From long-term trends monitoring of bats, to their habitat preferences

Transcription

1 From long-term trends monitoring of bats, to their habitat preferences First results analysis and prospects of bat section of the Vigie Nature biodiversity program Christian Kerbiriou, Jean François Julien, Alexandre Robert, Nicolas Deguines & Amandine Gasc Muséum National d Histoire Naturelle

2 Surveying abundance, what for? A 40% decrease of the distribution area corresponds to a decrease of 65% to 95% of the population size. Warren M Presence-absence data Emberiza citrinella Abundance data Altogether Vigie-Nature project aims to detect trends in abundance variation, Why abundance? Because important change in population size can occur long before any conspicuous change in distribution. Here s an example for a birds species in U.K. There is no striking difference between the two maps while abundance drastically decrease over the same period. Much more information in abundance data!

3 What are the «common and detectable» bat species? They will emerge from the results of our sampling! Traditionnal monitorings and Vigie Nature monitoring are complementary: Rare or less abundant species: Nurseries and hibernacula counting Often highly gregarious and localised bats. Most bat monitoring programs focus on roost survey of rare species. However common bats often have less conspicuous roosting habits. But most of them are readily detectable by acoustic methods. So our protocol as other is Rhinolophus, Miniopterus, many Myotis (myotis, blythi, cappacini based ) on acoustic detection «Common» bats: Acoustic detection. Smaller colonies, more evenly distributed species. A PRIORI : Pipistrellus, Eptesicus, Nyctalus, some Myotis??

4 Protocol based on detection and recording of time expanded calls 1. Road survey 2. Point count Involvement of a network of volunteers, each area surveyed twice a year 1rst : 15 June 15 July 2nd : 15 August 30 September 2 different versions are used. First: road survey, by car. Second: count point by foot. Both twice are done a year. Most of fieldwork is done by volunteers. 75% of detector and recorders are temporary transfer by our lab.

5 Random starting point. Ten times two km segments driven at 25 km/h. For the road survey: first the volunteers choose a route a random starting point is defined by the lab. Recording are made on two kilometers segments driven at 25 kilometers per hour.

6 Road survey specificities. To cope with the car speed, the acquisition times was reduced to : 0,3 secondes Replay: 3 secondes Thus, ~ 25m of «deafness» when driving at 30 km/h. Roughly equivalent to the detection range of common pipistrelle. Acquisition time is reduced to 0.3s to cope with the car speed

7 Random drawing of a 2km 2 square 5 points accounting for the different habitats of the square. 5 points chosen in the most favourable habitats. The count point protocol. In a square chose randomly, 10 points are chosen. Five are representative of the habitats of the square. Five are located in a priori favorable places such as river banks, ecotone etc 6 minutes recordings

8 Pedestrian protocole specificities. A longer acquisition time : 1,2 secondes Replay: 12 secondes Allows an easier identification of bat call sequences. Acquisition time is longer 1.2s to make identifications easier

9 Abundance variation from the first to the tenth segment for P. pipistrellus. 30 minutes after sunset. 1h30 hours later. Survey is short enough to avoid any period of decreasing activity

10 Count point look more efficient than car Abundance transect By foot By car

11 Abundance Maybe because of the effect of a priori favorable points. Chosen point Representative point

12 Main habitats representativity: in France Buissons Eau Forêt Habitation Milieux agricoles Pelouses in the monitored areas The overall representativity of our survey areas is not so biased, except for an increase in urban area compared to the whole country Woodland Buissons Young Eau woodland Bushes Forêt Water Jeune forêt Constructed Habitation area Farmlands Milieux agricoles Lawns Pelouses Rocks Rochers

13 The 2006 network. 24 by car ; 6 by foot. The 2007 network 79 by car ; 12 by foot. The 2008 network +140 by car ; +40 by foot

14 French network operating mode. National Museum of Natural History, Paris. The network work in the following way: volunteers collect recording in the field. As true expert, they identify calls, enter data send them to the Museum. There identification are checked and sent back to the volunteers.

15 Fieldwork feedback et volunteers training. 1) Training sessions (March 2007, February 2008, Mars 2008) Volunteers become expert through training sessions,

16 Fieldwork feedback et volunteers training. 1) Training sessions (March 2007, February 2008, Mars 2008) 2) le miniguide (printed booklet 2006, online edition 2008) Through documentation released by the lab

17 Fieldwork feedback et volunteers training. 1) Training sessions (March 2007, February 2008, Mars 2008) 2) le miniguide (printed booklet 2006, online edition 2008) 3) Web site Through webside Through documentation released by the lab

18 Fieldwork feedback et volunteers training. 1) Training sessions (March 2007, February 2008, Mars 2008) 2) le miniguide (printed booklet 2006, online edition 2008) 3) Web site 4) self training with reviewed identification forms. Through some kind of self training insured by the corrected identification forms we send back

19 Effect of training session on identification errors 0.5 avant Before stage après Afterstage 0.4 Taux d'erreur Error rate Nyctalus leisleri Eptesicus serotinus PipistrellusNathusii/Kulhi P.pipistrellus

20 First results As expected, the Vigie Nature monitoring program is a valid complement of the ongoing traditional monitoring programs, usually grounded on roost counts and focused on rare species such as these of the Habitat Annexe II. 1 Pipistrellus pipistrellus 2 Pipistrellus nathusii/kuhli 3 Nyctalus leisleri 4 Eptesicus serotinus 5 Nyctalus noctula 6 Myotis sp 7 Myotis daubentoni 8 Barbastella barbastellus 9 Pipistrellus pygmaeus 10 Plecotus sp. 11 Hypsugo savi 12 Miniopterus schreibersi 13 Myotis nattereri 14 Rhinolophus hipposideros 15 Rhinolophus ferrumequinum Now we know what are the common species, those we will be able to monitor. Pipistrellus pipistrellus is the first to emerge of course.

21 Species From a preliminary 2007 report drawn from 81 (of a total of 91) surveys : total: bat passes calls Pipistrellus pipistrellus Pipistrellus nathusius/kulhi Nyctalus leisleri Eptesicus serotinus Nyctalus noctula Myotis sp. Myotis daubentonii Barbastella barbastellus Pipistrellus pygmaeus Plecotus sp. Hypsugo savi Miniopterus schreibersi Rhinolophus hipposideros Myotis nattereri Rhinolophus ferrumequinum

22 1. Long term monitoring of abundance variations Right now, the dataset spans two years only Thus, it is difficult to check for global change effects, such as climate warming, agricultural intensification or urbanization.,. However, we can estimate the monitoring power. Accounting for both abundance and data noise, are we able to detect a few percent population change? We rely on computer simulation

23 Abundance 1. Trend modelisation Year

24 Abundance 2. Accounting for variations such as inter-transects and inter-segments differences. Year

25 Abundance 3. Accounting for identification errors. Is trend yet detectable? Year

26 P. Pipistrellus case, for a mean abundance of 3.8 individual/segment or count point (data from the July survey only) Number of surveyed years - 5% (sans eff. obs.) - 5% (avec eff. obs.) - 2% (sans eff. obs.) - 2% (avec eff. obs.) - 1% (sans eff. obs.) - 1% (avec eff. obs.) Number of monitored roads

27 By the year 2010 we will thus able to detect a 5%/year variation for P.pipistrellus Number of surveyed years - 5% (sans eff. obs.) - 5% (avec eff. obs.) - 2% (sans eff. obs.) - 2% (avec eff. obs.) - 1% (sans eff. obs.) - 1% (avec eff. obs.) Number of monitored roads

28 Temperature influence on bat activity Année 2007 Noctule de Leisler Pipistrelle commune Abundance s(temp) s(temp) temp Temperature As expected, the temperature seem influence some bat activity but in a species specific way. This bias could be modelled and include in statistical analysis Temperature temp This bias could be modelled and then included in statistical analysis!

29 2. Inter annual variations Right now, the dataset spans two years only. However, the two years surveyed are meteorologically contrasted. How is it reflected in our data?

30 July 2006 : A warm summer September 3 C above average

31 July 2007 : A cold summer September 2 C below average

32 Abundance variation of P. pipistrellus Pipistrelle from 2006 to Decline? Data from 20 car transects (405 validated segments) surveyed twice a year in 2006 and Indeed in our data set we observed a decrease in bat call count, is it the beginning of a decline?

33 When focus on July survey, the lactating period, we don t see any difference whereas the September survey are very contrasted. September survey July survey

34 3. Spatial abundance variation and habitat preferences Furthermore, why not use data collected for the longterm trends monitoring of common bats, to study their spatial variation of abundance and their habitat preferences?

35 Map abundance in the Gatinais Regional Park Nyctalus leisleri Pipistrellus pipistrellus kilometers

36 From local scale to national scale Town, Reserve, Park Province, Region 20 Km State 50 Km For some species we detected important spatial heterogeneity and the heterogeneity could be detected at different scale 500 Km

37 Habitat variable GIS data extracted from existing data base, such CorineLand Cover, IAURIF Spatial heterogeneity reflected of course in great part habitat preference. We explore this aspect using existing data base

38 Area (m²) 7 6 Abundance (indv.) P. pipistrellus E. serotinus N. leisleri N. noctula Circuit S (***) S (***) S (***) S (***) Crops Labour S (***) S (*) NS Wood Bois S (***) S (*) S (**) Urban Urbain Garden Jardins S (***) S (**) S (*) Wood Clairière clearing S (***) S (***) Pasture Pâture S (***) S (*) Water Eau S (***) First analysis show for example that P. pistrellus and N. leisleri are less abundant in areas dominated by crops, whereas garden S influence : significant them positively correlation ; positive correlation ; negative correlation

39 Owing to the lack of precision of available data set, our volunteers were involved in collecting habitat on a more detailed and adapted basis. Due to lack of precision of available data set our volunteers were involved in collecting habitat variable on a more detailed and adapted basis 1ère subdivision 2ème subdivision 3ème subdivision 4ème subdivision 5ème subdivision 6ème subdivision A B - 1 Présence d'arbre de A - Forêt A B C - 1 Feuillus exclusifs A B - 1 Clairière et lisière A B C D F - 1 Pâturé Chêne rouvre ou sessile A B C D - 2 beacoup de fougère diam. A B - 2 sup. Absence à 50 cm d'arbre de (arbres > A B C - 2 Mixte dom. Feuillus A B - 2 Voie forestière fermée Chêne vert aigle diam. sup.à 50 cm 10m de hauteur) A B C - 3 Mixte dom. conifères A B - 3 Voie forestière ouverte A B - 3 Sous-bois dense Hêtre A B C - 4 Conifères exclusifs A B - 4 En plein sous bois (+50m d'une lisière) A B - 4 Sous-bois clair Erable A B - 5 Peupleraie A B - 5 Ripisylve Châtaigner A B - 6 Parc (arbres épars et prairies) Frêne Autre essence feuillus Sapin Epicéa Pin Mélèze Autre essence conifère A B - 1 Présence d'arbre de B - Jeune forêt A B C - 1 Feuillus exclusifs A B - 1 Clairière et lisière A B C D F - 1 Pâturé Chêne rouvre ou sessile A B C D - 2 beacoup de fougère diam. A B - 2 sup. Absence à 50 cm d'arbre de ( 5 > arbres A B C - 2 Mixte dom. Feuillus A B - 2 Voie forestière fermée Chêne vert aigle diam. sup.à 50 cm < 10m A B C - 3 Mixte dom. conifères A B - 3 Voie forestière ouverte A B - 3 Sous-bois dense Hêtre de hauteur) A B C - 4 Conifères exclusifs A B - 4 En plein sous bois (+50m d'une lisière) A B - 4 Sous-bois clair Erable A B - 5 Peupleraie A B - 5 Ripisylve Châtaigner A B - 6 Parc (arbres épars et prairies) Frêne Autre essence feuillus Sapin Epicéa Pin Mélèze Autre essence conifère C - Buissons A B C - 1 Feuillus exclusifs C - 1 Forêt de régénération ou taillis A B C D F - 1 Pâturé Surtout grand 3-5 mètres Chêne rouvre ou sessile A B C D - 2 beacoup de fougère (< 5m D - A Pelouses B C - 2 Mixte dom. Feuillus D - 1 Pelouse C - sèche 2 Buissons calcicoles D F - 1 Haies aigle avec arbres Surtout petit 1-3 A mètres B C D F - 1 Chêne Pâturé vert de hauteur) & landes A B C - 3 Mixte dom. conifères D - 2 Lande C herbacée - 3 Buissons de zones humides D F - 2 Haies de buissons A B C D - 2 beacoup Hêtre de fougère aigle A B C - 4 Conifères exclusifs D - 3 Lande C à - bruyères 4 Lande arbustive non calcicoled F - 3 Lligne d arbres sans haie D F - 3 Fauché Erable C - 5 Friche D - 4 Autres C pelouses - 5 Garrigue sèches et maquis (Méditerrannée) D - 4 Avec buissons épars D G - 1 Végétation Châtaigner herbacée haute D - 5 Pelouse d'altitude D - 5 Sans buissons épars D G - 2 Végétation herbacée rase Frêne D - 6 Pelouse humide naturelle D - 6 Groupes isolés de 1-10 arbres D - 3 Lande rase à moyenne sèche D - 7 Pelouse inondée/marais pâturé D - 8 Tourbière D - 9 Roselière D - 10 Mégaphorbiais D - 11 Marais salant (halophile) D - 12 Dunes D - 7 Autres limites de terrain (digue, mur,...) Saule D - 4 Lande rase à moyenne humide Noisetier Prunellier Autre essence feuillus Sapin Epicéa E - Eau et milieu E - 1 Mare (moins de 50 m2) E - 2 Petit étang ( m2) E - 1 Rives nues E - 2 Rives bordée de buissons Pin E - 1 Eutrophique (eau verte) E - 2 Oligotrophique Mélèze (eau claire, peu d algues) Autre essence conifère aquatique E - 3 Lac/réservoir (berges naturelles) E - 3 Rives bordée d'arbres E - 3 Dystrophique (eau noire) E - 4 Réservoir (berges non naturelles) E - 4 Rivière sous voûtes d'arbres E - 4 Bigarré(eau claire, beaucoup d algues) E - 5 Carrière de gravier, de sable... E - 5 Petites îles E - 5 Courant faible / moyen E - 6 Fossé inondé (< 2m de largeur) E - 6 rive bordée de végétation herbacées moyenne à haute E - 6 Courant fort E - 7 Eaux saumâtres (salins, lagunes ) E - 8 Petit canal (2-5m. de largeur) E - 9 Grand canal (> 5m de largeur) E - 10 Ruisseau (< 3m de largeur) E - 11 Rivière (3m < largeur < 10m) E - 12 Fleuve / rivière large (> 10m) F - Milieux F - 1 Prairie cultivée (type Ray Grass) D F - 1 Haies avec arbres A B C D F - 1 Pâturé agricoles F - 2 Prairie non cultivée D F - 2 Haies de buissons F - 2 Céréales F - 3 Prairie et cultures D F - 3 Lligne d arbres sans haie D F - 3 Fauché F - 4 Grandes cultures F - 4 Entouré de bandes herbeuses F - 4 Arbres fruitiers F - 5 Vergers / vignes / cultures maraîchères F - 5 Avec des groupes isolés de 1-10 arbres? F - 5 Maïs F - 7 Bocage F - 6 Autre limite de terrain (digue, fossé, mur,...) F - 6 Tournesol F - 6 Autres types de cultures F - 7 Sans limite de terrain (les cultures se touchent) F - 7 Betterave industrielle F - 8 Maraichage F - 9 Colza F - 10 Vignes F - 11 Serres F - 12 Autres cultures F - 13 Non exploité (jachère agricole) F - 14 Non exploité (jachère apicole) 1ère subdivision 2ème subdivision 3ème subdivision 4ème subdivision G - Rochers G - 1 Falaise G - 1 En montagne D G - 1 Végétation herbacée haute terrestres G - 2 Eboulis, pente rocheuse G - 2 En bord de mer D G - 2 Végétation herbacée rase ou côtiers G - 3 Carrière G - 3 En bord de lac ou de rivière G - 4 Autres sols rocheux G - 4 Autre H - Habitation, H - 1 Zone urbaine résidentielle H - 1 Zones bâties prédominantes H - 1 Avec des haies de feuillus milieux H - 2 Zone urbaine industrielle H - 2 Habitations individuelles avec jardins H - 2 Avec des haies de conifères urbanisés H - 3 Zone suburbaine résidentielle H - 3 Parc public, zone de loisirs, espace vert H - 3 Sans haie H - 4 Zone suburbaine industrelle H - 4 Décharge d ordures H - 4 Mixte H - 5 Zone rurale H - 5 Friche urbaine H - 5 présence de buissons éparses H - 6 présence d'arbres isolés Saule

40 Suburban dwelling area Forest - Park Urban dwelling area Water, still. Rural dwelling area Farmlands, natural meadow Farmlands, meadow and crops Farmland, others Forest, deciduous only Water, streams. Wet grassland, grazed marsh. Mixed forest, mainly deciduous Rocks, cliff, carry Lawns Forest coniferous only Farmland intensive crops. Farmland cultivated grassland In our database, 1400 count Suburban point abd dwelling road area segment habitat data Young are forest available Bushes The 23 main habitats 1400 habitats sites description : 0 0,5 1 1,5 2 2,5

41 Mean abundance of Common Pipistrelles in different habitats: Suburban dwelling area Forest - Park Urban dwelling area Water, still. Rural dwelling area Farmlands, natural meadow Farmlands, meadow and crops Farmland, others Forest, deciduous only Water, streams. Wet grassland, grazed marsh. Mixed forest, mainly deciduous Rocks, cliff, carry Lawns Forest coniferous only Farmland intensive crops. Farmland cultivated grassland Here for example the distribution of Suburban abundance dwelling of common area pipistrelle among Young the main forest habitat class. Furthermore, Bushes using the habitat coefficient of variation we can attribute to each species a specialization habitat index Mean Specialization index = 0,35 0 0,5 1 1,5 2 2,5

42 Mean abundance of Kuhl + Nathusius Pipistrelles in different habitats: Forest coniferous only Farmlands, natural meadow Urban dwelling area Bushes Rural dwelling area Forest - Park Farmland cultivated grassland Lawns Rocks, cliff, carry Water, streams. Suburban dwelling area Forest, deciduous only Young forest Farmlands, meadow and crops Farmland intensive crops. Wet grassland, grazed marsh. Mixed forest, mainly deciduous Mixed forest, mainly coniferous Farmland, others Water, still. Suburban, dwelling and industrial. Mean Warning: underrepresented habitat in our sampling. Specialization index = 0,96 0 0,2 0,4 0,6 0,8 1 1,2 1,4

43 Mean abundance of Nyctalus leisleri in different habitats: Mixed forest, mainly coniferous Forest - Park Suburban dwelling area Farmlands, natural meadow Urban dwelling area Mixed forest, mainly deciduous Forest, deciduous only Rural dwelling area Wet grassland, grazed marsh. Lawns Young forest Farmlands, meadow and crops Farmland intensive crops. Water, streams. Forest coniferous only Water, still Suburban dwelling & industrial area Rocks, cliff, carry Farmland, others Farmland cultivated grassland Bushes Mean Warning: underrepresented habitat in our sampling. Specialization index = 1,03 0 0,05 0,1 0,15 0,2 0,25 0,3

44 Mean abundance of Eptesicus serotinus in different habitats: Urban dwelling area Rural dwelling area Forest - Park Water, streams. Farmlands, meadow and crops Mixed forest, mainly deciduous Suburban dwelling area Farmland cultivated grassland Forest, deciduous only Forest coniferous only Suburban, dwelling and industrial. Wet grassland, grazed marsh. Farmlands, natural meadow Farmland intensive crops. Mean Young forest Water, still. Lawns Mixed forest, mainly coniferous Rocks, cliff, carry Farmland, others Bushes Indice de spécialisation = ,1 0,2 0,3 0,4 0,5 0,6 0,7

45 Mean abundance of Nyctalus noctula in different habitats: Urban dwelling area Rural dwelling area Forest - Park Water, streams. Farmlands, meadow and crops Mixed forest, mainly deciduous Suburban dwelling area Farmland cultivated grassland Forest, deciduous only Forest coniferous only Suburban, dwelling and industrial. Wet grassland, grazed marsh. Farmlands, natural meadow Farmland intensive crops. Young forest Water, still. Lawns Mixed forest, mainly coniferous Rocks, cliff, carry Farmland, others Bushes Mean Specialization index = 1,49 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8

46 Mean abundance of Barbastella barbastellus in different habitats: Mixed forest, mainly deciduous Forest, deciduous only Farmlands, natural meadow Farmland intensive crops. Farmlands, meadow and crops Rocks, cliff, carry Wet grassland, grazed marsh. Lawns Farmland artificial meadow Farmland, others Young forest Urban dwelling area Suburban dwelling area Mean Suburban, dwelling and industrial. Rural dwelling area Forest - Park Mixed forest, mainly coniferous Forest coniferous only Water, streams. Water, still. Bushes Specialization index = 2,36 0 0,005 0,01 0,015 0,02 0,025

47 1,8 1,6 Crop fields surrounded by.. 1,4 1,2 1 0,8 0,6 0,4 0,2 0 Hedge with threes Entouré de haies avec arbres Hedge with bush Entouré de haies de buissons Without hedge Entouré de bandes herbeuses Looking at some micro habitat features such as hedge composition, we can also detected some bat preference

48 Why not a Pan-European Common Bat Monitoring? Our experience with the bird networks

49 Indeed, BirdWatchers are many...much many.

50 Point count in square..very numerous. but also a great diversity of methodology, which could be an obstacle to gather data Road survey Territory mapping

51 ..Nevertheless they produce indicators such as common bird trend or abundance maps. at European scale!!!

52 Why not a Pan-European Common Bat Monitoring? Transects done by car with Time Expansion x 10 acquisition time: 0.32 s 2 surveys/year. filing of acoustic recordings. Knowing that already an Irish program started in 2003 with a very similar protocol

53 Why not a Pan-European Common Bat Monitoring? 2005 Transects done by car with Time Expansion x 10 acquisition time: 0.32 s 2 surveys/year. filing of acoustic recordings. A british program in 2005

54 Why not a Pan-European Common Bat Monitoring? 2006 Transects done by car with Time Expansion x 10 acquisition time: most often 0.32 s 2 surveys/year. filing of acoustic recordings. The french team in 2006

55 Why not a Pan-European Common Bat Monitoring? 2007 Transects done by car with Time Expansion x 10 acquisition time: most often 0.32 s 2 surveys/year. filing of acoustic recordings. Ibats team And so far away from here, Croatia, Bulgaria, Moldavia, Romania started their national program last year in the ibats framework

56 Why not a Pan-European Common Bat Monitoring? 2008 Other protocols tested in other countries Netherlands Belgium Switzerland Furthermore, other monitors are tested

57 Why not a Pan-European Common Bat Monitoring? Europe

58 Thank to the French volunteers, Thank for your attention,