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**Supplementary materials 1**

**Supplementary material S1.1:** List of butterfly (A) and dragonfly (B) taxa (species and subspecies) included in the present study and values for their ecological attributes, protection statut and red list assessment at the scale of study area.

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

**Supplementary material S1.2:** Location and index of geographic units in France, Belgium and Luxembourg.

**Supplementary material S1.3:** Defined statuses of butterfly and dragonfly taxa in each geographic unit between the past and the recent period, according to their detection history.

**Supplementary material S1.4:** List of butterfly and dragonfly taxa excluded from the present analysis.

**Supplementary material S1.5:** Main land cover types and corresponding Corine Land Cover (CLC) categories and nomenclature considered in this study.

**Supplementary material S1.6:** Butterfly (a and b) and dragonfly (c and d) species richness in each administrative unit of the study area in 1980 (a and c) and 2015 (b and d).

**Supplementary material S1.7:** List of butterfly species observed in at least 10 French 'departements' during the studied period and showing a significant SES value of disappearance or appearance according to *null model 2*.

**Supplementary material S1.8:** List of dragonfly species observed in at least 10 French 'departements' during the studied period and showing a significant SES value of disappearance or appearance according to *null model 2*.
**Supplementary material S1.1:** 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.

**Table A.** List of butterfly taxa (species and subspecies) included in the present study and values for their ecological attributes, protection statut and red list assessment at the scale of study area: family (Hespé - Hesperiidae, Lycé - Lycaenidae, Nymphé - Nymphalidae, Papé - Papilionidae, Pier - Peridae, Rio - Riodinidae, Sat - Satyrinae), biogeographic background (Wide - widespread, North - northern Europe, South - southern Europe, Mount - mountain areas), bioclimatic zonation (Hill - hill zone, Meso - meso-mediterranean, Supra - supra-mediterranean, Mount - mountainous, Alp - alpine-subalpine), main breeding habitat (Alpine - alpine grassland, Dry - dry grassland, Humid - humid grassland, Meso - mesophile grassland, Shrub - shrubland), habitat specialization (Gen - generalist, Mid - mid-generalist, Spe - specialist), host plant specialization (1 - monophageous, 2 - oligophageous, 3 - polyphageous), host plant form (1 - ephemeral and small herbaceous, 2 - large herbaceous, 3 - bushes, 4 - large trees), mobility (1 - low abilities, 2 - medium abilities, 3 - high abilities), voltinism (1 - 0.5 generation, 2 - 1 generation, 3 - 1 or 2 generations, 4 - 2 generations, 5 - 2 to 3 generations, 6 - 3 to 4 generations), myrmecophily (1 - myrmecoxenous, 2 - facultative myrmecophilous, 3 - steadily myrmecophilous, 4 - obligatory myrmecophilous, 5 - no known interaction with ants), French protection status (Un - unprotected, Pr - protected), IUCN categories LC - least concern, NT - near threatened, VU - vulnerable, EN - endangered, CR - critically endangered).

**Table B.** List of dragonfly taxa (species and subspecies) included in the present study and values for their ecological attributes, protection statut and red list assessment at the scale of study area: family (Aesh - Aeshnidae, Calo - Calopterygidae, Cgstr - Cordulegastridae, Coen - Coenagrionidae, Culid - Corduliidae, Gom - Gomphidae, Lest - Lestidae, Lib - Libellulidae, Macr - Macromiidae, Platy - Platycnemididae), biogeographic background (Wide - widespread, North - northern Europe, South - southern Europe, Mount - mountain areas), bioclimatic zonation (Low - lowland, Hill - hill zone, Mount - mountainous), freshwater type (Lent. - lentic, Lot. - lotic, Both), main breeding habitat (Str. - stream, Riv. - river, Po. - pond, RivPo. - river and pond, Bog - marsh and bog), habitat specialization (Gen - generalist, Spe - specialist), mobility (1 - low abilities, 2 - medium abilities, 3 - high abilities), voltinism (1 - 0.5 generation, 2 - 1 generation, 3 - 1 or 2 generations, 3 - generations, 4 - 2 to 3 generations, 5 - 3 to 4 generations), French protection status (Un - unprotected, Pr - protected), IUCN categories LC - least concern, NT - near threatened, VU - vulnerable, EN - endangered, CR - critically endangered, RE - regionally extinct).
**Supplementary material S1.2:** Location and index of geographic units in France, Belgium and Luxembourg. Geographic units were defined as 96 ‘départements’ in France and 11 regions in Belgium, while Luxembourg was considered as a single geographic unit. Numbers on the maps are linked to the administrative name of each unit. Bold lines correspond to national limits and to the French “euroregions” as defined in 2015. White stripes cover geographical units included in the Mediterranean belt.

<figure><img src="image_28.png" /></figure>

<table><tr><td>1</td><td>- Ain</td><td>51</td><td>- Marne</td></tr><tr><td>2</td><td>- Aisne</td><td>52</td><td>- Haute-Marne</td></tr><tr><td>3</td><td>- Allier</td><td>53</td><td>- Mayenne</td></tr><tr><td>4</td><td>- Alpes de Haute-Provence</td><td>54</td><td>- Meurthe-et-Moselle</td></tr><tr><td>5</td><td>- Hautes-Alpes</td><td>55</td><td>- Meuse</td></tr><tr><td>6</td><td>- Alpes-Maritimes</td><td>56</td><td>- Morbihan</td></tr><tr><td>7</td><td>- Ardèche</td><td>57</td><td>- Moselle</td></tr><tr><td>8</td><td>- Ardennes</td><td>58</td><td>- Nièvre</td></tr><tr><td>9</td><td>- Ariège</td><td>59</td><td>- Nord</td></tr><tr><td>10</td><td>- Aube</td><td>60</td><td>- Oise</td></tr><tr><td>11</td><td>- Aude</td><td>61</td><td>- Orne</td></tr><tr><td>12</td><td>- Aveyron</td><td>62</td><td>- Pas-de-Calais</td></tr><tr><td>13</td><td>- Bouches du Rhône</td><td>63</td><td>- Puy-de-Dôme</td></tr><tr><td>14</td><td>- Calvados</td><td>64</td><td>- Pyrénées-Atlantiques</td></tr><tr><td>15</td><td>- Cantal</td><td>65</td><td>- Hautes-Pyrénées</td></tr><tr><td>16</td><td>- Charente</td><td>66</td><td>- Pyrénées-Orientales</td></tr><tr><td>17</td><td>- Charente Maritime</td><td>67</td><td>- Bas-Rhin</td></tr><tr><td>18</td><td>- Cher</td><td>68</td><td>- Haut-Rhin</td></tr><tr><td>19</td><td>- Corrèze</td><td>69</td><td>- Rhône</td></tr><tr><td>2A</td><td>- Corse du Sud</td><td>70</td><td>- Haute-Saône</td></tr><tr><td>2B</td><td>- Haute-Corse</td><td>71</td><td>- Saône-et-Loire</td></tr><tr><td>21</td><td>- Côte d'Or</td><td>72</td><td>- Sarthe</td></tr><tr><td>22</td><td>- Côtes d'Armor</td><td>73</td><td>- Savoie</td></tr><tr><td>23</td><td>- Creuse</td><td>74</td><td>- Haute-Savoie</td></tr><tr><td>24</td><td>- Dordogne</td><td>75</td><td>- Paris</td></tr><tr><td>25</td><td>- Doubs</td><td>76</td><td>- Seine-Maritime</td></tr><tr><td>26</td><td>- Drôme</td><td>77</td><td>- Seine-et-Marne</td></tr><tr><td>27</td><td>- Eure</td><td>78</td><td>- Yvelines</td></tr><tr><td>28</td><td>- Eure-et-Loir</td><td>79</td><td>- Deux-Sèvres</td></tr><tr><td>29</td><td>- Finistère</td><td>80</td><td>- Somme</td></tr><tr><td>30</td><td>- Gard</td><td>81</td><td>- Tarn</td></tr><tr><td>31</td><td>- Haute-Garonne</td><td>82</td><td>- Tarn-et-Garonne</td></tr><tr><td>32</td><td>- Gers</td><td>83</td><td>- Var</td></tr><tr><td>33</td><td>- Gironde</td><td>84</td><td>- Vaucluse</td></tr><tr><td>34</td><td>- Hérault</td><td>85</td><td>- Vendée</td></tr><tr><td>35</td><td>- Ille-et-Vilaine</td><td>86</td><td>- Vienne</td></tr><tr><td>36</td><td>- Indre</td><td>87</td><td>- Haute-Vienne</td></tr><tr><td>37</td><td>- Indre-et-Loire</td><td>88</td><td>- Vosges</td></tr><tr><td>38</td><td>- Isère</td><td>89</td><td>- Yonne</td></tr><tr><td>39</td><td>- Jura</td><td>90</td><td>- Territoire-de-Belfort</td></tr><tr><td>40</td><td>- Landes</td><td>91</td><td>- Essonne</td></tr><tr><td>41</td><td>- Loir-et-Cher</td><td>92</td><td>- Hauts-de-Seine</td></tr><tr><td>42</td><td>- Loire</td><td>93</td><td>- Seine-St-Denis</td></tr><tr><td>43</td><td>- Haute-Loire</td><td>94</td><td>- Val-de-Marne</td></tr><tr><td>44</td><td>- Loire-Atlantique</td><td>95</td><td>- Val-d'Oise</td></tr><tr><td>45</td><td>- Loiret</td><td>96</td><td>- Luxembourg</td></tr><tr><td>46</td><td>- Lot</td><td>97</td><td>- Hainaut</td></tr><tr><td>47</td><td>- Lot-et-Garonne</td><td>98</td><td>- Limbourg</td></tr><tr><td>48</td><td>- Lozère</td><td>99</td><td>- Bruxelles</td></tr><tr><td>49</td><td>- Maine-et-Loire</td><td>100</td><td>- Brabant-Flamand</td></tr><tr><td>50</td><td>- Manche</td><td>101</td><td>- Namur</td></tr><tr><td></td><td></td><td>102</td><td>- Luxembourg Belge</td></tr><tr><td></td><td></td><td>103</td><td>- Flandre-Orientale</td></tr></table>
<table><caption>**Supplementary material S1.3: Supplementary material S1.2:** Location and index of geographic units in France, Belgium and Luxembourg. Geographic units were defined as 96 'départements'.</caption><thead><tr><th></th><th>Before 1980</th><th>First atlas publication</th><th>Last atlas publication</th><th>Species status</th></tr></thead><tbody><tr><td>Butterfly data sources</td><td></td><td>Lafranchis 2000</td><td>Lafranchis et al. 2015</td><td></td></tr><tr><td>Dragonfly data sources</td><td></td><td>Grand & Boudot 2006</td><td>Grand et al. 2014</td><td></td></tr><tr><td rowspan="4">Species detection history in each geographic unit</td><td>Observed</td><td>Observed or absent</td><td>Observed</td><td>Stable</td></tr><tr><td>Absent</td><td>Observed</td><td>Observed</td><td>Stable</td></tr><tr><td>Observed</td><td>Observed or absent</td><td>Absent</td><td>Disappeared</td></tr><tr><td>Absent</td><td>Absent</td><td>Observed</td><td>Newly appeared</td></tr></tbody></table>

<caption>**Supplementary material S1.4: Supplementary material S1.2:** Location and index of geographic units in France, Belgium and Luxembourg. Geographic units were defined as 96 'départements'.</caption>

Some butterfly species indicated by Lafranchis et al. (2015) as part of the French fauna were not presented in the first published distribution atlas (Lafranchis 2000), because of several taxonomic revisions between the two publications (review in France by Dupont et al., 2013). Due to this lack of information, 11 butterfly species were excluded from the analysis: *Spialia therapne*, *Pyrgus malvoides*, *Iphiclides feisthamelii*, *Leptidea reali*, *Leptidea juvernica*, *Plebejus bellieri*, *Lasiommata paramegera*, *Hipparchia alcyone*, *Hipparchia genava*, *Erebia sthenyo*, *Aricia montensis*.

In the case of sub-species elevation to species rank, data concerning the recent period (Lafranchis et al. 2015) were pooled and analyzed under the former nominal species.

Six species for which the presence in France is considered doubtful were removed from the present work: *Erebia styx*, *Erebia tyndarus*, *Gegenes pumilio*, *Melitaea ornata*, *Polyommatus nivescens*, *Plebejus pylaon*

Four long-distance migrant species only known as punctual visitor in the study area were removed from the present work: *Anax junius*, *Danaus plexippus*, *Nymphalis xanthomelas*, *Vanessa virginiensis*
**Supplementary material S1.5:** Main land cover types and corresponding Corine Land Cover (CLC) categories and nomenclature considered in this study.

<table><thead><tr><th>CLC code</th><th>Description</th></tr></thead><tbody><tr><td colspan="2"><em>Urban areas</em></td></tr><tr><td>111</td><td>Continuous urban fabric</td></tr><tr><td>112</td><td>Discontinuous urban fabric</td></tr><tr><td>121</td><td>Industrial or commercial units</td></tr><tr><td>122</td><td>Road and rail networks and associated land</td></tr><tr><td>123</td><td>Port areas</td></tr><tr><td>124</td><td>Airports</td></tr><tr><td>131</td><td>Mineral extraction sites</td></tr><tr><td>132</td><td>Dump sites</td></tr><tr><td>133</td><td>Construction sites</td></tr><tr><td>141</td><td>Green urban areas</td></tr><tr><td>142</td><td>Sport and leisure facilities</td></tr><tr><td colspan="2"><em>Agricultural areas</em></td></tr><tr><td>211</td><td>Non irrigated arable land</td></tr><tr><td>212</td><td>Permanently irrigated land</td></tr><tr><td>213</td><td>Rice fields</td></tr><tr><td>221</td><td>Vineyards</td></tr><tr><td>222</td><td>Fruit trees and berry plantations</td></tr><tr><td>223</td><td>Olive groves</td></tr><tr><td colspan="2"><em>Closed natural areas</em></td></tr><tr><td>311</td><td>Broad-leaved forests</td></tr><tr><td>312</td><td>Coniferous forests</td></tr><tr><td>313</td><td>Mixed forests</td></tr><tr><td>322</td><td>Moors and heathlands</td></tr><tr><td>323</td><td>Sclerophyllous vegetation</td></tr><tr><td>324</td><td>Transitional woodland-shrubs</td></tr><tr><td colspan="2"><em>Open natural areas</em></td></tr><tr><td>321</td><td>Natural grasslands</td></tr><tr><td>231</td><td>Pastures</td></tr><tr><td colspan="2"><em>Wetland areas</em></td></tr><tr><td>411</td><td>Inland marshes</td></tr><tr><td>412</td><td>Peat bogs</td></tr><tr><td>511</td><td>Water courses</td></tr><tr><td>512</td><td>Water bodies</td></tr></tbody></table>
**Supplementary material S1.6:** Butterfly (a and b) and dragonfly (c and d) species richness in each administrative unit of the study area in 1980 (a and c) and 2015 (b and d). Bold lines correspond to the French “euroregions” as defined in 2015.

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>
**Supplementary material S1.7:** List of butterfly species observed in at least 10 French 'departements' during the studied period and showing a significant SES value of disappearance or appearance according to null model 2. Values were considered as significant for P-values < 0.05, after a correction for multiple comparisons using 'fdr' method. For each species, table indicates values of SESdisp or SES app, the number of 'departements' were the species was noted as appeared or disappeared (Obsunits), the total number of administrative units where the species was observed at least once (Total units), the ratio between these two values (Ratio), its main breading habitat (Habitat) and its status regarding the French protection list.

<table><thead><tr><th>Taxa</th><th>SES value</th><th>Obsunits/Total units</th><th>Ratio</th><th>Habitat</th><th>Protection</th></tr></thead><tbody><tr><td><strong>Disappearance</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Chazara briseis</td><td>14.26</td><td>51/74</td><td>69</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Pontia daplidice</td><td>14.19</td><td>58/88</td><td>66</td><td>Mesophile grassland</td><td>Unprotected</td></tr><tr><td>Coenonympha hero</td><td>10.72</td><td>28/30</td><td>93</td><td>Humid grassland</td><td>Protected</td></tr><tr><td>Polygonia egea</td><td>10.37</td><td>9/12</td><td>75</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Pyrgus cirsii</td><td>8.67</td><td>29/62</td><td>47</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Carcharodus floccifer</td><td>7.12</td><td>21/51</td><td>41</td><td>Mesophile grassland</td><td>Unprotected</td></tr><tr><td>Ephydryas maturna</td><td>6.75</td><td>17/21</td><td>81</td><td>Forest</td><td>Protected</td></tr><tr><td>Pyrgus carthami</td><td>6.66</td><td>23/58</td><td>40</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Lopinga achine</td><td>6.37</td><td>28/66</td><td>42</td><td>Forest</td><td>Protected</td></tr><tr><td>Maculinea alcon alcon</td><td>6.34</td><td>26/63</td><td>41</td><td>Humid grassland</td><td>Protected</td></tr><tr><td>Hyponephele lycaon</td><td>6.30</td><td>8/23</td><td>35</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Argynnis niobe</td><td>5.60</td><td>22/64</td><td>34</td><td>Mesophile grassland</td><td>Unprotected</td></tr><tr><td>Pyrgus alveus</td><td>5.26</td><td>23/68</td><td>34</td><td>Mesophile grassland</td><td>Unprotected</td></tr><tr><td>Euchloe crameri</td><td>5.23</td><td>21/65</td><td>32</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Pyrgus serratulae</td><td>5.03</td><td>22/68</td><td>32</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Polyommatus thersites</td><td>4.81</td><td>22/67</td><td>33</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Boloria euphrosyne</td><td>4.78</td><td>27/84</td><td>32</td><td>Forest</td><td>Unprotected</td></tr><tr><td>Coenonympha tullia</td><td>4.67</td><td>10/21</td><td>48</td><td>Humid grassland</td><td>Protected</td></tr><tr><td>Lampides boeticus</td><td>4.51</td><td>24/79</td><td>30</td><td>Shrubland</td><td>Unprotected</td></tr><tr><td>Pyrgus onopordi</td><td>4.49</td><td>7/27</td><td>26</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Parnassius apollo</td><td>4.12</td><td>8/32</td><td>25</td><td>Dry grassland</td><td>Protected</td></tr><tr><td>Carcharodus baeticus</td><td>4.07</td><td>4/13</td><td>31</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Polyommatus dorylas</td><td>4.003</td><td>10/42</td><td>24</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Hipparchia statilinus</td><td>3.90</td><td>18/62</td><td>29</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Erebia aethiops</td><td>3.71</td><td>11/41</td><td>27</td><td>Forest</td><td>Unprotected</td></tr><tr><td>Maculinea teleius</td><td>3.63</td><td>9/25</td><td>36</td><td>Humid grassland</td><td>Protected</td></tr><tr><td>Limenitis populi</td><td>3.57</td><td>21/65</td><td>32</td><td>Forest</td><td>Unprotected</td></tr><tr><td>Hipparchia fagi</td><td>3.49</td><td>18/71</td><td>25</td><td>Forest</td><td>Unprotected</td></tr><tr><td>Coenonympha oedippus</td><td>3.43</td><td>9/22</td><td>41</td><td>Humid grassland</td><td>Protected</td></tr><tr><td>Cupido osiris</td><td>3.42</td><td>6/28</td><td>21</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Pyrgus armoricanus</td><td>3.24</td><td>21/82</td><td>26</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Iolanas iolas</td><td>3.22</td><td>3/12</td><td>25</td><td>Forest</td><td>Unprotected</td></tr><tr><td>Hamaeris lucina</td><td>2.94</td><td>12/33</td><td>36</td><td>Humid grassland</td><td>Unprotected</td></tr><tr><td>Hipparchia semele</td><td>3.78</td><td>25/87</td><td>29</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Euchloe simplonia</td><td>3.02</td><td>3/13</td><td>23</td><td>Alpine grassland</td><td>Unprotected</td></tr><tr><td>Satyrus actaea</td><td>3.17</td><td>4/21</td><td>19</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Scolitantides orion</td><td>2.99</td><td>6/29</td><td>21</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Pieris mannii</td><td>2.88</td><td>9/44</td><td>20</td><td>Dry grassland</td><td>Unprotected</td></tr></tbody></table>
<table><tr><td>Hyponephele lupina</td><td>3.25</td><td>3/12</td><td>25</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Pseudophilotes baton</td><td>3.005</td><td>18/75</td><td>24</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Melitaea aurelia</td><td>2.89</td><td>11/30</td><td>37</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Polyommatus damon</td><td>2.83</td><td>4/19</td><td>21</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Colias palaeno</td><td>2.41</td><td>3/11</td><td>27</td><td>Humid grassland</td><td>Protected</td></tr><tr><td>Hipparchia fidia</td><td>2.35</td><td>3/16</td><td>19</td><td>Shrubland</td><td>Unprotected</td></tr><tr><td>Brenthis hecate</td><td>3.27</td><td>5/27</td><td>19</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Syrichtus proto</td><td>2.79</td><td>3/13</td><td>23</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Plebejus idas</td><td>2.68</td><td>18/77</td><td>23</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Maculinea alcon rebeli</td><td>2.69</td><td>11/45</td><td>24</td><td>Dry grassland</td><td>Protected</td></tr><tr><td>Melitaea parthenoides</td><td>2.79</td><td>21/85</td><td>25</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Satyrus ferula</td><td>2.38</td><td>4/28</td><td>14</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Boloria napaea</td><td>2.19</td><td>2/10</td><td>20</td><td>Alpine grassland</td><td>Unprotected</td></tr></table>

## Appearance

<table><tr><td>Cacyreus marshalli</td><td>20.30</td><td>32/51</td><td>63</td><td>Mesophile grassland</td><td>Unprotected</td></tr><tr><td>Scolitantides orion</td><td>6.90</td><td>8/29</td><td>28</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Cupido alcetas</td><td>6.58</td><td>11/50</td><td>22</td><td>Mesophile grassland</td><td>Unprotected</td></tr><tr><td>Satyrium pruni</td><td>6.15</td><td>11/73</td><td>15</td><td>Forest</td><td>Unprotected</td></tr><tr><td>Pieris mannii</td><td>5.26</td><td>8/44</td><td>18</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Melitaea diamina</td><td>5.14</td><td>10/75</td><td>13</td><td>Humid grassland</td><td>Unprotected</td></tr><tr><td>Maculinea alcon rebeli</td><td>4.87</td><td>6/45</td><td>13</td><td>Dry grassland</td><td>Protected</td></tr><tr><td>Brenthis daphne</td><td>4.67</td><td>9/73</td><td>12</td><td>Shrubland</td><td>Unprotected</td></tr><tr><td>Maculinea alcon alcon</td><td>4.42</td><td>8/63</td><td>13</td><td>Humid grassland</td><td>Protected</td></tr><tr><td>Pyrgus armoricanus</td><td>4.20</td><td>10/82</td><td>12</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Lycaena dispar</td><td>2.98</td><td>6/58</td><td>10</td><td>Humid grassland</td><td>Protected</td></tr><tr><td>Lasiommata petropolitana</td><td>2.99</td><td>2/14</td><td>14</td><td>Forest</td><td>Unprotected</td></tr><tr><td>Satyrus ferula</td><td>2.99</td><td>4/28</td><td>14</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Carcharodus lavatherae</td><td>2.78</td><td>3/26</td><td>12</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Plebejus argyrognomon</td><td>2.75</td><td>4/48</td><td>8</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Pyrgus bellieri</td><td>2.88</td><td>2/14</td><td>14</td><td>Dry grassland</td><td>Unprotected</td></tr><tr><td>Apatura iris</td><td>2.64</td><td>7/85</td><td>8</td><td>Forest</td><td>Unprotected</td></tr><tr><td>Satyrium spini</td><td>2.62</td><td>5/54</td><td>9</td><td>Shrubland</td><td>Unprotected</td></tr></table>
**Supplementary material S1.8:** List of dragonfly species observed in at least 10 French 'departements' during the studied period and showing a significant SES value of disappearance or appearance according to null model 2. Values were considered as significant for P-values < 0.05, after a correction for multiple comparisons using 'fdr' method. For each species, table indicates values of SESdisp or SES app, the number of 'departements' were the species was noted as appeared or disappeared (Obsunits), the total number of administrative units where the species was observed at least once (Total units), the ratio between these two values (Ratio), its main breading habitat (Habitat) and its status regarding the French protection list.

<table><thead><tr><th>Species</th><th>SES value</th><th>Obsunits/Total units</th><th>Ratio</th><th>Habitat</th><th>Protection</th></tr></thead><tbody><tr><td colspan="6"><strong>Disappearance</strong></td></tr><tr><td>Sympetrum depressiusculum</td><td>11.39</td><td>17/43</td><td>40</td><td>Pond</td><td>Unprotected</td></tr><tr><td>Leucorrhinia rubicunda</td><td>5.38</td><td>8/20</td><td>40</td><td>Marsh & Bog</td><td>Unprotected</td></tr><tr><td>Coenagrion hastulatum</td><td>4.61</td><td>7/36</td><td>19</td><td>Marsh & Bog</td><td>Unprotected</td></tr><tr><td>Leucorrhinia pectoralis</td><td>4.12</td><td>10/61</td><td>16</td><td>Marsh & Bog</td><td>Protected</td></tr><tr><td>Coenagrion ornatum</td><td>4.12</td><td>2/10</td><td>20</td><td>Steam</td><td>Unprotected</td></tr><tr><td>Epitheca bimaculata</td><td>3.31</td><td>6/42</td><td>14</td><td>Pond</td><td>Unprotected</td></tr><tr><td>Brachytron pratense</td><td>3.21</td><td>9/89</td><td>10</td><td>Pond</td><td>Unprotected</td></tr><tr><td>Leucorrhinia caudalis</td><td>2.52</td><td>5/41</td><td>12</td><td>Pond</td><td>Protected</td></tr><tr><td>Lestes macrostigma</td><td>3.04</td><td>2/11</td><td>18</td><td>Marsh & Bog</td><td>Unprotected</td></tr><tr><td>Coenagrion lunulatum</td><td>2.63</td><td>2/11</td><td>18</td><td>Marsh & Bog</td><td>Unprotected</td></tr><tr><td>Leucorrhinia albifrons</td><td>2.44</td><td>3/15</td><td>20</td><td>Marsh & Bog</td><td>Protected</td></tr><tr><td>Aeshna isoceles</td><td>2.52</td><td>8/79</td><td>10</td><td>Pond</td><td>Unprotected</td></tr><tr><td>Gomphus flavipes</td><td>2.61</td><td>4/33</td><td>12</td><td>River</td><td>Protected</td></tr><tr><td>Onychogomphus uncatus</td><td>2.44</td><td>4/47</td><td>9</td><td>Steam</td><td>Unprotected</td></tr><tr><td>Calopteryx virgo virgo</td><td>2.40</td><td>7/64</td><td>11</td><td>Steam</td><td>Unprotected</td></tr><tr><td>Sympetrum pedemontanum</td><td>2.31</td><td>5/38</td><td>13</td><td>River & Pond</td><td>Unprotected</td></tr><tr><td colspan="6"><strong>Appearance</strong></td></tr><tr><td>Hemianax ephippiger</td><td>20.40</td><td>34/62</td><td>55</td><td>Pond</td><td>Unprotected</td></tr><tr><td>Leucorrhinia pectoralis</td><td>12.82</td><td>20/61</td><td>33</td><td>Marsh & Bog</td><td>Protected</td></tr><tr><td>Gomphus flavipes</td><td>11.43</td><td>13/33</td><td>39</td><td>River</td><td>Protected</td></tr><tr><td>Leucorrhinia caudalis</td><td>8.06</td><td>12/41</td><td>29</td><td>Pond</td><td>Protected</td></tr><tr><td>Trithemis annulata</td><td>6.77</td><td>7/19</td><td>37</td><td>River & Pond</td><td>Unprotected</td></tr><tr><td>Gomphus graslinii</td><td>5.49</td><td>7/29</td><td>24</td><td>River</td><td>Protected</td></tr><tr><td>Coenagrion ornatum</td><td>3.77</td><td>3/10</td><td>30</td><td>Steam</td><td>Unprotected</td></tr><tr><td>Ophiogomphus cecilia</td><td>3.64</td><td>4/23</td><td>17</td><td>River</td><td>Protected</td></tr><tr><td>Oxygastra curtisii</td><td>3.11</td><td>8/77</td><td>10</td><td>River & Pond</td><td>Protected</td></tr><tr><td>Coenagrion caerulescens</td><td>2.58</td><td>3/19</td><td>16</td><td>Steam</td><td>Unprotected</td></tr><tr><td>Somatochlora arctica</td><td>2.60</td><td>3/34</td><td>9</td><td>Marsh & Bog</td><td>Unprotected</td></tr></tbody></table>
# Chapitre 2

Insight into butterfly-hostplant bipartite networks: using projected network as suitable tools for insect conservation
Ce deuxième chapitre s'intègre directement dans la suite des conclusions issues du chapitre 1 en termes de lien entre les exigences écologiques des espèces et leur capacité de réponse aux changements globaux. Les ressources trophiques constituent une des composantes essentielles de la niche des espèces. Du fait des interactions étroites qu'ils entretiennent avec leurs plantes nourricières au cours de leur phase larvaire, les Rhopalocères constituent un modèle d'étude approprié pour mener une investigation plus poussée de la relation entre le degré de spécificité de ces relations et l'état de conservation des espèces.

Ce travail est basé sur la première analyse d'un jeu de données faisant la synthèse de toutes les plantes nourricières mentionnées pour chacun des Rhopalocères présents en France entre le 19ème siècle et la période actuelle. La mise à disposition de ce jeu de données par le MNHN a offert l'opportunité de construire un réseau d'interactions bipartite entre 241 papillons de jours et 527 plantes utilisées comme ressources trophiques par leurs chenilles.

Dans un objectif de recherche plus fondamental, une première étape a visé à décrire la structure de ce réseau afin de dégager les patrons d'associations entre les Rhopalocères et leur plantes nourricières, de les comparer avec les patrons observés dans d'autres régions du globe et de tenter d'identifier les principaux facteurs (évolutifs, écologiques, géographiques) qui les conditionnent. Le lien entre la position des Rhopalocères au sein de ce réseau et leur risque d'extinction a été exploré dans une deuxième étape, à travers le calcul de métriques simples (degré, degré moyen de partenaires, ...). Le réseau projeté des Rhopalocères a enfin été utilisé pour visualiser les relations entre espèces à travers le partage des mêmes plantes nourricières et estimer le bénéfice potentiel des listes de protection actuelles pour la couverture indirecte des principaux enjeux de conservation.

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. Le jeu de données compilé dans cette analyse fournit des éléments précis et essentiels pour une meilleure localisation de ces habitats, permettant une meilleure évaluation de la capacité d'accueil d'un site lors de la réalisation d'une étude d'impact sur l'environnement ou de la mise en place d'une mesure de compensation écologique.
# Insight into butterfly-hostplant bipartite networks: using projected network as suitable tools for insect conservation

DELPON Gaël<fnref n="1" />,<fnref n="2" />, DUPONT Pascal<fnref n="3" />, RICHARD Franck<fnref n="1" /> and SCHATZ Bertrand<fnref n="1" />

## Abstract

1. The butterflies are herbivorous insects with varying degrees of specificity to hostplant depending on the species. Host-range is an essential component of their ecological niche and the specificity of the link with hostplants have an important influence on species patterns of presence.

2. In the present study, we investigate the structure of the bipartite network between 241 French butterflies and 527 hostplants interactions, characterized by 768 nodes and 1094 two-species links. Our aim is (i) to identify the determinants of species host-range, (ii) to assess the relationships between the position of butterfly species in this network and their conservation issue and (iii) to explore projected links between butterflies through hostplant to evaluate potential coverage of these conservation issues through the current lists of protected species.

3. Our results reveal a highly modular structure of butterfly-hostplant network, significantly correlates with butterfly's taxonomic distances. Modules are constrained by the privileged association of certain butterfly families with certain hostplant families. This structure appears strongly conserved among different regions of the world, with a great similarity in the concerned taxa.

4. The analysis showed a significant relationship between extinction risk of butterflies in France and a high level of host specificity. The lack of correlation between the butterfly's degree and the average degrees of their hostplants showed that the hosts of specialist butterflies were not necessarily exclusive to them. Protected butterfly species were indirectly related to few other butterflies through the projected network. These indirect partners themselves were not highly dependent to the shared hostplants and they did not represent significant conservation issues. This pattern could allow deducing (i) a low potential of indirect protection through the current lists, and (ii) a weak influence of the sharing of the same resource on the extinction risk of two distinct butterfly species.

5. Network models help to map the relationship of butterflies with hostplants and to focus attention to potential biodiversity losses and potential homogenization.

**Key words:** Plant-insect interactions, herbivory, bipartite network, modularity, conservation.

<footnote n="1">¹ Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), UMR CNRS 5175, 1919 route de Mende, 34293 Montpellier cedex 5, France</footnote>

<footnote n="2">² Biotope, 22 Boulevard Maréchal Foch, 34140 Mèze, France</footnote>

<footnote n="3">³ Museum National d'Histoire Naturelle, 36 rue Geoffroy-Saint Hilaire, 75005 Paris, France</footnote>
## II.1. Introduction

Protect ecosystem integrity and species diversity is at the core of all conservation or restoration management actions. Protecting rare, threatened or emblematic species has guided conservation strategies since the emergence of conservation biology (Jenkins & Ricklefs, 2011). Some taxa support, through the dependence of associated partners, a significant diversity and could be considered as "keystone" species in the local ecosystem (Mills et al., 1993). The interaction among species in their community has an increasing weight in the assessment of conservation priorities and helps to go from species-centered approaches to systemic conservation strategies (Jordán et al., 2007; Cadotte et al., 2011). Facing cost-effective issues of such strategies (Layock et al., 2009), this initially species-centered view has progressively evolved towards a more integrative vision, going towards the conservation of ecosystem functions and the biotic interactions that underlie them (Heleno et al., 2012; Pollock et al., 2017). Beyond the list of its elements (genes, species, habitats, landscapes), biodiversity also concerns complex networks of ecological interactions, from mutual (e.g. plant-pollinator) to antagonistic (e.g. host-parasite) (Montoya et al., 2006; Ings et al., 2009), as well as from direct to indirect (Williams et al., 2002). Investigating the structure of these networks and their stability is now seen as an important way to understanding community structure (Albrecht et al., 2017; Bascompte & Jordano, 2007; Thébault & Fontaine, 2010). This growing interest in biotic interactions has also increased with a better awareness of co-extinction processes (Koh et al., 2004a; Dunn et al., 2009). The decline of a species in response to environmental changes will inevitably lead to the cascading effects on specifically associated partners (Allesina & Bodini, 2004; Dunne & Williams, 2009). As consequence, global changes favor a general homogenization of species and interactions, i.e. generalist species and opportunist interactions (Albrecht et al., 2017; Bascompte & Jordano, 2007; Eskildsen et al., 2015).

In such context, measuring changes in biodiversity requires relevant and specific methods (Vogt-Schilb et al., 2016; Joffard et al., submitted). During the last decade, network approaches have been developed to model the complexity of biotic interactions (Proulx et al., 2005; Bascompte, 2010). By representing the topology of associations (links) between species (nodes) from two groups of organisms (Latapy et al., 2008), bipartite ecological networks have been used to address important ecological (Bastolla et al., 2009), functional (Coux et al., 2016) or evolutionary issues (Krasnov et al., 2012). Many diverse metrics have
been introduced to evidence general patterns in bipartite networks such as centrality (Martín González et al., 2010), modularity (Olesen et al., 2007), nestedness (Staniczenko et al., 2013) or robustness and stability facing potential environmental change (Pocock et al., 2012). The study of relationships between species through their projected network has also provided new insight about complex community assemblages through indirect interactions, such as facilitation or competition (Nacher & Akutsu, 2013; Taudière et al., 2015). This new analytical framework was seen as an opportunity to narrow the gap between concepts in ecology and applied conservation biology (Harvey et al., 2017). Indeed, several studies have used network analysis to investigate the effects of habitat alterations and land use changes on biotic interactions (Weiner et al., 2014; Villa-Galaviz et al., 2012; Staniczenko et al., 2017). Several authors recently advocated for the application of these theoretical metrics to the field of applied conservation biology in order to develop more relevant planning and monitoring (Tylianakis et al., 2010; Kaiser-Bunbury & Blüthgen, 2015). In this context, the relationships between plants and insects, largely present in different ecosystems, have been the subject of particular attention (Evans et al., 2017), especially by the study of plant-pollinator (Astegiano et al., 2015) and herbivorous plants interaction networks (De Araújo, 2016).

With more than 900,000 species on earth, insects are the most diverse group of organisms (Foottit & Adler, 2009) and form the basis of most food webs (Strong et al., 1984; Moir et al., 2014). About half of the insect species described are specifically related to plants through herbivory (Speight et al., 1999; War et al., 2012). These trophic interactions constitute one of the most important energy flows in terrestrial ecosystems (Metcalfe et al., 2014; Forister et al., 2015) and strongly influence the biomass and diversity of plant communities (Carson & Root, 2000; Bagchi et al., 2014). Herbivorous insects generally show significant conservatism in their interactions with hostplants (Fontaine & Thébault, 2015), resulting from coevolution and fine adaptation to plants’ defenses (Cagnolo et al., 2011; Jensen et al., 2011; Ibanez et al., 2016). These evolutionary histories have led to a high specificity of many taxa for the exploitation of a particular plant species or genus (Richards et al., 2015; Moir et al., 2016). Because of this often specific links, plants represent essential nodes that condition the robustness of many networks and the maintenance of a wide diversity of herbivorous insects (Koh et al., 2004b; Fonseca, 2009; Schleuning et al., 2016).

Lepidoptera represent the largest lineage of herbivorous organisms, whose diversification has accompanied the explosive radiation of angiosperms (Regier et al., 2013;
Vila, 2014; Schatz et al., 2017). Most species feed on plants during at least a part of their larval stage, with a variable specificity depending on the considered taxonomic group or geographic area (Dyer et al., 2007; Muto-Fujita et al., 2017). Butterflies constitute one of the best studied groups of Lepidoptera at the scale of Europe in terms of ecology and distribution (Thomas, 2005; Lewis & Senior, 2011). The bipartite interactions linking host plants to the butterfly species are known more and more extensively within several countries (Robinson et al., 2010; Ferrer-Paris et al., 2013; Eskildsen et al., 2015; Muto-Fujita et al., 2017). Host range is an essential component of butterfly biology and the distribution of these resources significantly conditions the suitability of habitats (Dennis et al., 2006; Vanreusel & Van Dyck, 2007). The specificity of the plant-butterfly link also shapes spatio-temporal patterns of species (Cizek et al., 2006; Altermatt, 2010; Slove & Janz, 2011), and plays important role on the stability of its populations (León-Cortés et al., 2003; Komonen et al., 2004) and on metapopulation-specific parameters, such as colonization or local dispersal processes (Krämer et al., 2012; Fourcade & Öckinger, 2017; Salz & Fartmann, 2017).

Butterflies show a general decline at the scale of Europe since the beginning of the last century, both in terms of range and size of populations (Warren et al., 2001; Habel et al., 2016; Delpon et al., in press). The most specialized species present the largest regression, and several local to regional extinctions could be related to the negative impacts of landscape changes on hostplant populations (Hardy et al., 2007; Pearse & Altermatt, 2013; Brunbjerg et al., 2017). Several studies also showed a strong correlation between the local richness of floristic communities and the specific richness of butterflies in thermophilous habitat (Hawkins & Porter, 2003; Cremene et al., 2005), and the decrease of the first is usually followed by an impoverishment of butterfly communities (Haddad et al., 2009, Pöyry et al., 2009). However, the topological characteristics of the butterfly-hostplant interaction network, which partly underlie the structuring of butterfly communities, have been poorly investigated in Europe. Studies of the link between population dynamics and hostplant range are usually centered on a unique butterfly species (Krauss et al., 2005; Krämer et al., 2012; Cormont et al., 2013). Strategies built around community modules, including all closely interdependent species, have been developed to allow more consistent conservation of some iconic butterfly species (Hochberg et al., 1996; Mouquet et al., 2005). However, no synthesis of butterfly-plant interactions has been exploited in order to evaluate the indirect links between butterfly species, through a sharing of host plants, from an integrated conservation perspective.
In the present study, we describe the structure of a large ecological bipartite network of butterfly-hostplant interactions at the scale of France. Using projected networks, the indirect links between butterflies through shared hostplants are analyzed. We pay a particular attention to protected species, in order to analyze their position within the interaction network. This network was constructed from a qualitative dataset summarizing field records of caterpillar's development of 241 French butterfly species on 527 plant species, based on an exhaustive literature review. More especially, we address the following questions: (1) Could the association patterns of butterflies be predicted by the degree of co-occurrence, the similarity of breeding habitat and the taxonomic distance of butterfly? (2) Is there a significant correlation between the degree of specialization of a butterfly species and the attractiveness of its hostplants for other species? (3) Is there a correlation between butterfly specialization and their extinction risk? (4) Do current protection policies allow, through the projected network, an indirect coverage of conservation issues associated with butterflies?

## II.2. Material & methods

### II.2.1. Butterfly-hostplant interaction matrix

The present study was focused on the analysis of a large dataset that summarized hostplant species consumed by French butterfly species during their larval stage. This dataset was assembled by one of the coauthors (P.D.), on the basis of a literature review including 79 references (specific studies, regional syntheses, atlas of biodiversity, online databases ...) published between 1887 and today in eight European countries (France, Germany, England, Belgium, Spain, Italy, Sweden, Switzerland), but only targeting butterfly and plant species present in France. The interaction data collected are only qualitative and coded in the form of an incidence matrix (observation (1) or non-observation (0) of caterpillars feeding on a plant). The strength of each interaction could not be quantified and local variations in butterfly diet within the study area (e.g. Meister et al., 2015) were not taken into account. The dataset was constructed only with field observations, and interactions observed within farms or greenhouses were excluded. Duplicate interactions due to synonymy and those involving taxa identified at higher ranks than species (i.e. genera or families) were also excluded. Butterfly and plant species names have been homogenized based on the most recent taxonomic reference at the scale of the study area (TaxRef v.10.0, www.inpn.mnhn.fr). Subspecies and varieties have been systematically reduced to specific resolution.
This database grouped 2232 interactions between 249 species of butterflies and 830 species of plants. All the observations for which the nature of the data or the initial source could not be clearly identified were removed from the analysis. In the end, this led to a database of 1094 interactions between 241 butterfly species (distributed among 6 families; Suppl.S2.1) and 527 plant species (distributed among 53 families; Suppl.S2.2). This represents 92% of butterfly species currently referenced in France (Lafranchis *et al.*, 2015). The exploited hostplants represent approximately 27% of families and 9% of species referenced in France for these families (Bock *et al.*, 2013).

## II.2.2. Distance matrices

Three matrices were assembled to represent different components of the distance separating each of the butterfly species included in the analysis: geographic distance, ecological distance, and taxonomic distance. The first matrix was extracted from the most recent synthesis of butterfly distribution at the scale of the study area (Lafranchis *et al.*, 2015). According to this synthesis, France was divided into 96 administrative units (AU, called "departments"). Species range was based on AUs where butterflies occurred between 2000 and 2015.

A matrix of ecological distance between different butterfly species was constructed by extracting the characteristics of their main breeding habitats known in France (Lafranchis *et al.*, 2015). These features have been summarized into 6 major habitat types in which each species has been ranked (1) if the species breeds in the habitat, (0) if not): (1) mesophilous grasslands, (2) dry grasslands, (3) humid grasslands, (4) alpine grasslands, (5) shrublands, (6) forests.

In order to test the influence of butterfly taxonomy on their proximity within the interaction network, a taxonomic distance matrix between butterfly species was constructed and used as a proxy for their phylogenetic distance (Koehler *et al.*, 2012). Using a method already applied for the analysis of several ecological networks (Rezende *et al.*, 2007; Bellay *et al.*, 2015), the matrix of taxonomic distance (MTD) was calculated on the basis of the following equation:

$$ MTD = Md - (F_{\omega} + Sf_{\omega} + G_{\omega} + Sp_{\omega}) \quad (1) $$
where *Md* is the maximum distance found in the butterfly database (maximum distance = 4, referring to the taxonomic category class) and *F*<sub>ω</sub>, *Sf*<sub>ω</sub>, *G*<sub>ω</sub> and *Sp*<sub>ω</sub> are matrices for each taxonomic category (family, subfamily, genus and species, respectively) generated by the function *weight.taxo* available in package Ape (Paradis *et al.*, 2004) for R. The lower the value of the index calculated between two species, the closer they are within the taxonomic classification. Therefore, species within the same genus exhibit a value of taxonomic distance (*td*) equal to 1, different genera have *td*= 2, different subfamilies have *td*= 3 and different families have *td*= 4. The name currently used for each taxonomic degree has been extracted from the French taxonomic reference system TaxRef (TaxRef v.10.0, www.inpn.mnhn.fr).

II.2.3. Species conservation status

The protection status and IUCN red list of each butterfly species was compiled from the most
recent assessments at the scale of the study area (Suppl.S2.1). Each species was defined as
“unprotected” or “protected” at the national level based on the French list of protected insects
(https://inpn.mnhn.fr/programme/reglementation). According to their red list categories
(UICN France et al., 2014), they were classified as “least concern” (LC), “near threatened”
(NT), “vulnerable” (VU), “endangered” (EN), “critically endangered” (CR), and “extinct” (at
the French scale) (EX). Species categorized as VU, EN or CR were grouped into a
“threatened” category for our analysis. Taxa ranked as ‘not evaluated’ (NE) or ‘not
applicable’ (NA: introduced or vagrant taxa) were excluded from the analysis.

II.2.4. Analysis of bipartite and projected networks

An ecological bipartite network was constructed from the butterfly-hostplant association matrix using the package Igraph (Csárdi & Nepusz, 2006) in the R software (R 3.3.3, R Development Core Team, 2015) and the software GEPHI v.0.9.1 (Bastian et al., 2009). Following network theory terminology, we estimated the degree of each butterfly species in the bipartite network *k*<sub>b</sub> as the number of hostpálant species with which they interact (with *k*<sub>p</sub> corresponding to hostplant bipartite degree). This value was considered as a proxy of butterfly diet specialization (saw in this paper as the extent of the theoretical biotic part of the niche of butterfly species). In order to explore the attractiveness of the butterfly resource for other species, we estimated the mean degree of plants associated with each butterfly in the bipartite network *c*<sub>b</sub> (defined as “partner specialization” of *b* by Taudière et al., 2015). The projected network of butterflies was also constructed to visualize the indirect interactions established
between these species through the sharing of one or more hostplants. In order to quantify the importance of this sharing, the projected degree $I_b$ was calculated for each butterfly species as the number of partners to which it was indirectly related in the projected network. This projected network allowed us to explore the potential indirect coverage of butterfly conservation issues through the sharing of a resource with a protected species.

From these values $k$ and $c$, we developed an index ($D$) allowing to quantify the level of dependence of the partners of a butterfly species in the projected network to the hostplants they shared with this species. Assuming that the interactions between a butterfly and each of its hostplants have the same weight in the bipartite network (no primary/secondary host differentiation), a butterfly with a high degree $k_b$ will be less dependent to each hostplant. The value of $D$ of a butterfly was calculated by estimating the average degree of species related to each of its hostplants, using the following equation:

$$D_b = \frac{\sum_{p}^{1} c_p}{k_b} \qquad (2)$$

where $D_b$ corresponded to the average dependence of the indirect partners of the butterfly $b$, $c_p$ corresponded to the partner specialization of each of the plants $p$ with which $b$ interacts in the bipartite network and $k_b$ corresponded to the degree of $b$. In order to avoid an artificial inflation of $D_b$, species $b$ was not taken into account when calculating the value of $c_p$ for each of its plants ($D_b$ could not be calculated for butterfly species with a projected degree of zero). Given this formulation, a butterfly characterized by a low value of $D$ was associated, in the projected network, with species highly dependent of the same hostplants (see Suppl.S2.3).

## II.2.5. Null models

We simulated randomized network through two different null models in order to test the significance of patterns observed in the network structure, following the method of Taudière et al. (2015). The null model 1 consisted in a simple randomization, fixing only the total dimensions of the network while maintaining a mean degree of butterflies and plants in the bipartite network identical to that of the observed network. The null model 2 randomized the links between species while fixing the degree distribution within the bipartite network (the degree of each species was identical to that in the observed network). Null model 1 has been
implemented through the *permatfull* function of the *Vegan* package in R and the null model 2 with the *quasiswap* method of the *permatswap* function of the same package (with 999 permutations for each model) (Patefield, 1981). The estimation of the deviation from the simulated value allowed to evaluate whether the *k*, *c* and *l* values of the different species are higher or lower than expected by chance. It was calculated in each case from the following equation:

$$SES = \frac{(x - \mu)}{\sigma} \qquad (3)$$

where *x* is the actual observed value (of *k*, *c* or *l*), *μ* is the mean of the null values, and *σ* is the standard deviation of null values. The use of these two models allowed to evaluate the part of the observed patterns which was solely related to the degree distribution within the network.

## II.2.6. Network topology

In order to describe the structure of the butterfly-hostplant network, the modularity (*M*) of the bipartite network was analyzed through the algorithm of Newman & Girvan (2003) implemented in the NETCARTO software (Guimerà & Amaral, 2005). This value, ranging from 0 to 1, refers to a measure of the level at which species appear to be organized in modules. These modules corresponded to subsets of species that tend to interact more with each other than with other species of the network (Krause *et al.*, 2003). The significance of this measurement was evaluated using the same software, which generated random network simulations (999 randomizations) with the same degree distribution among species (Guimerà *et al.*, 2004). The membership of each species of the network to some module was estimated using the Igraph package in R, through a partitioning of the network allowing a maximization of the modularity (i.e. partitions of the original graph were only allowed if they increased modularity) (Newman, 2006). The role of each plant species in the network structure was also evaluated according to its topological properties. The position of each species has been defined by the crossing of the estimated values of within module degree (*z*, position relative to the other species of the module) and among-module connectivity (*c*, connection with the species of the other modules) (Santamaría *et al.*, 2014). These values were calculated using the NETCARTO software and the plant species were divided into four categories according to Olesen *et al.* (2007) (peripherals, connectors, module hubs and network hubs).
## II.2.7. Statistical analysis

A Mantel test was performed to test the influence of geographical, ecological and taxonomic distance between butterfly species on their position within the network. Each of these explanatory matrices as well as the butterfly-plant association matrix were converted into dissimilarity matrices using the Jaccard index available function *vegdist* of the Vegan package (Oksanen et al., 2014). Mantel test was run using 9 999 randomizations and Spearman's rank correlation. In order to evaluate the link between butterfly specialization and their vulnerability, the degree $k_b$ in the bipartite network was compared between butterflies characterized by different IUCN categories by means of a Kruskall-Wallis H test, followed by pairwise comparisons by the Wilcoxon rank-sum test, with p-values adjusted using the 'fdr' correction (Benjamini & Hochberg, 1995). We compared the degrees of butterflies in the bipartite network ($k_b$), their degree in the projected network ($l_b$), the mean degree of their hostplants ($c_b$) and the specialization of their indirect partner ($D_b$) between protected and unprotected species, using Wilcoxon's signed-rank tests. Finally, a Fisher's exact test was performed to assess independence between the coverage of species by current protection policies (directly protected, indirectly protected through the proposed network, unprotected) and their classification within the IUCN categories.

## II.3. Results

### II.3.1. Topological features of the network

The bipartite butterfly-hostplant interaction network consists in 768 nodes (241 butterflies and 527 plants) and 1094 links (Fig.2.1). Most butterflies interact with few plants. About 75% of the species feed on one to five hostplants and about 17% of the species are monophageous, specialized on a single plant resource. The degree of butterflies in the bipartite network ranges from 1 to 29 (*Callophrys rubi* has the higher number of hostplants), with an average value of 4.53 ± 4.25 (Suppl.S2.4a). Plants show an even higher degree of specialization, with about 95% of the species being consumed by only 1 to 5 butterflies, and 60% by a single species. Their degree varied between 1 and 27 (*Festuca ovina* was consumed by the highest number of species), with an average value of 2.08 ± 2.19 (Suppl.S2.4b).
<figure><img src="image_33.png" /></figure>

**Figure 2.1** : Graphical representation of the bipartite butterfly-hostplants network based on the spatialization algorithm Force Atlas 2 (Jacomy et al., 2014) implemented in the software GEPHI (Bastian et al., 2009). Circles correspond to species of butterflies and hostplants and their size is proportional to species’ degrees. Grey circles represent hostplants and colored circles correspond to butterfly families: Lycaenidae in blue, Papilionidae in purple, Pieridae in yellow, Nymphalidae in green, Hesperiidae in orange and Riodinidae in cyan.
<figure><img src="image_34.png" /></figure>

**Figure 2.2:** Role of each plant in the bipartite butterfly-hostplant network based on a modularity analysis. Solid lines correspond to the limits between each topological role, as defined by Olesen et al., (2007). Colored dots correspond to plants hosting protected butterfly species and grey dots represent other plant species.

<figure><img src="image_35.png" /></figure>

**Figure 2.3:** Distribution of butterfly species according to their degree in the bipartite network (kb) and to the mean degree of their related hostplants (cb). Green dots correspond to protected butterfly species (n = 26) and grey dots represent unprotected species (n = 215). Orange dots correspond to the mean position of butterfly species according to the null model 2, in which links are randomly distributed at fixed bipartite degree. Grey bars correspond to their 95% confidence intervals.
The studied network had a significant modular structure (M = 0.852) made of 39 modules (Suppl.S2.5). About 20 independent modules, containing between one to six butterfly species, were identified. They were mainly composed by butterflies with a low degree, which belonged to three families: Lycaenidae, Nymphalidae and Papilionidae (Fig.2.1). The two largest modules, including more than 10 species of butterflies, were completely independent of the rest of the network, which was structured into a set of interconnected modules. The modules mainly associated butterflies belonging to the same family (and usually to the same subfamily) and consuming taxonomically related plants. Most butterfly species (60%), regardless of their degree, were feeding on plants belonging to the same family. Hostplant preferences differed clearly among butterfly subfamilies. Some butterfly subfamilies consumed plants from many families but a low diversity of species within each family, while others were specialists from certain families and generalists within this family (Suppl.S2.6). Satyrinae (Nymphalidae) and Hesperiinae (Hesperiidae) specifically interacted with the Poaceae plant family (as well as some Cyperaceae and Juncaceae) and were grouped in an independent module with high hostplant species richness (Suppl.S2.6; Suppl.S2.7). Pieridae also showed a strong specificity in terms of plant families and a privileged association with Brassicaceae, with species of Pierinae grouped in an independent module. Nymphalidae and Lycaenidae consumed the greatest diversity of plant families, although the majority of their hostplants belonged to the Fabaceae (Suppl.S2.6). Most Lycaeninae species were grouped in an independent module in which they interacted only with plants of the genus *Rumex* (Polygonaceae). Pyrginae (Hesperiidae) were strongly related to Rosaceae and Papilionidae mainly consumed Apiaceae, Rutaceae, Aristolochiaceae and Papaveraceae (Suppl.S2.7).

Among the 53 plant families present in the butterfly-plant network, three families centralized about half of the links with butterflies: Poaceae (23% of total links), Fabaceae (17%) and Rosaceae (10%). The modularity analysis showed that the majority of plant species (518 species) had a peripheral role in the structure of the network (Fig.2.2). Only nine species were considered as hubs, characterized by a central role in the cohesion of their module: *Bromus erectus*, *Colutea arborescens*, *Dorycnium pentaphyllum*, *Festuca ovina*, *Festuca rubra*, *Lotus corniculatus*, *Medicago sativa*, *Onobrychis supina*, *Potentilla neumanniana*. They corresponded to five Fabaceae, three Poaceae and one Rosaceae (Fig.2.2). They were all consumed by at least eight species of butterflies (with an average degree of 12 ± 6).
II.3.2. Correlation between distance matrices

The application of Mantel's tests revealed a significant and positive correlation between the
distance matrix constructed from butterflies host range and the geographic, ecological and
taxonomic distance matrices (Mantel tests, P < 0.001). However, despite their significance,
correlations between butterfly host ranges and the similarity of species distribution and
habitats remained low (Mantel tests, rs=0.06 and rs=0.07 respectively). Taxonomic
relatedness of butterfly species showed the higher correlation with their host range (Mantel
tests, rs=0.34).

<figure><img src="image_36.png" /></figure>

**Figure 2.4** : Distribution of butterfly species according to their degree in the bipartite network (k<sub>b</sub>) and to the standard effect size (SES) of the mean degree of their related hostplants (c<sub>b</sub>) with respect to its value in null model 2. Both dotted lines indicate the significance of species deviation to the null value of c<sub>b</sub>. Species located between these lines show no difference between the observed and null situation. Species located above the upper line interact with significantly more plants hosting a large number of butterflies than expected in random situation. Species located below the lower line interact with significantly more plants hosting a few butterflies than expected in random situation. Green dots correspond to protected butterfly species (n = 26) and grey dots represent unprotected species (n = 215). The name of protected species with a significant SES value is indicated.
II.3.3. Hostplant mean degree and projected network

A significant variation of the mean degree of related hostplant (c<sub>b</sub>) was observed for butterflies, with some monophagous species grouped in independent modules (c<sub>b</sub> = 1) and some species related to highly consumed plants (c<sub>b</sub> = 27 for the maximum value, which mainly concerned Satyrinae). Most butterfly species interact with plants consumed by less than 10 species (on average c<sub>b</sub> = 4.86 ± 3.96). There was no significant correlation between the bipartite degree k<sub>b</sub> of butterfly species and the mean number of butterflies feeding on their hostplants c<sub>b</sub> (Spearman's correlation, rs=0.04, P > 0.05; Fig.2.3). The variances of both parameters were significantly higher in the analyzed network than in the random situation simulated through null model 1 (random k<sub>b</sub> ranged between 4 and 5, random c<sub>b</sub> ranged between 3 and 3.15). Simulations based on the random model with fixed degree (null model 2) did not reveal a correlation between the degree k<sub>b</sub> and the average degree of the hostplants c<sub>b</sub> (Fig.2.3). The distribution of the butterfly degrees did not seem to impose a strong constraint on the structure of the network and the real c<sub>b</sub> values showed a range of variation greater than expected according to this random model. However, the majority of butterfly species (75%) did not deviate significantly from the null value of c<sub>b</sub> simulated by the null model 2 (Fig.2.4). Only 25 butterfly species had more hostplants heavily consumed than expected by chance, and 8 species more specific hostplants than expected by chance, given their bipartite degree. Eighteen species of butterflies were not linked to any other species across their projected network (Fig.2.5). The projected degree (I<sub>b</sub>) of other species ranged from one to 45 (the highest values were for Satyrinae and Hesperiinae), with an average of 10.61 ± 10.97. The average dependence of the indirect partners of these butterflies in the projected network (D<sub>b</sub>) ranged from one (high dependence the same hostplants) to 25 (low dependence to the shared hostplants), with an average of 7.66 ± 3.98 (Fig.2.5). There was no significant correlation between the projected degree of butterfly species and the average dependence of their indirect partners D<sub>b</sub> (Spearman's correlation, rs=0.04, P > 0.05).
<figure><img src="image_37.png" /></figure>

**Figure 2.5** : Distribution of butterfly species according to their degree in the projected network ($l_b$) and to the mean dependency of their indirect partners ($D_b$). Green dots correspond to protected butterfly species (n = 26) and grey dots represent unprotected species (n = 215).

<figure><img src="image_38.png" /></figure>

**Figure 2.6** : Mean values (± SE) of (a) degree in the bipartite network ($k_b$), (b) mean degree of related hostplant ($c_b$), (c) projected degree ($l_b$) and (d) specialization of related butterflies ($D_b$), compared between protected (n = 26) and unprotected (n = 215) butterflies. Asterisks indicates significant differences between both groups (***: P < 0.001, *: P < 0.05, NS: P > 0.05, Wilcoxon's signed-rank tests).
II.3.4. Conservation issues and protected species

The comparison of the degree of species in the bipartite network according to their IUCN
category revealed a significant link between species specialization and their risk of extinction.
Species classified as Least Concern showed a significantly higher mean degree (4.98 ± 4.45)
to Near Threatened species (2.23 ± 1.68) and Threatened species (2.47 ± 2.32) (Kruskal-
Wallis test, P < 0.01).

Twenty-six protected butterfly species were included in the butterfly-plant interaction network. They interacted with 77 plant species. Almost all these plants (99%) had a peripheral role in the network (Fig.2.2). Protected butterflies interacted indirectly with a total of 54 butterfly species across the projected network (which we called “indirectly protected”). Protected species had a relatively low bipartite degree ($k_p$) (3.58 ± 3.62) but did not show a significant difference with the rest of the network (4.65 ± 4.31) (Wilcoxon test, P > 0.05; Fig.2.6). The mean degree of their hostplants ($c_p$) was significantly lower (3.15 ± 2.78) than the other butterflies in the network (5.08 ± 4.04) (Wilcoxon test, P <0.01; Fig.2.6) and three species (*Euphydryas aurinia*, *Maculinea alcon* and *Parnassius apollo*) had a value of $c_p$ significantly lower than expected by chance (Fig.2.4). The correlation between the bipartite degree of protected butterflies and the average degree of their host plants was significant and negative (Spearman’s correlation, rs=-0.39, P < 0.05). This revealed a tendency for less specialized protected species to share their resources with a limited number of butterflies (Fig.2.3). The projected degree of protected butterflies was also significantly lower than that of unprotected ones (Wilcoxon test, P <0.001; Fig.2.6), with an average of 3.96 (± 4.85) and 11.41 (± 11.24) respectively. The average dependence of the indirect partners of these protected butterflies in the projected network ($D_b$) (7.74 ± 6.40) was very close to the average of the complete network, and did not show a significant difference with unprotected species (7.64 ± 3.66) (Wilcoxon test, P > 0.05; Fig.2.6). The few species interacting indirectly with butterflies’ dependent of the same hostplants (i.e. with a low $D_b$ value) (Fig.2.5) were in fact linked together within independent modules in the projected network (e.g. *Lycaena helle* and *Boloria eunomia*; *Maculinea teleius* and *Maculinea nausithous*).
<table><caption>**Table 2.1:** Vulnerability of the studied butterfly species according to their coverage by the current national protection lists. 'Directly protected' species are included on these lists, 'Indirectly protected' species are indirectly linked to protected butterflies through a shared host plant, 'Unprotected' species are neither listed nor linked to protected species. Butterfly vulnerability corresponds to their IUCN status on the French red list ("Threatened" corresponds to categories VU, EN and CR).</caption><thead><tr><td>IUCN categories</td><td>Directly protected</td><td>Indirectly protected</td><td>Unprotected</td></tr></thead><tbody><tr><td>Least Concern</td><td>13</td><td>52</td><td>143</td></tr><tr><td>Near Threatened</td><td>6</td><td>0</td><td>11</td></tr><tr><td>Threatened</td><td>7</td><td>1</td><td>8</td></tr></tbody></table>

The coverage of butterfly species by the current protection lists varied significantly depending on their IUCN categories in France (Fisher test, P < 0.01; Table 2.1). Half of the directly protected species were Near Threatened and Threatened. The high majority of other species in these two categories were unprotected. The potentially “indirectly protected” species were for 98% Least Concern species.

## II.4. Discussion

In this study, we constructed the interaction network connecting French butterflies to their host plants, from a large original and verified dataset. This first analysis of these relations at the scale of France allowed describing the specific structure of this network, likely to have an influence on the recent dynamics of the species, and to identify the main explanatory factors of this structure. Taxonomic relationships between butterflies appeared to have a significant influence on their tendency to share a resource, and the most closely related species generally have a close relationship with related plants. Due to this conservatism, which is expressed by a modular structure, the majority of plants do not constitute potentially key resources for large butterfly assemblages. This structure was put in perspective with the vulnerability and protection status of butterfly species in order to contribute to the development of a more systemic view of butterfly conservation. From this work, we showed the higher vulnerability of the most specialized butterflies as compared to generalists and some potential limits of the current protection lists.
II.4.1. Butterfly diet constrained by taxonomic distances

The analysis of the association matrix revealed a structuration of the butterflies and their
hostplants in a highly modular network (Fig.2.1), significantly different from a random
situation (null model 1). Patterns of indirect interactions between butterflies through a sharing
of trophic resources are therefore particularly constrained, leading to the existence of several
entirely independent modules. This highly modular structure seems to be a common trend of
antagonistic networks, especially in the case of plant-herbivore insect interactions (Thébaut &
Fontaine, 2010). The existence of modules has been related to the high proportion of
specialized insect and interaction range boundaries that determine modules are mainly
imposed by the phylogeny of hostplants (Weiblen et al., 2006; Cagnolo et al., 2011). This
type of conservatism also seems to be at the base of the structure of the analyzed network.
Identified modules were mainly composed by related species (genus, subfamilies, families),
both on the side of plants and that of butterflies. The important correlation between the
overlap of butterfly host ranges and their taxonomic proximity, as evidenced by the
comparison of distance matrices, confirmed these observations. This type of congruence
between the phylogeny of butterflies and plants has already been described through a review
of several datasets at the global scale (Ferrer-Paris et al., 2013).

This structure appears strongly conserved among different regions for which more
complete butterfly-hostplant matrices are available, with a great similarity of the patterns of
associations (Munguira *et al.*, 2009). The small proportion of plants consumed by butterflies
at the scale of a territory (between 20 and 30% of plant families; Ferrer-Paris *et al.*, 2013) and
the significant butterfly richness associated with Fabaceae, Poaceae and Rosaceae seem to be
constants whatever the explore areas worldwide (Ackery, 1991; Tiple *et al.*, 2011; Muto-
Fujita *et al.*, 2017). The main modules, *i.e.* Satyrinae / Hespriinae with Poaceae, Pierinae with
Brassicaceae, Lycaenidae with Fabaceae are also conserved (Munguira *et al.*, 2009; Ferrer-
Paris *et al.*, 2013; Muto-Fujita *et al.*, 2017). Such modules contribute to spatially segregate
butterflies' families in different habitats; for example, with Satyrinae / Hesperiinae are mainly
present in open areas dominated by grasses (Poaceae). These patterns reveal an evolutionary
signal probably constrained by coevolution processes, related to an adaptation of butterflies to
the chemical mediation (defenses, pollinator attraction) of plants (Muto Fujita *et al.*, 2017).
The specificity of the chemical compounds used by different plant taxa could be a more
conservative trait than their phylogeny (Wahlberg, 2001; Endara *et al.*, 2017). The hypothesis
of radiation events of some butterfly lineages following a host-shift, allowing the exploitation
of a new group of host plants, has been advanced in order to describe the diversification of the
butterflies, especially at the scale temperate regions (Fordyce, 2010; Condamine et al., 2012).

II.4.2. Butterflies vulnerability and network metrics

Apart from some common Poaceae (e.g. *Bromus erectus*, *Festuca ovina*, *Festuca rubra*), which may host a wide variety of Satyrinae, the majority of host plants did not show a central position within the network or even within a module (Fig.2.2). Shifts in plant distribution patterns would not lead to a significant deconstruction of the butterfly communities. Indeed, the modular structure of antagonistic networks is generally considered as a characteristic favorable to a higher robustness (Thébaut & Fontaine, 2010). In most cases, it leads to a reduction of the propagation of negative cascades effects throughout the network in the case of local extinctions. The simulation of extinction cascades on a butterfly-plant dataset, taking into account the level of plant abundance, showed a significant delay between the decrease in plant richness and that of the butterfly communities associated with them (Pearse & Altermatt, 2013). The extinction of a plant may only affect a small proportion of the network, with a greater or lesser impact depending on the degree of specialization of the indirect butterfly partners.

More, the study of the butterfly-hostplant network did not reveal any significant
correlation between the degree of butterfly species and the average degree of their hostplants,
and this last value showed few significant deviations as compared to a random situation (null
model 2; Fig.2.3). The most generalist butterflies feed on many plants characterized by a low
$k_p$ degree, with significant deviation to null situation for some of them (e.g. *Euphydryas
aurinia*; Fig.2.4). Butterflies with the highest degree $k_b$ values in the bipartite network (e.g.
*Callophrys rubi*, *Papilio machaon*, *Pieris brassicae*, *Pieris rapea*) accumulate a large
proportion of hostplants that are exclusive to them. In contrast, the most specialized butterflies
did not show a strong tendency to be isolated in individual modules and some of them feed on
more attractive plants (i.e. high value of $c_b$) than expected by chance (e.g. *Hipparchia fagi*;
Fig.2.4). Therefore, these specialist species do not seem to be linked to plants free from any
potential competitor within the butterfly communities. These patterns are in agreement with
recent studies concluding in the little influence of interspecific competition in the
specialization process and the structuring of butterfly-plant networks (Nakadai et al., 2017).
However, the disappearance of certain plants in the network may have a high impact on the most specialized butterflies which depend on it. Several species of the studied network are monophageous and some are isolated within independent modules. Analyses thus showed a significant link between the level of specificity (i.e. low degree $k_b$) and the risk of extinction of butterflies. Constrained by the very narrow range of resources required for their development, specialized species will have low capacity to respond to environmental changes and will be particularly vulnerable to landscape structure alteration (Komonen et al., 2004; Stefanescu et al., 2011). In addition to the direct extinction that may result from the degradation of hostplant populations (Pearse & Altermatt, 2013), the decrease in their density or the mismatch between their phenology and that of butterflies (for example under climate changes) could have important consequences for the survival of the most specialized butterfly species, in particular within fragmented landscapes (Schweiger et al., 2008; Brunbjerg et al., 2017; Guardiola et al., 2017). In addition, some characteristics of the hostplants may condition the response of the butterfly species, in particular by constraining their phenology, the speed of their development or their voltinism (Dennis et al., 2004; Cizek et al., 2006; Altermatt, 2010). Monophageous butterflies, which present a priori less possibility of range or host shift, could therefore be strongly constrained by these characteristics.

### II.4.3. Patterns of potential indirect protection

The construction of the projected network provided an insight of indirect interactions that could link several species of butterflies sharing the same host plants. It has been used as a tool to investigate the potential role of umbrella species which could be associated with the species currently protected in France (26 protected butterflies in the network). Since these lists require the protection of the habitat and resources needed to complete the life cycle of the target species, this work aimed at estimating the indirect benefits of protecting hostplants for the conservation of key issues associated with butterflies. Most of the protected species showed a low $k_b$ degree, indicating a tendency for specialization. In contrast, hostplants on which they feed were almost exclusively peripheral nodes of the network (Fig.2.2). This induces a weak sharing of these plants with other interacting butterflies, and was reflected by a low mean degree of partners ($c_b$) for most of them (Fig.2.3). The average value of this parameter was significantly lower than in the rest of the network (Fig.2.6) and, although few protected species showed a significant deviation from the null model 2, about 75% of them tended to
feed on less attractive plants than expected by chance (Fig.2.4). As a result, the protected
species showed very little potential indirect interactions with other butterflies in the network
(i.e. low value of *I*<sub>b</sub>; Fig.2.6). The protection of hostplants as resources for the maintenance of
protected butterfly species is unlikely to favor a great diversity of butterflies.

In addition, the few butterflies indirectly linked to the protected species did not show a
strong dependence on shared hostplants (i.e. low value of D<sub>b</sub>; Fig.2.6). In general, they had a
host range close to the population average, thus including several species of hostplants on
which they can theoretically be distributed. The study of the status of these “indirectly
protected” butterflies revealed that the majority of them were not concerned by a risk of
extinction (Table 2.1). Most endangered species were not covered by the current statuses,
whether directly or through indirect potential protection. This pattern could allow to deduce
(1) a low benefit from the current protection lists for butterflies representing a real
conservation issue and which would not be directly covered, and (2) a weak influence of the
sharing of the same resource on the extinction risk of two distinct butterfly species. In a
perspective limited to their biotic niche, the vulnerability of butterfly species could in this
case be mainly dependant on the specificity of their caterpillars for one or a few host plants
rather than by the specific characteristics of these plants.

II.4.4. Limits of the analyzed datas

Given the nature of the data used for the construction of the studied association matrix, it is
important to make some considerations about their quality. Interaction networks built from
literature reviews can indeed be seen as a summary of "potential" interactions on a given
scale, derived from punctual observations in time and space and allowing to deduce the
possibility for two species to interact when they are in co-occurrence (Delmas et al., 2017).
Since the interactions synthesized in our work cover a large geographical area, it is possible
that one species of butterfly does not meet all the referenced hostplants across its entire range
Some hostplants may have a more restricted distribution and may be only consumed by a few
populations of the butterfly species following local adaptation (Srinivasan et al., 2007;
Meister et al., 2015). In addition, our dataset does not take into account the interaction
strengths between butterflies and their different hostplants, limiting the analysis to the
topological structure of the network. Given these limits, indirect sharing of resources among
butterflies must therefore be considered as a potentiality related to an overlap of theoretical
niches, limited by the hypothesis of a temporal and spatial co-occurrence of the involved species (Pocock *et al.*, 2012).

The nature of the analyzed data may also lead to some biases in favor of an underestimation of hostplant species, related to the non-detection of certain interactions because of heterogeneous sampling effort (Gibson *et al.*, 2011; Costa *et al.*, 2016). On the other hand, given the floristic diversity in the studied area and the difficulty of identifying some groups, it is possible that a part of the interactions results from misidentification of a plant by an entomologist (i.e. false presence). It seems particularly important to take this potential bias into consideration when analyzing the Stayrinae-Poaceae module, in which some data may be related to inaccurate identification of plants. However, predictive models based on this type of network have shown a high robustness to these both sources of bias (Pearse & Altermatt, 2015). Given the diversity of sources and the period covered, our dataset can therefore be considered as a comprehensive basis to describe the general patterns of association despite potential biases.

### II.4.5. Contribution to integrative butterfly conservation

Network models help to map the relationship of butterflies with host plants, and then to document the component of biodiversity devoted to the interactions among species. As such, network models may be a useful tool for assessing one component of herbivore extinction risk (Pearse & Altermatt, 2013). This approach centered on the biotic niche of the species allows identifying the component of species vulnerability linked solely to its host range and to the sharing of these hostplants. The projected network also constitutes an efficient tool to explore potential indirect conservation, in a perspective of identifying "umbrella" species in the context of resources allocated to biodiversity conservation (New, 1997). Species-specific characteristics, related to their location in this bipartite network, need to be integrated into a more holistic conservation strategy, to avoid too much simplification of the processes conditioning their vulnerability.

Hostplants are only one component of species' habitat, and their presence alone is not enough to ensure the presence and abundance of butterfly populations associated with them. Other essential resources compose species micro-habitats and filter their occupancy (Dennis, 2010). This is expressed by the often much wider distribution of the hostplant as compared to
that of its consumer (Quinn *et al.*, 1998). These other factors, for example related to landscape
structure or connectivity, often have a more significant, or at least more visible, influence on
species decline than the regression of host plant populations (Pearse & Altermatt, 2013). In
particular, the influence of the specificity of the adult diet on the status of species is
increasingly explored. Although the specificity of the caterpillar diet constitutes the most
restrictive variable (Altermatt & Pearse, 2011), specialist species in the larval stage are often
also specialists in the adult state (Hardy *et al.*, 2007); this double food dependence makes
their conservation challenging. Since the nectariferous plants required for adults can be
different from caterpillars' hostplant and influence their response to habitat changes (Tudor *et
al.*, 2004), the processes that condition the risk of local extinction of populations of a species
are probably more complex and requires the crossing of several layers of specificity
(Astegiano *et al.*, 2017).

Nevertheless, interaction networks are essential tools for better prediction of potential
biodiversity losses and potential homogenization in the face of large-scale reshaping of
natural habitats. They allow for example the demonstration of cascades of potential
extinctions or the destructuring of the links between species which underlie the diversity of
the ecosystems, which can occur well before any decline in species richness (Dainese et al.,
2017). The synthesis of interaction data and the first analysis of the butterfly-hostplant
bipartite network conducted in this study contribute to the improvement of the knowledge of
the resources that condition the presence of butterflies and how they affect their conservation
status. This knowledge is an important basis for the formulation of effective conservation
management programs (Dennis et al., 2008).

Acknowledgments

The present work was a collaboration between Biotope (www.biotope.fr) and the Centre
d'Ecologie Fonctionnelle et Evolutive (www.cefe.cnrs.fr) in the form of a CIFRE 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. We are grateful to Nina Joffard and François Massol for
helpful discussions. Thanks to Adrien Taudière for his help in the development of statistical
analysis.
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**Supplementary materials 2**

**Supplementary material S2.1:** List of 241 butterfly species included in the butterfly-hostplant interaction network.

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

**Supplementary material S2.2:** List of 527 plant species included in the butterfly-hostplant interaction network.

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

**Supplementary material S2.3:** Illustration of the estimation of the index D.

**Supplementary material S2.4:** Histogram of the distribution of (a) butterflies and (b) plants degrees within the bipartite butterfly-hostplant interaction network.

**Supplementary material S2.5:** Graphical representation of modules in the bipartite butterfly-hostplants network.

**Supplementary material S2.6:** Specialization patterns of butterfly subfamilies composing the analyzed butterfly-hostplant interaction network.

**Supplementary material S2.7:** Graphical representation of the bipartite butterfly families-hostplants families network.
**Supplementary material S2.1:** 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.

List of 241 butterfly species included in the butterfly-hostplant interaction network. The higher taxonomic levels (family and subfamily) are shown on the basis of the most recent revision. The main breeding habitat and conservation status (protection, category on the French red list (LR), range trends, significance of decline) of each species are summarized. The degree (k), the projected degree (l) and the average degree of the partners (c) were calculated for each species. Each species was assigned to one of the modules (Mod) structuring the network.

**Supplementary material S2.2:** 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.

List of 527 plant species included in the butterfly-hostplant interaction network. The higher taxonomic levels (order, family, and subfamily) are shown on the basis of the most recent revision. The degree (k), the projected degree (l) and the average degree of the partners (c) were calculated for each species. Each species was assigned to one of the modules structuring the network.
**Supplementary material S2.3: Illustration of the estimation of the index D.**

The index *D*, developed in the present study, allowing to index (*D*) allowing to quantify the level of dependence of the partners of a butterfly species in the projected network to the hostplants they shared with this species. The value of *D* of a butterfly was calculated by estimating the average degree of butterfly species related to each of its hostplants. The value of *D*<sub>b</sub> for a butterfly *b* is given by the sum of the average degree of each of its host plants in the bipartite network (*c*), divided by its own degree in the bipartite network (*k*<sub>b</sub>).

In the example below (a), butterfly A shares its single hostplant with a butterfly B. This butterfly B has no other hostplant. The value of the index *D* for butterfly A will therefore be low, indicating that indirect partners (here B) is a specialist of the same resource. In example (b), the butterfly C shares its single hostplant with the butterfly E, which also consume two other hostplants. The value of the index *D* for C will therefore be higher than that of A, indicating that its indirect partners (here E) have a lower degree of specialization on its hostplants (E is not monophageous and can consume two other plants).

<figure><img src="image_39.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_40.png" /></figure>

Plant designs taken from Levine *et al.*, 2017. Butterfly designs created by Alena Artemova for the Noun Project (https://thenounproject.com).
**Supplementary material S2.4:** Histogram of the distribution of (a) butterflies and (b) plants degrees within the bipartite butterfly-hostplant interaction network.

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>
**Supplementary material S2.5:** Graphical representation of modules in the bipartite butterfly-hostplants network based on the spatialization algorithm *Force Atlas 2* (Jacomy *et al.*, 2014) implemented in the software GEPHI (Bastian *et al.*, 2009). Circles correspond to species of butterflies and hostplants and their size is proportional to species' degrees. Different colors correspond to the different modules identified through the software NETCARTO (Guimera & Amaral, 2005).

<figure><img src="image_43.png" /></figure>
**Supplementary material S2.6:** Specialization patterns of butterfly subfamilies composing the analyzed butterfly-hostplant interaction network. The number of butterfly species in each subfamily composing the network (Recorded Richness) was compared with their species richness in France (French richness). The number of orders, families and hostplant species which interacted with each butterfly subfamily was summarized.

<table><thead><tr><th>Butterfly subfamilies</th><th>French richness</th><th>Recorded richness</th><th>Number of associated hostplant orders</th><th>Number of associated hostplant families</th><th>Number of associated hostplant species</th></tr></thead><tbody><tr><td>Apaturinae</td><td>2</td><td>2</td><td>2</td><td>2</td><td>9</td></tr><tr><td>Charaxinae</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td>Coliadinae</td><td>7</td><td>7</td><td>3</td><td>3</td><td>24</td></tr><tr><td>Danainae</td><td>2</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Dismorphiinae</td><td>4</td><td>3</td><td>1</td><td>1</td><td>8</td></tr><tr><td>Heliconiinae</td><td>19</td><td>19</td><td>5</td><td>6</td><td>55</td></tr><tr><td>Hesperiinae</td><td>6</td><td>6</td><td>1</td><td>2</td><td>42</td></tr><tr><td>Heteropterinae</td><td>2</td><td>2</td><td>1</td><td>1</td><td>6</td></tr><tr><td>Libytheinae</td><td>1</td><td>1</td><td>1</td><td>2</td><td>2</td></tr><tr><td>Limenitinae</td><td>3</td><td>3</td><td>2</td><td>2</td><td>8</td></tr><tr><td>Lycaeninae</td><td>7</td><td>7</td><td>1</td><td>1</td><td>31</td></tr><tr><td>Melitaeinae</td><td>15</td><td>14</td><td>5</td><td>11</td><td>71</td></tr><tr><td>Nymphalinae</td><td>12</td><td>10</td><td>7</td><td>13</td><td>56</td></tr><tr><td>Papilioninae</td><td>5</td><td>5</td><td>3</td><td>3</td><td>48</td></tr><tr><td>Parnassinae</td><td>5</td><td>5</td><td>3</td><td>4</td><td>23</td></tr><tr><td>Pierinae</td><td>15</td><td>15</td><td>4</td><td>6</td><td>121</td></tr><tr><td>Polyommatinae</td><td>46</td><td>44</td><td>17</td><td>21</td><td>189</td></tr><tr><td>Pyrginae</td><td>22</td><td>22</td><td>4</td><td>5</td><td>87</td></tr><tr><td>Riodininae</td><td>1</td><td>1</td><td>1</td><td>1</td><td>3</td></tr><tr><td>Satyrinae</td><td>73</td><td>61</td><td>1</td><td>3</td><td>230</td></tr><tr><td>Theclinae</td><td>12</td><td>12</td><td>8</td><td>10</td><td>78</td></tr></tbody></table>
**Supplementary material S2.7:** Graphical representation of the bipartite butterfly families-hostplants families network based on the spatialization algorithm *Force Atlas 2* (Jacomy *et al.*, 2014) implemented in the software GEPHI (Bastian *et al.*, 2009). Circles correspond to families of butterflies and hostplants and their size is proportional to total families' degrees. The width of the links is proportional to the number of species pairs of the two related families interacting in the butterfly-hostplant network. Green circles represent hostplant families and orange circles butterfly families.

<figure><img src="image_44.png" /></figure>
# Chapitre 3

Multi-species occupancy model reveals the poor completeness of entomological surveys in environmental impact assessment
Ce **troisième chapitre** se place directement dans une perspective de recherche appliquée. Il vise à répondre à une demande croissante de données permettant de quantifier la représentativité des inventaires réalisés dans le cadre des études d'impacts sur l'environnement. Etant donné la limitation financière de la pression d'échantillonnage souvent mise en place dans le cadre de ces inventaires et les biais liés l'imperfection de la détectabilité propre aux données biologiques, cette quantification permettrait d'évaluer la qualité de la mesure des impacts d'un projet et la pertinence de la séquence ERC programmée par l'aménageur.

Afin de répondre à cette problématique des inventaires entomologiques ont été réalisés sur plusieurs sites méditerranéens, sur la base de plans d'échantillonnage incluant toutes les contraintes auxquelles sont soumis les bureaux d'études en environnement lors de la construction de leurs offres (compétitivité, enjeux ciblés, potentialités d'accueil du site, ...). Les prospections de terrain ont ainsi été ciblées non seulement sur la recherche des Rhopalocères et des Odonates, mais ont également inclus un inventaire des espèces d'Orthoptères et de cigales afin de correspondre aux pratiques courantes des bureaux d'études dans le sud de la France.

La richesse spécifique détectée sur chaque site et la distribution observée pour chacune des espèces ont été comparées à des valeurs potentielles, estimées à travers des modèles statistiques permettant de corriger les biais liés à l'hétérogénéité de la détection. Ces modèles sont particulièrement pertinents pour extraire le maximum d'informations de matrices d'occurrence multi-espèces incluant plusieurs espèces rares et offrent la possibilité d'intégrer des covariables environnementales et celles propres aux conditions de réalisation de ces relevés susceptibles d'influencer la qualité des inventaires.

À terme, les résultats issus de cette étude sont susceptibles de permettre le développement d'outils méthodologiques et commerciaux permettant d'améliorer la qualité des plans d'échantillonnage en condition d'étude d'impact, voire même d'être par la suite présentés aux services instructeurs en vue d'harmoniser les pratiques des bureaux d'études à l'échelle nationale.
# Multi-species occupancy model reveals the poor completeness of entomological surveys in environmental impact assessment

DELPON Gaël<fnref n="1" />,<fnref n="2" />, BESNARD Aurélien<fnref n="1" />, SANNIER David<fnref n="2" />, RICHARD Franck<fnref n="1" /> and SCHATZ Bertrand<fnref n="1" />

## Abstract

1. Imperfect species detection is likely to induce serious biases in conventional estimators of species distributions and species richness. Such biases are a major issue in the case of environmental impact assessment (EIA) as failure to detect a threatened species actually present on a site may lead to direct destruction of its local populations.

2. We aimed to obtain an initial evaluation of the completeness of the entomological surveys carried out under EIA, both in terms of total species richness and of threatened species detection. We conducted insect surveys at 20 sites located in the French Mediterranean area. They were targeted on four groups of insects (butterflies, dragonflies, grasshoppers and cicadas) and the sampling period was here doubled for each site in order to evaluate the link between the completeness of these results and the sampling effort. Collected data were analyzed under a multi-species occupancy models (MSOM), allowing estimating the potential richness of the community and the occurrence probability of each observed species, by taking into account biases related to the heterogeneity of their detection.

3. The results showed a significant discrepancy between the estimated species richness and the detected richness. Almost 50% of the potential richness remained undetected under classical condition of EIA and species occurring on a site had on average less than 30% of chance to be detected. Sampling coverage was dependent on the number of visits conducted on the site and was variable according to the conservation status of species. Survey conditions had a significant influence on species-specific detection probability.

4. Our results emphasize the need for field protocols that accommodate estimation of detection error when conduction entomological surveys in EIA. The significance of biases related to imperfect detection has demonstrated the importance of this type of scoping study for a better measurement of the risks linked to the project development. Such information could help the regulator to advocate minimum survey requirements guaranteeing the quality of EIA studies. The number of visit, their spread over the year and the time at which they will be carried out will then constitute important parameters for the establishment of cost-efficient sampling design which provide reliable knowledge.

**Key words:** Environmental impact assessment, imperfect detection, survey effort, sampling coverage, species richness, Mediterranean, hierarchical model.

<footnote n="1">¹CEFE UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - EPHE, 1919 route de Mende, 34293 Montpellier cedex 5, France</footnote>

<footnote n="2">²Biotope, 22 Boulevard Maréchal Foch, 34140 Mèze, France</footnote>
III.1. Introduction

Species occurrence is a primary biodiversity datum of central importance in ecology and wildlife management (MacKenzie, 2005a; Soberon et al., 2007). Depending on the scale at which they are collected, occurrence data can be aggregated into several metrics, comparable through time and space to access basic information about the response of species to environmental changes (Yoccoz et al., 2001; Powney & Isaac, 2015). The count of the number of occurring species on the same site, namely species richness, is for instance one of the most widely used metric of biodiversity (Gotelli & Colwell, 2011). The presence of a same species within several sampling units informs about its distribution (Royle & Nichols, 2003; Gaston & He, 2011; Bird et al., 2014). These metrics are widely used for a range of taxa to monitor some target species, to highlight their response to perturbations or to management actions (Tyre et al., 2003; MacKenzie, 2005b). It is also on the basis of these measures that the main conservation issues within a site of interest or a region could be identified, and that operational measures could be constructed to reduce and mitigate the impact of the alteration of their habitat (Gotelli & Colwell 2001; Johnson et al., 2004; DeWan & Zipkin, 2010).

In the vast majority of cases, a complete survey on a site is impossible, especially
because of the limited human investment and financial cost necessary to reach such
completeness (Kéry & Plattner, 2007; Kellner & Swihart, 2014). Ocrruence are typically not
observed perfectly and these data will be subject to biases related to "false absence" events,
i.e. a species may be undetected in a survey even when it is actually present within a site
(MacKenzie et al., 2006; Miller et al., 2011). Failure to take into account the imperfection and
variability of species detection will lead to confusion between true absences (reflecting actual
species occurrence) and false absences leading to artificially zero-inflated data sets (Tyre et
al., 2003; Martin et al., 2005). Because some species could be particularly cryptic or rare the
probability of detection could show a wide heterogeneity in a community (Bouligner et al.,
1998; Iknayan et al., 2014), which could lead to bias in the estimation of species richness
(Chao & Jost, 2012). In addition, the probability of detecting a species is rarely constant in
time and space (Yoccoz et al., 2001; Moore et al., 2014). It can widely vary with site-specific
environmental conditions (Iknayan et al., 2014), with observer experience (Nichols et al.,
2000; Fitzpatrick et al., 2009) or depending on the conditions under which field session are
conducted (Chen et al., 2009; Petitot et al., 2014). Biological and ecological characteristics of
different species (life history traits, life cycle, period of activity...) (Rovero *et al.*, 2014; Potoka *et al.*, 2016)

These biases could significantly affect the quality of the results of studies based on occurrence data, such as species distribution or habitat modeling (Gu & Swhihart, 2004; Lahoz-Monfort *et al.*, 2014; Guillera-Arroita, 2017), identification of the processes that govern meta-populations dynamics (e.g. colonization, extinction or dispersal; Moilanen, 2002), monitoring of rare or vulnerable species (Chadès *et al.*, 2008; Kéry & Schmidt, 2008; DeWan & Zipkin, 2010), planning management measures for invasive (Hauser & McCarthy, 2009; Regan *et al.*, 2011) or endangered species (Wintle *et al.*, 2004; MacKenzie 2005a). Hence, the importance of understanding and accounting for imperfect detection extends to major decision-making bodies (MacKenzie *et al.*, 2006; Cruickshank *et al.*, 2016).

This is particularly crucial in the case for environmental impact assessment (EIA) procedures, which use this type of occurrence and richness data to inform decision makers about the environmental consequences of project developments and provide elements allowing to avoid, reduce and mitigate for adverse impacts (Jay *et al.*, 2007). The main purpose of the EIA was to determine whether development can be allowed to proceed at a site despite the risk of adversely affecting one or several threatened species that might occupy the site (Ritter *et al.*, 2017). Failure to detect a species actually present (or underestimation of its presence) on a site may lead to a decision favorable to the project and thus to direct destruction of its local populations, which may have a significant negative impact on many endangered species (Garrard *et al.*, 2015). The lack of quality or completeness of data from EIA may therefore have a significant cost in terms of conservation (Guillera-Arroita *et al.*, 2015). It reveals the need for methodological framework developments by the regulator in order to set a minimum survey effort or sampling conditions (Garrard *et al.*, 2008; Wintle *et al.*, 2012). The development of cost-efficient sampling plans for EIAs, currently mainly focused on optimizing the competitiveness of structures in charge of the surveys, requires the respect of a trade-off between the costs of the surveys and the quality of the results (Moore *et al.*, 2014). However, the quality of the results of the current practice of EIA remains poorly evaluated in terms of sampling coverage and level of detection bias.

Many methods have been developed in recent decades to deal with imperfect detection when estimating species richness or species distribution (Mazerolle *et al.*, 2007; Bailey *et al.*,
2014). For instance, the methods based on accumulation curves of incidence data as a function of sampling effort were developed to obtain an unbiased estimate of richness (Soberon & Llorente, 1993; Gotelli & Colwell, 2011) or to compare several sites that have been surveyed under different efforts (Colwell et al., 2012; Hsieh et al., 2016). However, none of these methods consider the direct effect of detection biases and of the heterogeneity of detection probabilities between species on the estimation of species richness (Bouligner et al., 1998; Kéry & Royle, 2008; Iknayan et al., 2014).

Site-occupancy models, initially developed to address single-species issues, have known increasing success since the beginning of the century (MacKenzie et al., 2006; Bailey et al., 2014). From a history of detection of a species collected through repeated visits to several sites, these models allow estimating species occurrence while accounting for imperfect species detection and assessing the variation of these both quantities as a function of relevant environmental variables (MacKenzie et al., 2002; MacKenzie & Royle 2005). Various extensions of these single species models have been proposed to estimate community-level metrics and dynamics. Multi-species occupancy models (MSOM thereafter) allow estimating unbiased occurrence and detectability of each surveyed species and use these estimates to draw conclusions at the community level (i.e. species richness) in the same framework (Dorazio & Royle, 2005). This relatively recent modelling framework have been used to test the effect of different management regimes (Zipkin et al., 2010; Carrillo-Rubio et al., 2014), evaluate biodiversity metrics and meta-community structure (Dorazio et al., 2011; Mihaljevic et al., 2015) or improve monitoring programs (DeWan & Zipkin, 2010). Recent extensions allow incorporating effects of several covariates (Kéry & Royle, 2008), multiple states (Kéry et al., 2009), dynamic processes (Dorazio et al., 2010) and effect of species abundance (Tobler et al., 2015). MSOMs could constitute a relevant method for valuing multi-species incidence data in order to obtain a baseline for the evaluation of biological survey results (both in terms of community richness and specific occurrence), and to draw up guidelines for the implementation of cost-efficient protocols (Wintle et al., 2012).

Biases related to imperfect species detectability, are now widely documented and taken into account when studying the occurrence or species richness of a large number of taxa, including plants (Shefferson et al., 2006; Chen et al., 2013), fishes (Holtrop et al., 2010; Anderson et al., 2012), amphibians (DeSolla et al., 2005; Schmidt, 2005), reptiles (Durso et al., 2011; McGrath et al., 2015), large mammals (Rovero et al., 2014; Bowler et al., 2016),
bats (Gorressen et al., 2008) or birds (Olea & Mateo-Tomas, 2011; Tingley & Beissinger, 2013). Unfortunately, inferential biases for insect sampling are often neglected and applications of correcting methods are still few (Kellner & Swihart, 2014; Alabano et al., 2015). However, the detection in insect can notably vary between species according to their visual appearance (color, size ...), their behavior (height of flight ...) (Dennis et al., 2006), their occupation of space during the day (Dennis & Sparks, 2006) or the life stage considered during surveys (Alliberti Lubertazzi & Ginsberg, 2009; Bried et al., 2012a). The heterogeneity of the detection of insects is also governed by survey conditions, in particular by the date of the visits (Pellet, 2008), the number of visits and their duration (Bried et al. 2012b), the period of the day at which they are performed (Scalercio et al., 2009), the weather conditions during the visit (Bried & Pellet, 2012), the experience of the observers (Lewandowski & Specht, 2015) or the spatial distribution of survey locations on a site (Nufio et al., 2009). Given the large proportion of species characterized by low densities or low probability of detection (Driscoll, 2010; New, 2010), the actual entomological richness of a given site is particularly difficult or impossible to achieve with limited effort (Foggo et al., 2003).

Long neglected in the field of conservation biology despite the significant amount of
biodiversity they represent (Dunn, 2005), insects are now recognized as major components of
the functioning of a wide variety of ecosystems (Prather et al. 2013; Yang & Graton, 2014),
largely affected by global change (Fonseca, 2009). Due to their great diversity, a large part of
which remains undescribed, insect conservation is still a challenge in terms of evaluation and
ranking of conservation priorities (New, 2010). At the European level, only a few popular
groups, for which biology and distribution are particularly well documented, have been
evaluated and concentrate the majority of conservation efforts (Leandro et al., 2017).
Butterflies, dragonflies and grasshoppers are notably among the most studied taxa in Western
Europe (Isaac & Pocock, 2015). Because of this legal protection covering certain species,
these taxa belong to the groups commonly surveyed during the realization of EIAs. These
surveys aim to specifically searching for these protected or vulnerable and, to a lesser extent,
to estimate the richness and the patrimoniality of the species assemblages present on the site.
The sampling effort is adjusted according to the conservation issues potentially occurring on
the site and especially according to economic constraints (Field et al., 2005). In the most
common cases, surveys are conducted through a limited number of visits, focusing on the
periods of activity of protected or vulnerable species, in order to maximize their detection,
and during which the three taxa are sought in simultaneous ways. Despite the various
detection biases mentioned above and the rise of the culture of public policy evaluation, the
quality of the results resulting from this current practice of entomological inventories under
EIA conditions remains little evaluated at present.

The aim of the present study was to assess the completeness of the entomological
surveys carried out under classical conditions of EIA, both in terms of total species richness
and of protected or vulnerable species detection. We conducted insect surveys under EIA
conditions at 20 sites located in the French Mediterranean area. The surveys were targeted on
four groups of insects (butterflies, dragonflies, grasshoppers and cicadas) commonly followed
in this type of study in the area, where conservation issues are particularly high (diversity,
endemism ...). In order to evaluate the link between the completeness of these results and the
sampling effort, the number of day of EIA in each site was doubled by a survey carried out
under closed conditions. Incidence data from these two levels of effort were analyzed under a
MSOM, estimating the potential richness of the community and the occurrence probability of
each observed species, correcting biases related to the heterogeneity of their detection. These
estimates were used as reference values to calculate the completeness of the results and in
particular to answer the following questions: (1) Is there a significant gap between the
potential richness of insect communities present at the sampled sites and the richness
observed in classical EIA conditions? (2) To what extent is this gap limited by the increase in
sampling effort applied to a site? (3) Are detection biases evenly distributed within the
sampled community? (4) Which characteristics of the surveyed sites and the survey
conditions have an influence on the proportion of observed richness and the probability of
species detection? (5) What average effort would be required to ensure reliable detection of
species under optimal survey conditions?

### III.2. Material & methods

**III.2.1. Study area and site-level habitat variables**

This study was conducted in the Languedoc and Provence regions, located in southern
continental France. The study area extended along the Mediterranean Sea, between Narbonne
(Lat. 43°11′N, Long. 3°00′E) in the west and Toulon (Lat. 43°07′N, Long. 5°55′E) in the
east, within an area of about 80km from the coastline to the Cevennes region in Languedoc
and Luberon massif in Provence (Lat. 44°03′N for the northern site) (Fig.3.1). This area
shows a Mediterranean climate, characterized by dry summers and mild winters, with a
maximum rainfall in autumn (Debussche et al., 1999).

<figure><img src="image_45.png" /><figcaption>**Figure 3.1 :** Map of the study area in southern France.</figcaption></figure>

Twenty sites were selected for the collection of entomological data, based on the location of development projects (commercial area, highway or railway, photovoltaic power station, wind turbine ...) requiring biological surveys as part of an EIA. In order to allow consistent modeling of community structure and species parameters (see below), we selected sites located within landscapes showing similar characteristics. These landscapes were mainly structured by disturbances related to human activities (agriculture, grazing, forestry ...) and the important dynamics of secondary successions that followed the abandonment of most of these practices in the second half of the 20th century, as shown by Sirami *et al.*, (2010) for a sector comprised within the global area of these twenty selected sites. The perimeter of selected sites mainly included a mosaic of thermophilous habitats *i.e.* xeric grasslands, dry wastelands, scrublands and woodlands mostly dominated by the holm oak (*Quercus ilex*). Aquatic habitats within sites were represented by sunny streams, large slow-flowing rivers and temporary wetlands.
A majority of these sites (N=13) were located in coastal plain at less than 100m altitude, but seven of them corresponded to sectors with stronger topographic variation, located from 150m to 650m above sea level. They were distant from each other by at least 2.5 km (measured from their centroids) and their average area was 540 ha (ranging from 9 ha to 950 ha) (Suppl.S3.1). They displayed an important variability in the diversity of natural habitats they covered, going from five to 65 natural habitats on a site. This diversity was measured on each site from a mapping of natural habitats, identified on the basis of botanical field surveys and following the Corine Land Cover nomenclature. These mapped data were also used to calculate the proportion of the site area occupied by closed habitats (e.g. shrubland, hedge, sclerophyllous vegetation, transitional woodland-shrubs, forest...). This proportion varied widely between sites, ranging from less than 1% for the most opened to 98% for the most wooded site. These site-level habitat variables were calculated using the Qgis mapping software (Quantum GIS Development Team, 2015).

### III.2.2. Surveyed taxa

Four groups of insects were surveyed in each studied site: (1) butterflies (Lepidoptera: Papilionoideae) and burnet mothes (Lepidoptera: Zygaenidae), (2) dragonflies (Odonata), (3) grasshoppers (Orthoptera), and (4) cicadas (Hemiptera: Cicadidae) (Table 3.1). These groups belonged to the most studied insect taxa in the context of EIAs. These groups also include a significant number of species targeted by regulatory protection policies (representing around 65% of the protected entomological diversity in France, www.legifrance.gouv.fr) or considered as highly vulnerable at the scale of the French Mediterranean area. Papilionoideae and Zygaenidae were treated in the same group in the following analyzes. For more clarity, they were grouped under the term "butterfly" in the rest of the paper.

The French diversity of these taxa is highly variable, ranging from 22 species for cicadas to 231 species for butterflies (Puissant, 2006; Lafranchis et al., 2015) (Table 3.1). The Mediterranean area, which is the richest region in Europe in terms of invertebrate species diversity (Blondel et al., 2010), is a hotspot of diversity across France for the studied taxa. About 80% of the national species richness of each of these groups is present in the two regions covered by the study area (Table 3.1). These two regions also included a significant proportion of the range of many protected or vulnerable species (IUCN France et al., 2014; 2016). Given the known range of species and their ecological requirements (Puissant, 2006;
Grand *et al.*, 2014; Lafranchis *et al.*, 2015; Sardet *et al.*, 2015), the diversity of these four groups potentially present within the targeted landscapes of the study area represented between 60 and 75% of their regional richness (Table 3.1).

<table><caption>**Table 3.1 :** List of surveyed taxa. The known species richness of each group was summarized at the scale of France ('nat.') and of studied regions ('Reg.', Languedoc and Provence). 'Land.' refers to the potential number of species which could be observed in surveyed habitat types (based on local atlases and species ecological requirements). The total number of species observed during the present study ('Obs.') was also indicated for each taxon, as well as the part of these species covered by conservation status ('Obs.Cons'). Methods applied to survey different life stages of each taxon were also summarized. 'vis.' refers to visual methods, 'ac.' to acoustic method and 'col.' to collection method.</caption><thead><tr><th>Surveyed taxa</th><th>Order</th><th>Reg. / nat.</th><th>Land.</th><th>Obs.</th><th>Obs.Cons.</th><th>Sampled life stage and survey methods</th></tr></thead><tbody><tr><td>Papilionoideae</td><td>Lepidoptera</td><td><strong>231</strong> / 262</td><td>140</td><td>93</td><td>7</td><td>Adult, larvae, eggs (vis.) ; hostplant (vis.)</td></tr><tr><td>Zygaenidae</td><td>Lepidoptera</td><td><strong>22</strong> / 26</td><td>15</td><td>9</td><td>1</td><td>Adult (vis.) ; hostplant (vis.)</td></tr><tr><td>Odonata</td><td>Odonata</td><td><strong>76</strong> / 96</td><td>57</td><td>46</td><td>9</td><td>Adult (vis.) ; exuviae (col.)</td></tr><tr><td>Orthoptera</td><td>Orthoptera</td><td><strong>207</strong> / 244</td><td>126</td><td>71</td><td>15</td><td>Adult (vis. and ac.) ; larvae (vis.)</td></tr><tr><td>Cicadidae</td><td>Hemiptera</td><td><strong>18</strong> / 22</td><td>12</td><td>8</td><td>1</td><td>Adult (vis. and ac.) ; exuviae (col.)</td></tr></tbody></table>

Two categories of species were distinguished within these four taxa according to the conservation issue they represented: (1) species considered as conservation issues ('covered'), which combined 'protected' species (taken from the French legislation) and 'vulnerable' species at the national or local level, and (2) species with no conservation status ('uncovered'). Vulnerable species have been identified according to their patrimonial status, their scarcity or the region's responsibility for their conservation. This information was collected from several sources: (1) national red lists (Sardet & Defaut, 2004; IUCN France *et al.*, 2014; 2016) and regional red lists (Bence *et al.*, 2011; Bence, 2014), (2) lists of species considered as indicator for the designation of zone of high ecological interest (https://inpn.mnhn.fr/programme/inventaire-znieff/presentation), (3) national (Dupont, 2010) and regional action plans (OPIE *et al.*, 2011; Lambret *et al.*, 2011), (4) national (Grand *et al.*, 2014; Lafranchis *et al.*, 2015; Sardet *et al.*, 2015) or regional distribution atlases (Jaulin *et al.*, 2011; Papazian *et al.*, 2017). This typology was used by the company to define the sampling strategy on each of the sites monitored.
<table><caption>**Table 3.2 :** Posterior estimates of the species richness for each surveyed site, as estimated under the MSOM (with mean, standard deviation, CI<sub>95%</sub>). The number of species observed during field surveys was also included for each site, under the classical and doubled conditions. 'Surveys' refers to the number of visits conducted during the classical EIA.</caption><thead><tr><th rowspan="2">Site</th><th rowspan="2">Locality</th><th rowspan="2">Surveys</th><th rowspan="2">Classical EIA condition</th><th rowspan="2">Doubled EIA conditions</th><th colspan="4">MSOM predictions</th></tr><tr><th>Mean</th><th>Standard deviation</th><th>CI 2.5%</th><th>CI 97.5%</th></tr></thead><tbody><tr><td>1</td><td>Lieuran-lès-Béziers</td><td>5</td><td>93</td><td>109</td><td>147.78</td><td>7.41</td><td>135</td><td>164</td></tr><tr><td>2</td><td>Mauguio</td><td>4</td><td>49</td><td>68</td><td>121.68</td><td>7.02</td><td>109</td><td>136</td></tr><tr><td>3</td><td>Lespignan</td><td>4</td><td>63</td><td>76</td><td>112.65</td><td>6.51</td><td>101</td><td>126</td></tr><tr><td>4</td><td>Fabrègues</td><td>3</td><td>60</td><td>77</td><td>123.43</td><td>6.92</td><td>111</td><td>138</td></tr><tr><td>5</td><td>Montdardier</td><td>4</td><td>86</td><td>99</td><td>131.74</td><td>6.14</td><td>121</td><td>145</td></tr><tr><td>6</td><td>Rians</td><td>3</td><td>80</td><td>98</td><td>136.53</td><td>6.83</td><td>124</td><td>151</td></tr><tr><td>7</td><td>Gallargues-le-Montueux</td><td>2</td><td>33</td><td>47</td><td>110.60</td><td>7.65</td><td>96</td><td>126</td></tr><tr><td>8</td><td>Pézenas</td><td>2</td><td>52</td><td>72</td><td>126.21</td><td>7.13</td><td>113</td><td>141</td></tr><tr><td>9</td><td>Bessan</td><td>2</td><td>53</td><td>62</td><td>121.92</td><td>7.48</td><td>108</td><td>137</td></tr><tr><td>10</td><td>Saint-Thibéry</td><td>2</td><td>75</td><td>79</td><td>132.75</td><td>7.28</td><td>119</td><td>148</td></tr><tr><td>11</td><td>Pinet</td><td>9</td><td>79</td><td>94</td><td>124.71</td><td>6.63</td><td>113</td><td>139</td></tr><tr><td>12</td><td>Poussan</td><td>9</td><td>93</td><td>110</td><td>132.84</td><td>6.01</td><td>122</td><td>146</td></tr><tr><td>13</td><td>Murviel-lès-Béziers</td><td>5</td><td>114</td><td>127</td><td>160.87</td><td>7.16</td><td>148</td><td>176</td></tr><tr><td>14</td><td>Saint-Pargoire</td><td>7</td><td>91</td><td>103</td><td>135.68</td><td>6.66</td><td>124</td><td>150</td></tr><tr><td>15</td><td>Saint-Victor-la-Coste</td><td>4</td><td>81</td><td>90</td><td>119.55</td><td>6.38</td><td>108</td><td>133</td></tr><tr><td>16</td><td>Lamanon</td><td>4</td><td>56</td><td>63</td><td>102.64</td><td>6.67</td><td>90</td><td>116</td></tr><tr><td>17</td><td>Istres</td><td>3</td><td>56</td><td>65</td><td>125.39</td><td>7.78</td><td>111</td><td>141</td></tr><tr><td>18</td><td>Valliguieres</td><td>2</td><td>50</td><td>64</td><td>111.47</td><td>7.35</td><td>98</td><td>127</td></tr><tr><td>19</td><td>Saint-Hilaire-d'Ozilhan</td><td>2</td><td>48</td><td>59</td><td>113.05</td><td>7.51</td><td>99</td><td>128</td></tr><tr><td>20</td><td>Vendargues</td><td>3</td><td>55</td><td>64</td><td>114.18</td><td>6.98</td><td>101</td><td>129</td></tr></tbody></table>

### III.2.3. Data collection and survey conditions

Data were collected over two consecutive years: 8 sites were sampled in 2015 and 12 sites in 2016. Insect communities were surveyed at each site through a sampling plan involving repeated visits, all performed during the same year for a given site. The number of visits was very variable between sites, ranging from 2 to 9 visits with a mean of 3.95 (± 2.16) visits by site (Table 3.2). The number of visits was fixed for each site following the usual procedure of a consultant in ecological engineering carrying out an EIA, according to the importance of the conservation issues potentially present on the site and the budget allocated by the developer. All visits were conducted between the beginning of April and the beginning of August. They were distributed within this interval according to the period when the highest diversity of each sampled taxa was detectable and to the phenology of species that were of conservation concern potentially present on each site. The visits were conducted under favorable weather conditions for insect activity. Rainy or cloudy days (cloud cover estimated to be greater than 50%), with low or too high temperatures (generally below 15° C or above 35° C) and high winds (greater than 20 km/h) have been avoided (Pollard & Yates, 1993; Kéry & Plattner,
2007). The majority of visits were made during the day, between 10:00 am and 18:00 pm, but
night surveys (between 22:00 pm and 02:00 am) were performed on some sites to maximize
detection of some grasshopper species particularly active at night (concerned 10 sites and 11
visits). The length of survey varied a lot between the different visits, comprised between 2h
and 8h of survey on the same site, spread out over a whole day or only part of the day
(morning, afternoon or night according to the time the visit started).

The observer did not follow a fixed route during the surveys, but rather attempted
complete coverage of the diversity of natural habitats present within the site, by prioritizing
the habitats of species which representing conservation issue or areas potentially displaying
the greater entomological diversity (e.g. heterogeneous habitats...). Although such wandering
surveys are not standardized, they constitute a low-cost method allowing to collect data on
species occupancy over large area, which is the main focus of EIAs (Longcore et al., 2010 ;
Taron & Ries, 2015). Repeated visits were carried out by three professional observers (GD
and SD, authors of the present paper, and one other person from the Biotope compagny),
considered to have similar skills and experience, with only one of them surveying a site
during a visit.

A combination of several sampling techniques was used during each visit to allow
detection of the different taxa studied (Table 3.1). The search for adult stages (with sometimes
temporary capture to identify the most cryptic species) was preferred for all taxa. Caterpillars
and host plants of butterflies and exuviae of dragonflies and certain cicadas have been
occasionally sought. Mating calls produced by a large number of Orthoptera and Cicada were
also used for the acoustic detection of some of the most cryptic species (Jeliazkov et al.,
2016). All detected individuals have been identified at the specific level according to the most
recent taxonomic reference available at the scale of France (TaxRef v.10.0,
www.inpn.mnhn.fr).

In order to test the influence of sampling effort on inventories completeness in the case
of EIA, the number of visits performed at each surveyed site has been multiplied by two. For
each visit, a duplicate was made between 1 and 14 days after the initial visit, in similar
conditions (in terms of weather conditions, time of beginning, and duration of the visit...). The
same sampling technics were applied through wandering surveys to obtain occupation data for
each species. Although survey routes frequently cannot be closely replicated according to this
type of survey, the observer has tried to cover an area and a diversity of habitats as close as
possible to the conditions of the initial visit. All the duplicates were made by the same
observer (GD), generally different from the observer from the initial visit but who has the
same skills and level experience (expert entomologist with professional training). Considering
these duplicates, a total of four to 18 replicated visits per surveyed site were thus available for
analyses. These spatial and temporal replicated visits were used to construct the species-
specific detection histories that were used for the implementation of occupancy model (as
described below).

### III.2.4. Multi-species occupancy model

The completeness of EIA entomological surveys was evaluated by estimating the difference
between the observed species richness (for the whole community and each species category)
from the two levels of effort applied (classical and doubled) and the number of species
potentially occurring on each site, accounting for those undetected during sampling. We used
a MSOM to estimate this unknown total species richness of the insect community (Dorazio &
Royle, 2005; Dorazio *et al.*, 2006).

The MSOM is an extension of the single-species occupancy model (MacKenzie *et al.*, 2002) to all species in a community, whether or not they were detected during sampling (Sanderling *et al.*, 2014). Occupancy models produce estimates of two probability-based parameters: detection (p), which is defined as the probability of detecting a species given that it is present at a site and available for capture; and occurrence (Ψ), which is the probability that a species is present at a site during sampling (Bailey *et al.*, 2014) (Suppl.S3.2). Sampling design through repeated visits is then a basic requirement to the distinction between true absence and non-detection (MacKenzie *et al.*, 2006). Given their structure, these models are referred as hierarchical (Royle & Dorazio, 2008), with these both parameters estimating from two linked processes: the ecological process (site occupied vs. unoccupied) and the detection process (species detected vs. undetected at occupied sites) (Guillera-Arroita *et al.*, 2014; Gimenez *et al.*, 2014). The MSOM expands these levels across all detected species and allows taking into account partially or never detected species through addition of a third hierarchical component corresponding to community process (Dorazio *et al.*, 2011; Iknayan *et al.*, 2014).
Species-level and community-level attributes are then combining into the same modeling framework, with species treated as random effects governed by ‘hyper-parameters’ of the community (Hunt *et al.*, 2013). The detection and occupancy probabilities of each species are then assumed to come from this community-level distribution and are used to inform the mean and variance of this distribution. By combining all individual species through this community hierarchical process, precision of species-specific estimates is improved, leading to a more efficient use of data resulting from multispecies surveys. Such ‘data shrinkage’ (Hobbs & Hooten, 2015) allows to make inference for rare species with a limited number of detections that otherwise would be difficult or impossible to model on their own (Zipkin *et al.*, 2009; Tingley & Beissinger, 2013). It could give significant advantages over SSOMs, provided that occurrence responses of species integrated in a same model can conceivably come from a common distribution (Sauer & Link, 2002).

Ecological equivalence constitutes one of the main assumptions of MSOM aiming to estimate occurrence of non-detected species (Dorazio *et al.*, 2006). Such equivalence must be considered in the sense that species should be likely to respond similarly, but not identically, to changes in their environment or habitat (Dorazio *et al.*, 2011). Although the occurrence may vary significantly between each species, all species are related in the sense that they belong to a larger community of ecologically similar species. The survey of taxa mainly breeding in open habitats, at the level of sites located within equivalent landscape entities allowed us to limit the potential biases related to the non-respect of this hypothesis. Site-occupancy analyses may also be susceptible to bias from violations of population closure (Kendall, 1999; Rota *et al.*, 2009). Since the surveys were conducted during a single flying season, we assumed community closure between the first and the last visit on a site. The assumptions of independence of sampling units and absence of false positive (i.e. species falsely detected in an unoccupied site following misidentification) (MacKenzie *et al.*, 2006) were also considered respected due to the distance between sites and the experience of the observers.

### III.2.5. Modeling framework

We implemented a single season MSOM similar to that used by Zipkin *et al.* (2010) to estimate species and community responses to site covariates and survey covariates. We let $z(i, j)$ denote true occurrence status modelling as a binary variable in which $z(i, j) = 1$ when
species *i* occupies the site *j*, and *z*(*i*, *j*) = 0 otherwise. This occurrence state was assumed to be the outcome of a Bernoulli random variable, denoted by:

$$z(i, j) \sim \text{Bernoulli}(\psi_{i,j}) \qquad (1)$$

where $\psi_{i,j}$ is the probability that species *i* occurs at site *j*. As occurrence is imperfectly observed, the occurrence state $z(i, j)$ is usually not known with certainty. Instead, we observed data $x(i, j, k)$ for species *i* at site *j* during replicate visit *k*. Conditional on the outcome of the occurrence state, this detection process was also modelled as a Bernoulli random variable:

$$x(i, j, k) \sim \text{Bernoulli}(p_{i,j,k} \times z(i, j)) \qquad (2)$$

where $p_{i,j,k}$ is the detection probability of species *i* for the *k*th visit at site *j*, given that species *i* is in fact present at site *j* (i.e. $z(i, j) = 1$). The probability of detecting the species *i* at site *j* will then be zero if the species does not occupy site *j*, since $z(i, j) = 0$.

We considered that the occurrence ($\psi_{i,j}$) and detection ($p_{i,j,k}$) probabilities varied by species and were influenced by habitat and survey covariates, respectively. These effects were incorporated into the model using the logit link function (Kéry & Royle, 2008). Each covariate was centered and scaled prior to analysis. Covariates standardization allowed direct comparison of model coefficients as effect sizes relative to variation in each covariate (Hunt et al., 2013). We modeled species-specific occurrence probabilities as a function of three habitat covariates: *area* was the area of the surveyed site (in ha); *divhabitat* covariate corresponded to the number of natural habitat present within the site (in number of natural habitats); *propclosed* was defined as the proportion of closed habitats within the site (varied from 0% to 100%). The first two covariates were only included as *linear effect* on occurrence, since we did not have strong a priori reasons for expecting non-linear effects. However, we had both *linear* and *quadratic effect* for the third covariate (*propclosed*²) in order to include possible intermediate response of species occurrence to this habitat characteristic. We used the following equation to relate these species-specific habitat covariates and occupancy to the hierarchical models described above:

$$\text{logit} (\psi_{i,j}) = u_i + a1_i\text{area}_j + a2_i\text{divhabitat}_j + a3_i\text{propclosed}_j + a4_i\text{propclosed}_j^2 \quad (3)$$
where $u_i$ correspond to the occurrence probability (on the logit-scale) for species $i$ in sites with ‘average’ values of habitat covariates (given their standardization). The coefficients $a1_i$, $a2_i$, $a3_i$ and $a4_i$ were the effects of each habitat covariates on species $i$ respectively.

In the same way, species-specific detection probabilities were assumed to vary based on three survey covariates: *date* of the visits (linear and quadratic effects in order to detect potential optimum in species activities during the surveyed season, expressed in Julian date); *hour* at which each visit started (linear effects, in hours); *length* of each visit (linear effects, in hours). We modelled the effects of these covariates on detection probability with a logit function:

$$ \operatorname{logit}(p_{i,j,k}) = v_i + b1_i date_{jk} + b2_i date_{jk}^2 + b3_i hour_{jk} + b4_i length_{jk} \quad (4) $$

where $v_i$ correspond to the detection probability (on the logit-scale) for species $i$ at ‘average’ values of survey covariates, modeled by the coefficients $b_i$.

Species-specific occurrence and detection parameters were random effects governed by a common community-level hyper-parameter. Each parameter was modeled as drawn from a normal distribution described by the community mean ($\mu$) and standard deviation ($\sigma$) hyper-parameters. For example, we specified species-specific occurrence as:

$$ u_i \sim N(\mu_u, \tau_u) \quad (5) $$

where $\mu_u$ is mean occurrence from the community and $\tau_u$ the standard deviation between species. The distribution of hyper-parameters was specified in the same way for each $a_i$ and $b_i$ covariate effect.

The total richness was estimated using a data augmentation procedure (Divino et al., 2011; Broms et al., 2016) which allows estimating the number of species in the community including those never detected during visits (Royle et al., 2007; Royle & Dorazio, 2012). This process consists in an addition of an arbitrarily large number of all-zero detection histories corresponding to hypothetical undetected species (Kéry & Royle, 2008). Zero-inflated version
of the model could then be seen as a 'super-community' (M) from which the observed community (n) was drawn. Using this method, we assumed a uniform (0, M) prior for N, the 'true' species richness, where M was a fixed constant chosen to be much greater than the number of observed species (n). Considering the number of insect species detected during our visits and the potential species richness of the studied landscapes in our study area (Table 3.1), we added 200 species with zero encounter history to the observed community. The occurrence process was modified as:

$$ 
\begin{array}{l}
z(i, j) \sim \text{Bernoulli}(\psi_{i,j} \times \omega_i), \\
\omega_i \sim \text{Bernoulli}(\Omega)
\end{array} 
\qquad (6) $$

where i was a vector including all the species from 1 to M and ωᵢ was the probability of a species from the 'super-community' to be available to capture (Dorazio et al., 2006). If ωᵢ = 0, species was unavailable to capture and cannot occur in the study area (ψᵢⱼ = 0). If ωᵢ = 1, species is a member of the observable community and had a probability ψᵢⱼ to occur in the area. The estimation of the parameter Ω, which governed the distribution of this probability, was an indirect way to have access to N. We estimated this species richness including unobserved species at the scale of the study area, as well as at each visited sites. Species richness was also estimated for each taxonomic group and conservation class in each visited sites.

### III.2.6. Model implementation

The model was implemented in a Bayesian framework using the BUGS language and run in JAGS (Plummer, 2003) from the R software (R Development Core Team, 2015) through the package *jagsUI* (Kellner, 2015). Given the lack of prior knowledge of a parameter's true value, parameters and hyper-parameters were implemented with non-informative priors (Link et al., 2002; Mata et al., 2014). We used a uniform distribution from 0 to 1 for community-level parameter Ω, and for the community mean (μ) of species-specific occurrence and detection probabilities (uᵢ and vᵢ). The means of the site-specific (aᵢ) and survey-specific (bᵢ) effects were given normal priors with mean zero and variance 1,000. The community standard deviations (σ) of all these parameters were given gamma priors as *gamma* (0.1, 0.1).
We ran eight parallel chains of 150,000 iterations, after a burn-in of 50,000 iterations and thinned the posterior chains by 50. We assessed convergence by visual assessment of MCMC chains and by using the Gelman-Rubin statistic ('Rhat'), with values <1.1 considered acceptable (Gelman & Hill, 2007). An assessment of model fit to the actual dataset was performed using a Bayesian p-value approach and an estimation of a 'lack-of-fit' statistics (Kéry & Schaub, 2012; Broms et al., 2016) (Suppl.S3.3). In contrast to the frequentist models for which the use of model selection criteria is widespread, there is currently no consensus regarding the appropriate methods for covariates selection or model comparison in the framework of complex hierarchical models (Hooten & Hobbs, 2015; Broms et al., 2016). The importance of the effects of these covariates on species-level occurrence and detection probabilities were assessed based on posterior distributions of aᵢ and bᵢ parameters, considering non-significance if their 95% posterior credibility interval (CI95%) included zero (Burton et al., 2012; Chen et al., 2013).

### III.2.7. Minimum survey effort requirements

In an operational approach, we aimed to use species-specific detection probabilities given by the MSOM to estimate minimum survey effort required to ensure some degree of confidence in the representativeness of an EIA results. We followed the analytical framework developed by Wintle et al. (2012), based on the estimation of the probability that a species was present on a site given that it was not detected following a certain number of visits (Pr(presence|non detection)). By distinguishing the part of apparent absence linked to detection defects from those related to true absences, this method allowed to determine the number of sequential non-detections necessary to achieve a pre-specified level of confidence in the inference of species true absence. In addition to the prior fixation of this confidence level, this method required a prior knowledge of the detectability of the species (which may be dependent on the level of some covariates) and its prevalence within the study area (i.e. its expected rate of occurrence, which could be seen as a measure of the favorability of the area for the species) (Petitot et al., 2014). According to this method, the minimum number of sequential non-detection required to infer the absence of a species (n*) is then estimate as:

$$n^* = \frac{\log(\frac{\alpha}{1-\alpha}) - \log(\frac{\gamma}{1-\gamma})}{\log(1-p')} \quad (7)$$
where $\gamma$ was the prior probability that the species is present on the site (prevalence), $p'$ corresponded to the prior probability of detecting the species given it is present and $\alpha$ was the posterior probability that the site is occupied given species was unobserved (which represents the level of confidence).

We used this equation to estimate the minimum number of visits without detection required to be 95% sure that a species is absent from a site. Because of the lack of knowledge on regional prevalence for the studied species pool, species-specific occurrence probabilities ($\psi_{i,j}$) were used as prior belief of species prevalence. Inference was therefore limited to sites presenting the same characteristics (in terms of landscape, climate...) than the present survey area, and the prevalence could be seen as a measure of the favorability of such characteristics to target species (species with high prevalence had more chance to occur in the site). The species-specific probability ($p_{i,j,k}$) estimated from the MSOM were used as prior detection probability $p'$. Considering the three survey covariates included in the simulation (date, hour, length), the value of $p_{i,j,k}$ was chosen for a combination of covariate level leading to the higher probability of detection of each species, respectively. The minimum survey effort requirement was thus estimated for the case of repeated visits conducted under survey conditions considered as optimal. This number was estimated for each of the 227 species observed on at least one surveyed site. In order to ensure a certain level of accuracy of these estimates, the species observed in less than two visits in doubled EIA conditions were excluded from this part of the analysis (representing 22 species).

### III.2.8. Statistical analysis

The results from the two applied sampling plans, classical EIA and doubled EIA, were compared in terms of observed species richness and sampling coverage. The species richness estimated from the MSOM was used as a reference value for defining the completeness of classical and doubled surveys. This sampling coverage was calculated at the community level as well as at the site level. Given the non-normality of the data, classical EIA, double EIA and MSOM richness were compared from a non-parametric rank-based Friedman test (Oja, 2010). The sampling coverage of the first two modalities was compared using Wilcoxon's signed-rank test. These comparisons were repeated for each group of species (taxonomy or conservation status). The sign and the significance of the correlation between the mean value
of sampling coverage by sites and habitat covariates (i.e. site area, habitat diversity and percentage of closure) were estimated with a Spearman's rank correlation test.

Considering the species-specific level, the mean occurrence and detection probabilities were compared between these same groups by means of a Kruskall-Wallis H test, followed by pairwise comparisons by the Wilcoxon rank-sum test, with p-values adjusted using the 'fdr' correction (Benjamini & Hochberg, 1995). An absolute bias value in observed occurrence was also calculated for each species (Kéry & Royle, 2008). This bias value corresponds to the difference between the observed occurrence of each species (the proportion of sites at which they were observed) and their mean occurrence probability, estimated from the MSOM ($\psi$). Absolute bias value was used as an indicator of sampling coverage at the specific scale. Two values of this bias were calculated: one from the occurrence observed under classical EIA conditions, and another from the occurrence observed under doubled EIA conditions. These values were compared through a Wilcoxon signed-rank test. Absolute bias was also compared between the different groups of species defined above (Kruskall-Wallis H test and pairwise comparisons by the Wilcoxon rank-sum test).

Estimates of the minimum number visits required to certify the absence of a species were compared across species according to their conservation status. These comparisons were made from a Kruskall-Wallis H test, followed by a comparison of the Wilcoxon rank-sum test, with p-adjusted values using the 'fdr' correction. All these analyses were performed using the R software (R 3.3.3, R Development Core Team, 2015).

### III.3. Results

#### III.3.1. Observed richness

Field surveys allowed collecting more than 4300 incidence data from 158 visits across the 20 studied sites. A total of 227 insect species were observed on all visits: 102 butterflies (45%), 46 dragonflies (20%), 71 grasshoppers (30%) and 8 cicadas (4%) (Table 3.1). This richness represents approximately 41% of the total number of known species in the biogeographic region, and 65% of the estimated gamma diversity for the landscapes targeted by the present study (Table 3.1). Among the observed species, 33 species were covered by conservation status (10 protected species and 23 vulnerable species) (14.5%). Twenty-two species (all taxa
combined) were only detected during a single visit (even under doubled conditions). Conversely, four species accounted for almost one-tenth of the observations (*Lasiommata megera*, *Pieris rapae*, *Polyommatus icarus* and *Tettigonia viridissima*).

Higher species richness was observed under doubled EIA conditions, with 10 species more than under classical EIA conditions (217 species observed in this last case). At the scale of the studied sites, the doubling of the sampling effort allowed to detect an average number of species significantly greater than under classical EIA conditions (Friedman test, P< 0.001) (Fig.3.2a). An average of 60.35 species (± 20.57 species) was observed per site under classical EIA conditions while 81.3 species (± 21.20 species) were detected on average under doubled EIA conditions (Table 3.2); that corresponds to a gain of 20.95 species (+25.8 %). Butterflies and uncovered species showed the higher difference of observed species between classical and doubles conditions (6.7 and 12.1 respectively).

<table><caption>**Table 3.3 :** Posterior estimates of the mean species richness per site for the community ('All taxa') and each group of species (as defined in Materials and methods), as estimated under the MSOM (with mean, standard deviation, CI95%). The mean number of species observed during field surveys was also included for each site, under the classical and doubled conditions.</caption><thead><tr><th rowspan="2">Surveyed taxa</th><th rowspan="2">Classical EIA condition</th><th rowspan="2">Doubled EIA conditions</th><th colspan="4">MSOM predictions</th></tr><tr><th>Mean</th><th>Standard deviation</th><th>CI 2.5%</th><th>CI 97.5%</th></tr></thead><tbody><tr><td>All taxa</td><td>68.35</td><td>81.30</td><td>125.28</td><td>15.09</td><td>100</td><td>171</td></tr><tr><td>Lepidoptera</td><td>31.00</td><td>37.70</td><td>55.61</td><td>8.81</td><td>29</td><td>73</td></tr><tr><td>Odonata</td><td>9.70</td><td>11.85</td><td>17.06</td><td>9.84</td><td>5</td><td>35</td></tr><tr><td>Orthoptera</td><td>24.60</td><td>27.95</td><td>38.32</td><td>4.38</td><td>30</td><td>46</td></tr><tr><td>Cicadidae</td><td>3.50</td><td>3.75</td><td>5.25</td><td>1.28</td><td>2</td><td>7</td></tr><tr><td>Covered species</td><td>3.84</td><td>4.25</td><td>7.44</td><td>2.81</td><td>2</td><td>13</td></tr><tr><td>Uncovered species</td><td>65.05</td><td>77.15</td><td>108.10</td><td>11.73</td><td>88</td><td>137</td></tr></tbody></table>
### III.3.2. Estimated richness and sampling coverage

The goodness-of-fit test showed an adequate fit of the MSOM to the data (p = 0.75, lack of fit = 1.01, see Suppl S3.3 for detailed results). The model estimated 313.9 species in the surveyed community on the whole study area (CI95% = 271 - 382; Suppl S3.4). The average richness per site was estimated at 125.28 species (Table 3.3), ranging from 102 to 160 species depending on the site considered (Table 3.2). Butterfly estimated richness is the highest, with an average of 55.61 species per site, compared to only 5.65 for cicadas (Table 3.3). Estimated richness was significantly higher than observed richness under classical and doubled EIA conditions, regardless of the group of species considered (Friedman test, P < 0.001) (Fig.3.2a).

When considering these estimated richness values as the upper limit of potential site richness, sampling coverage showed significant variability among survey methods (i.e. classical vs doubled), sites and species groups. At the community level, sampling coverage was significantly higher in doubled EIA conditions (Wilcoxon test, P < 0.001), with an average of 64.17% (± 11.33%) of species detected at one site, whereas only 53.82% (± 11.76%) of species were detected in average under classical EIA conditions (Fig.3.2b). Regardless of the group of species considered, the sampling coverage never exceeded 75% (Fig.3.3). We could not find significant difference between taxonomic groups when comparing their average sampling coverage per site, regardless of the survey conditions applied (Kruskall test, P > 0.05; Fig.3.3a). On the other hand, although the species covered by a conservation status do not differ from those not covered in terms of sampling coverage under classical EIA conditions (Wilcoxon test, P> 0.05), they had a significantly lower average sampling coverage under doubled EIA conditions (Wilcoxon test, P < 0.05), with an average of 51.43% of detected species per site (± 26.11%) (Fig.3.3b). Cicadas were the only species group that showed no significant increase in the average sampling coverage when changing from classical to doubled EIA conditions (Wilcoxon test, P > 0.05). For all other groups, sampling coverage significantly increased with double visits (Wilcoxon test, P < 0.05; Fig.3.3).
<figure><img src="image_46.png" /></figure>

**Figure 3.2:** Mean values of species richness (a) and sampling coverage (b) among surveyed sites under classical and doubled EIA conditions. The observed species richness was also compared to mean posterior estimates of the hierarchical MSOM. Sampling coverage was calculated with MSOM estimated richness considered as maximum reference value. Different letters refer to a significant difference (P < 0.05) between modalities according to non-parametric rank-based Friedman test (a) and Wilcoxon's signed-rank test (b).

<figure><img src="image_47.png" /></figure>

**Figure 3.3:** Mean values (± SE) of sampling coverage among insects' species observed under classical (dark bars) and doubled (light bars) EIA conditions, 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). 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).