiques, expérience de l'observateur, ...). Une extension à d'autres types de milieux, d'autres régions ou d'autres groupes faunistiques permettrait d'envisager une déclinaison de ce type d'outils d'évaluation à une échelle plus large. En effet, les résultats quantitatifs issus de cette étude ne sont valables que dans des
conditions comparables aux inventaires qui ont permis de les obtenir, à savoir un échantillonnage des quatre taxa mentionnés plus hauts sur un site de plaine méditerranéenne dominé par des formations de garrigues et de friches sèches.
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