EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 20 (2013) Parasite, 20 (2013) 34 Full HTML
Open Access
Issue
Parasite
Volume 20, 2013
Article Number 34
Number of page(s) 10
DOI https://doi.org/10.1051/parasite/2013035
Published online 11 October 2013

© J.-A. Rioux et al., published by EDP Sciences, 2013

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction

For several decades, the epidemiology of leishmaniasis has benefited from ecology-based scientific concepts and methods. The aim of this approach was to investigate epidemiological cycles in terms of “parasitic systems”. The ecoepidemiological method developed on this occasion [11, 23] enabled identification of key factors that govern the structure and function of leishmaniasis outbreaks. The disciplines involved in this approach include: 1° mesology (biotopes, biogeography, bioclimatology, predators and pathogens), 2° taxonomy, genetics and evolution, 3° trophic and sexual behaviours of sandflies (hematophagy, trophogonic cycle, “eurystenogamy”, fertility, fecundity and diapause). 4° morphophysiological modifications in parasites during the intravectorial cycle (multiplication, fusion, parietal attachment, metacyclogenesis), 5° Leishmania virulence in vertebrate reservoirs (receptivity, inoculation chancre, visceralization and immunity), 6° the last step of the approach, rational control, involves several techniques (physical, chemical and biological) targeting the cycle overall, i.e. the parasite, vectors and reservoirs.

This ecoepidemiological approach was applied in the Mediterranean region, which gave rise to the vector pre-eminence concept, i.e. the sandfly vector is the main factor responsible for the structure and dynamics of leishmaniasis outbreaks [23, 28].

Global warming was recently taken into account with respect to the emergence and expansion of these outbreaks, thus giving new impetus to this type of research [5, 14, 16, 26]. However, to clearly confirm the involvement of climatic factors, project leaders have stressed the need to very precisely determine the conventional zero point. In Languedoc-Roussillon, where mean annual temperatures increased by around 2 °C between 1946 and 2004 (Figure 1), these recommendations prompted us to reassess the statistical quality of the trapping initiatives conducted in 1981 in the eastern Pyrenees region where both Phlebotomus ariasi and P. perniciosus, i.e. sandfly vectors of Leishmania infantum, are found. The aim of the present study was to detect potential sampling bias, which could call into question the conclusions of certain previous studies [22].

thumbnail Figure 1.

Variations in mean monthly temperatures at the Montpellier-Fréjorgues (France) meteorological station between 1946 and 2004 (blue triangle: 1981, year of the study). The data have a normal distribution and the regression is significant (trend curve: Y = 0.0024, X + 13.511). The temperature increase over the considered period was 1.895 ± 0.068 °C.

Material and methods

General points

The survey was carried out at Vallespir, in the vicinity of Céret (P.O., France), in an area of mixed oak, including Quercus ilex, Q. pubescens and Q. suber (Figure 2), which is typical subhumid Mediterranean climate [4, 8, 9, 13, 20].

thumbnail Figure 2.

Map of botanical successions around Perpignan (in: H. Gaussen : Carte de la végétation de la France au 1 / 200 000e, CNRS). In orange: cork oak. In yellow: holm oak. In green: white oak. In light blue: beech. In dark blue: fir. In purple: Scots pine. In red: mountain pine. In pink: alpine storey. Sampling area, , located in the Mediterranean holm oak and white oak succession. There were patches of cork oak.

In the study area (mean elevation 200 m), 12 sampling stations located 2–3 km apart were selected using a purposive sampling method [12]. All stations except No. 1, 8 and 10 were located in wooded rural or periurban areas. Each had a retaining wall with drainage cavities (weep holes). Sandfly samples were obtained using flight interception traps (20 × 20 cm sticky paper that were stretched vertically in the weep holes) (Figure 3). From 3 June to 12 November 1981, the 12 stations were sampled on a 10-day basis from March to November (total number of traps: 4,263; total area: 341.04 m2; mean number of traps per station: 355.25; mean area per station: 28.42 m2; mean number of traps per 10-day period: 284). Sampled sandflies were placed in a 90° alcohol solution and identified. For each species, imago densities were calculated on the basis of the number of sandflies (P♂ + ♀) counted per m2 of sticky paper [2, 23, 27, 32]. The statistical analysis was focused on P. ariasi and P. perniciosus, the only confirmed vectors sampled at the site, using Kolmogorof-Smirnov, Wilcoxon and Friedman, Cochran and McNemar tests (SYSTAT®9 software) [33, 35].

thumbnail Figure 3.

Station No. 3. Trapping wall with weep holes (or so-called “barbacanes” in French). Inset: a weep hole containing a sticky trap. Forefront: Brachypodium phoenicoides meadow along the edge of a road. Background: Robinia pseudacacia and Quercus ilex.

List of sampling stations and coordinates

Station No. 1 – Le Vila-locality. GPS coordinates (Coord.): latitude North (N) 42° 29,912′, Longitude east (E) 02° 42,651′. Elevation (Elev.): 150 m. Wall exposure (exp.): east southeast. Forest vegetation (For. veg.): Quercus ilex (Qi), Q. suber (Qs). Number of sampled traps (Tot. traps): 489.

Station No. 2 – Road (R) D15. Coord.: N 42° 29,901′, E 02° 42,528′. Elev.: 140 m. Exp.: E. For. veg.: Qi, Qs; Tot. traps: 73.

Station No. 3 – R D618 (Le Vila at Palalda). Coord.: N 42° 29,441′, E 02° 41,020′. Elev.: 280 m. Exp.: S. For. veg.: Qi, Qs. Tot. traps: 386. (Figure 3).

Station No. 4 – R D618 (Palalda 2 km). Coord.: N 42° 29,290′, E 02° 40,622′. Elev.: 260 m. Exp.: S. For. veg.: Qi. Tot. traps: 254.

Station No. 5 – Palalda-locality. Coord.: N 42° 29,087′, E 02° 40,437′. Elev.: 220 m. Exp.: SSE. For. veg.: Qi. Tot. traps: 332.

Station No. 6 – Palalda-locality. Coord.: N 420 29,067′, E 02° 40,315′. Elev.: 200 m. Exp.: SE. Tot. traps: 678.

Station No. 7 – R D618 (Palalda-Amélie). Coord.: N 42° 28,920′, E 02 40,203′. Elev.: 180 m. Exp.: SE. For. veg.: Qi, Q. pubescens (Qp). Tot. traps: 543.

Station No. 8 – Arles-localité. Coord.: N 42° 27,746′, E 02° 38,294′. Elev.: 200 m. Exp.: SE. Tot. traps: 712.

Station No. 9 – R F13 (Céret-locality). Coord.: N 42° 29,322′, E 02° 44,705′. Elev.: 120 m. Exp.: NE. Tot. traps: 203.

Station No. 10 – Céret-locality. Coord.: N 42° 29,430′, E 02° 44,800′. Elev.: 120 m. Exp.: E. Tot. traps: 305.

Station No. 11 – R D63 (Le Vila-Taillet). Coord.: N 42° 30,194′, E 02° 42,487′. Elev.: 235 m. Exp.: S. For. veg.: Qs, Qp, Qi. Tot. traps: 144.

Station No. 12 – R D63 (Le Vila-Taillet). Coord.: N 42° 30,182′, E 02° 42′,506′. Elev.: 240 m. Exp.: S. For. veg.: Qs, Qi, Qp. Tot. traps: 144.

Corine biotope habitats

A 2,000 m2 area was delineated around each sampling wall to integrate the different natural or manmade habitats that could have an impact on sandfly abundance. These habitats (including walls and access roads) were classified according to Corine Biotope codes [1, 36]. Most of them corresponded to synsystematic units (phytosociological classification of J. Braun-Blanquet). For each station, the cover of each habitat was expressed as a percentage occupation within the delineated areas. The weep-hole retaining walls and access roads accounted for 18% of the overall area.

Habitat/station/sandfly relationships were described by normalized principal component analysis (PCA) [7, 34] on a matrix designed to correlate 12 sampling station records with 19 Corinne Biotope habitats (Table 1, Figures 9 and 10). In order to determine potential links between vegetation complexes and vector productivity, total sandfly densities were projected on the first PCA axis which pooled all of the main floristic characteristics in the records (ADE®4 software).

Table 1.

Frequency (% cover per 2,000 m2) of Corine Biotope habitats in the 12 sampling stations (including trapping walls and access roads). These stations were highly modified by human activities, but holm oaks were still well represented in most of them (20–40%).

Results and Discussion

  1. At the end of the survey, 8,280 sandflies (P) were caught in a total of 4,263 traps (Table 2). When calculated in terms of m2 of sticky paper (both sides), the annual cumulative frequency of P. ariasi, P. perniciosus and Sergentomyia minuta was 24.27 P/m2. S. minuta was more abundant (5,600 specimens: 16.42 P/m2), followed by P. ariasi (2,297 specimens: 6.73 P/m2), and P. perniciosus (383 specimens: 1.12 P/m2). Stations with the highest abundance of P. ariasi (>8 P/m2, No. 3, 4, 11, 12) were south-facing. For S. minuta, stations 4 and 11 also had the highest abundances (44.83 P/m2 and 257.37 P/m2, respectively).

    Table 2.

    Annual frequencies of the three sandfly species (Phlebotomus ariasi, P. perniciosus, Sergentomyia minuta) caught at the 12 selected sampling stations (“rational choice” method). There were considerable differences in sampled sandfly abundances between these stations (1.36–268.57 P/m2).

  2. The cumulative monthly total for the 12 stations was used to plot annual activity curves for both vectors. At Vallespir, P. ariasi thus had a bimodal distribution pattern, with the second peak in early autumn, whereas this species had a unimodal distribution in the Cévennes region (Figure 4). This pattern was likely due to bioclimatic differences, as indicated by the presence of cork oak, since the Pyrénées-Orientales stations had much milder climatic conditions than the southern Cévennes. These differences could be explained by the fact that P. ariasi thrives in humid-subhumid climatic conditions [23]. A similar phenomenon was observed with P. perniciosus, which is the most thermophilic sandfly species. At Vallespir, as in the Cévennes, this species had a unimodal distribution, while it has a bimodal pattern in the vicinity of Tunis, where semiarid climatic conditions prevail (Figure 5) [3]. However, there could have been a temporal shift in the maximum activity peaks between stations. At station No. 12, the peak for P. ariasi was thus around 20 June, whereas it occurred around 20 July at station No. 4 (Figure 6). Excluding the fact that the corresponding walls had identical exposure, this shift could have been due to other factors, such as the extent of solar radiation or the presence of vertebrate hosts.

    thumbnail Figure 4.

    Monthly frequency distribution of Phlebotomus ariasi ♂ + ♀ (sampling with sticky-paper traps): in the Cévennes region in 1960 and in Pyrénées-Orientales region in 1981. The subhumid bioclimatic conditions in the Pyrénées-Orientales could explain the bimodal distribution pattern and the autumn frequency spread.
    thumbnail Figure 5.

    Monthly frequency dynamics of Phlebotomus perniciosus in Pyrénées-Orientales region (1981) and in Tunisia (1960). In the vicinity of Tunis, under a semiarid Mediterranean bioclimate, these frequencies had a biphasic distribution pattern (summer diapause?). At Vallespir, in subhumid bioclimatic conditions, a unimodal distribution was noted.
    thumbnail Figure 6.

    10-day frequency distribution for Phlebotomus ariasi at the 12 sampling stations. The stations with the highest abundance (No. 4 and No. 12) showed a marked shift in maximum peak abundance: in July-June for No. 14 and July for No. 4. There was a similar shift in the lower second peak. This shift was likely related to the characteristics of the two trapping walls.

  3. P. ariasi was clearly the most abundant of the two vectors present at the site: for 4,263 sticky paper traps (surface area 341.04 m2), a total of 2,297 specimens (6.73 P/m2) were sampled, as compared to only 383 for P. perniciosus (1.12 P/m2). Moreover, there were substantial between-station differences in densities of both species. At stations No. 4 and No. 12, for P. ariasi, a total of 51.62 P/m2 and 39.84 P/m2, respectively, were sampled, whereas at the 10 other sites, the frequencies never exceeded 13 P/m2. For P. perniciosus, the two stations with the greatest densities (No. 4, No. 11) had a total of 9.25 P/m2 and 2.25 P/m2, respectively, whereas the frequencies at the remaining stations ranged from 0.08 P/m2 (No. 8) and 1.36 P/m2 (No. 2) (Table 2).

  4. The statistical analyses confirmed these findings. The comparison of vector densities revealed significant differences between the 12 stations, i.e. each had a specific sandfly abundance pattern with respect to both P. ariasi (Figure 7) and P. perniciosus.

    thumbnail Figure 7.

    Phlebotomus ariasi: mean annual density per station (P ♂ + ♀ / m2). These frequencies did not have a normal distribution (Kolmogorov-Smirnov test, p < 0.01). The Friedman test, p < 0.001 (nonparametric, matched by date), showed that the frequencies were significantly different between the 12 stations, i.e. each station had its own specific sandfly density.

  5. Moreover, although the sandfly densities were specific to each station, this was not the case for their variations. Between samplings (10-day sampling periods), the variation trends were not significantly different: the curves were parallel and in the same direction for the 12 stations (Figure 8).

    thumbnail Figure 8.

    10-day variations in Phlebotomus ariasi densities-sampling results for stations No. 3 (red) and No. 11 (blue). The 10-day variation patterns did not significantly differ between stations (Cochran’s Q test, p = 0.949, nonparametric, matched by date).

  6. Projection of the vector abundances on the first PCA axis did not reveal any relationship between the sampled sandfly abundance of the trapping walls and their phytological environment, expressed in terms of the cover of the different Corine Biotope habitats (Figures 9, 10).

    thumbnail Figure 9.

    Circle of correlation from a normalized PCA based on the data in Table 1 (19 Corine Biotope codes × 12 sampling station records). These correlations led to the identification of the Corine Biotope habitats most frequently associated in each of the 12 records. The F1 axis thus compares records containing wild Robinia stands (83.324), western Mediterranean riparian forests (44.5) and brambles (31.831) with those containing Osyris alba brush (32.216), tree rows (84.1), and holm oak thickets (45.3) and vegetation cover on siliceous rocks (62.2). The F2 axis compares records that associate orchards (83.15) and vineyards (83) with those that do not. The method used to differentiate the stations according to their phytoecological profile … and nothing else!
    thumbnail Figure 10.

    Projection of Phlebotomus ariasi (a), P. perniciosus (b) and Sergentomyia minuta (c) densities at sampling stations on the normalized PCA F1 axis. Among the readings grouped at the origin on the F1 axis, there are two habitat groups that are opposed in Figure 9 (circle of correlations). There is therefore no gradual relationship between the vector abundance and the environmental (habitat) trends. Corine Biotope habitats thus do not seem to be suitable for drawing up a vector sampling plan.

Conclusion and prospects

The present results led to the following conclusions:

  • At the study site, the per-station P. ariasi abundances were significantly higher than those of P. perniciosus. However, this species was not as scarce as it was in the southern Cévennes region. This difference could be explained by the bioclimatic conditions, i.e. humid Mediterranean climate in the Cévennes and subhumid at Vallespir. These climatic differences could also explain the P. ariasi variation patterns, i.e. monophasic in the Cévennes and diphasic at Vallespir.

  • The significant between-station differences in sampled sandfly abundance were due to the specific environmental conditions at each station (geopedology, orientation and shadows cast on the trapping wall, animal occupation of the weep holes, nearby livestock) [18, 28]. Note that the weep holes served as roosting sites, not as breeding sites

  • During many surveys, our attempts to isolate larvae or nymphs in weep-hole soil were always fruitless. However, the constant high abundance sampled in some especially attractive walls could be explained by the high movement capacity of imagos [30].

  • The parallelism in the 10-day variations could have been due to a single factor, i.e. likely weather related (storm, heat wave, etc.), taking place accidentally and sporadically at all stations.

  • The multivariate analysis did not reveal any association between sandfly frequencies and the phytoecological environment of the sampling stations (Figures 9 and 10). As we noted, these associations occurred with certain physical or biotic properties specific to each wall. More generally, the cartographic typology of the area according to the Corine Biotope system or its derivatives (Corine Land Cover, EUNIS [15, 17]) cannot be recommended as a vector abundance indicator, especially in the definition of zero points. By their phytosociological nature, the corresponding maps should be used for studies on landscape modifications resulting from climate change, fire, flooding, cropping or non-native plant invasions. However, these maps have a real value in applied research, e.g. in forest management (see French Forestry Office). Finally, they can still be used as density indicators to characterize certain zoological groups on the condition that they are closely tailored to specific botanical taxa or syntaxa. This category includes strict pests, specific pollinators, specialized herbivores and animals with a narrow ecological niche. In other cases, caution is necessary [21], especially since Corine Biotope maps are not the only way to express vegetation-indicator relationships. This was the case in studies carried out in Morocco to identify the preferred bioclimatic conditions of sandflies. In this country, the only phytoclimatic map, drawn up on the basis of vegetation layers (from humid to arid), made it possible to attribute a bioclimatic value to each inventoried sandfly species, and especially to specify the current or future geographical distribution (global warming) of Leishmania spp. vectors [23]. Otherwise, in metropolitan France, a very successful phytoecological map was drawn up of biotopes for pre-adults of Aedes spp. (Diptera-Culicidae). The detection of eggs in the litter of halophyte plants revealed a close relationship between some plant species (Salicornia spp., Scirpus maritimus) and certain Aedinae species (Aedes caspius, A. detritus). This relationship was dependent on the egg-laying behaviour of gravid females, which were found to often only lay eggs on one or two species of plants growing along narrow strips in lagoon environments. The focus has thus readily shifted from “egg laying sites” to the phytoecological mapping of “breeding sites”, which has turned out to be a remarkable operational tool that has been very successful in the control of pest mosquitoes in Mediterranean coastal regions [10, 24, 25].

  • Finally, the current results should be considered as a starting point for further research. At Vallespir, after more than 30 years, the same protocol should be applied at the same sampling stations. This type of operation was recently undertaken in the lower Cévennes region [19, 31], which generated promising results. At Vallespir, if the sandfly density modifications and geographical distributions noted here are confirmed, it would be of interest to supplement this work by more in-depth taxonomic and genetic analyses, both in terms of specific taxa (P. sergenti is already present in Pyrénées-Orientales region [29]), and within the same species (modification in population-based polymorphism, selection of thermophilic variants, drift [30], etc.). Future epidemiological research focused on the impact of climate change or of agronomic-silvicultural modifications should be very cautiously carried out, especially with respect to vector sampling and the use of phytoclimatic maps as vector density indicators.

Acknowledgments

We warmly thank Anne-Laure Bañuls, Bulent Alten, Nathalie Barras, Jean Cousserans, Henri Descamps, Jean-Charles Gantier, Christian Jean, Nicole Léger, Michele Maroli, Georges Metge, Pierre Quézel, Joseph Travé and Marco Zito.

1

Experience must be allowed its freedom: only showing the side that proves while hiding the side that contradicts is to maintain it captive”. Denis Diderot – Pensées sur l’interprétation de la nature, 1774, § 47 [6].

References

  1. Bissardon M, Guibal L. 1997. Corine biotopes, Version originale. Types d’habitats français. École Nationale du Génie Rural, Nancy. [Google Scholar]
  2. Croset H, Rioux JA, Léger N, Houin R, Cadi-Soussi M, Benmansour N, Maistre M. 1977. Les méthodes d’échantillonnage des populations de Phlébotomes en Région méditerranéenne, Écologie des leishmanioses. Centre National de la Recherche Scientifique, Paris, 239, 139–151. [Google Scholar]
  3. Croset H, Rioux JA, Juminer B, Tour S. 1970. Fluctuations annuelles des populations de Phlebotomus perniciosus Newstead, 1911, Phlebotomus perfiliewi Parrot, 1930 et Sergentomyia minuta parroti (Adler et Theodor, 1927) [Diptera : Psychodidae] en Tunisie du Nord, Archives de l’Institut Pasteur de Tunis, 47, 43–56. [Google Scholar]
  4. Daget P. 1977. Le bioclimat méditerranéen: analyse des formes climatiques par le système d’Emberger. Vegetatio, 34, 87–103. [CrossRef] [Google Scholar]
  5. Dereure J, Vanwambeke S, Malé P, Martinez S, Pratlong F, Balard Y, Dedet JP. 2009. The potential effects of global warming on changes in canine leishmaniasis in a focus outside the classical area of diseases in southern France. Vector-born and zoonotic diseases, 9, 687–694. [Google Scholar]
  6. Diderot D. 1753. Pensées sur l’interprétation de la nature, Paris, 99 p. [Google Scholar]
  7. Dray S, Dufour AB. 2007. The ade4 package: implementing the duality diagram for ecologists. Journal of Statistical Software, 22, 1–20. [Google Scholar]
  8. Emberger L. 1930. Sur l’étage de végétation. Comptes rendus de l’Académie des Sciences, 191, 389–390. [Google Scholar]
  9. Emberger L. 1955. Une classification biogéographique des climats. Recherches et Travaux du Laboratoire de Botanique, Géologie, et Zoologie de la Faculté des Sciences de Montpellier, 7, 3–43. [Google Scholar]
  10. Gabinaud A. 1975. Écologie de deux Aedes halophiles du littoral méditerranéen français, Aedes (Ochlerotatus) caspius (Pallas, 1771), Aedes (Ochlerotatus) detritus (Haliday,1833) (Nematocera Culicidae). Utilisation de la végétation comme indicateur biotique pour l’établissement d’une carte écologique. Application en dynamique des populations, Thèse Sciences, Université des Sciences et techniques (USTL), Montpellier, (enregistrée au Centre National de la Recherche Scientifique A.O.11990), 451 p. [Google Scholar]
  11. Giraudoux P, Raoul F, Pleydell D, Craig PS. 2008. Multidisciplinary studies, systems approaches and parasite eco-epidemiology: something old, something new. Parasite, 15, 469–476. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  12. Gounot M. 1969. Méthodes d’étude quantitative de la végétation. Masson, Paris, 314 p. [Google Scholar]
  13. Gaussen H. 1970. Carte de la végétation de la France. 2ème édition, Centre National de la Recherche Scientifique (CNRS), Toulouse, 75. [Google Scholar]
  14. Hartemink N, Vanwambeke S, Heesterbeek H, Rogers D, Morley D, Pesson B, Davies C, Mahamdallie S, Ready P. 2011. Integrated mapping of establishment risk for emerging vector-borne infections: a case study of canine leishmaniasis in southwest France. PLos One, 6, e20817. [CrossRef] [PubMed] [Google Scholar]
  15. Institut Français de l’Environnement. 2011. L’utilisation de Corine land cover 2000, Institut Français de l’Environnement (IFEN), Orléans, 2007–2011. [Google Scholar]
  16. La Rocque de S, Rioux JA, Slingenbergh J. 2008. Climate change: effects on animal disease systems and implications for surveillance and control. Revue des sciences et des techniques, Office International des Epizooties, 27, 339–354. [Google Scholar]
  17. Louvel J, Gaudillat V, Poncet L. 2013. EUNIS, Système d’information européen sur la nature, Classification des habitats, Traduction française. Muséum National d’Histoire Naturelle: Paris, 289 p. [Google Scholar]
  18. Maroli M, Rossi L, Baldelli R, Capelli G, Ferroglio E, Genchi C, Gramiccia M, Mortarino M, Pietrobelli M, Gradoni L. 2008. The northward spread of leishmaniasis in Italy: evidence from retrospective and ongoing studies on the canine reservoir and phlebotomine vector. Tropical Medicine and International Health, 13, 256–264. [CrossRef] [Google Scholar]
  19. Prudhomme J, Rahola N, Senghor M, Elgara E, Alten B, Serano D, Banuls AL. 2012. Spatiotemporal dynamic and bio-ecology of sandflies in the region of Montpellier. E-Sove, the 18th International Conference, Montpellier, p. 17. [Google Scholar]
  20. Quézel P, Médail F. 2003. Écologie et biogéographie des forêts du Bassin méditerranéen. Elsevier, 571 p. [Google Scholar]
  21. Quézel P, Verdier P. 1953. Les méthodes de la phytosociologie sont-elles applicables à l’étude des groupements animaux ? Quelques associations rupicoles de Carabiques dans le Midi de la France et leurs rapports avec les groupements végétaux correspondants. Vegetatio, 4, 165–181. [CrossRef] [Google Scholar]
  22. Ready P, Ready PA. 1981. Prévalence of Phlebotomus spp. in southern France: sampling bias due to different man-biting habits and autogeny. Annals of Tropical medicine and Parasitology, 75, 475–476. [PubMed] [Google Scholar]
  23. Rioux JA. 2001. Trente ans de coopération franco-marocaine sur les leishmanioses : dépistage et analyse des foyers. Facteurs de risque. Changements climatiques et dynamiques nosogéographiques. Association des Anciens Élèves de l’Institut Pasteur, 168, 90–100. [Google Scholar]
  24. Rioux JA, Cousserans J. 2008. La « démoustication » du littoral méditerranéen : base écologique, stratégie opérationnelle, devenir. Bulletin de l’Académie des Sciences et Lettres de Montpellier, NS, 39, 343–356. [Google Scholar]
  25. Rioux JA, Croset H, Corre JJ, Simmoneau P, Gras G. 1968. Phyto-ecological basis of mosquito control: Cartography of larval biotopes. Mosquito News, 28, 572–582. [Google Scholar]
  26. Rioux JA, La Rocque de S. 2003. Climats, leishmanioses et trypanosomoses. Changements climatiques, maladies infectieuses et allergiques, Sciences Médicales, Paris, 41–62. [Google Scholar]
  27. Rioux JA, Golvan YJ, Croset H, Houin R, Juminer B, Bain O, Tour S. 1967. Écologie des leishmanioses dans le Sud de la France. 1. Les Phlébotomes. Échantillonnage, éthologie. Annales de Parasitologie humaine et comparée, 42, 561–603. [Google Scholar]
  28. Rioux JA, Golvan YJ, Croset H, Tour S, Houin R, Abonnenc E, Petitdidier M, Vollhardt Y, Dedet JP, Albaret JL, Lanotte G, Quilici M. 1969. Épidémiologie des leishmanioses dans le Sud de la France, Préface N. Ansari, Monographie de l’Institut National de la Santé et de la Recherche Médicale (INSERM), Paris, 217 p. [Google Scholar]
  29. Rioux JA, Jarry D, Maazoun R, Wallbanks K. 1982. Confirmation de l’existence en France continentale de Phlebotomus sergenti Parrot, 1917. Annales de Parasitologie humaine et comparée, 57, 647–648. [Google Scholar]
  30. Rioux JA, Killick-Kendrick R, Leaney AJ, Turner DP, Bailly M, Young CJ. 1979. Écologie des leishmanioses dans le Sud de la France. 12. Dispersion horizontale de Phlebotomus ariasi Tonnoir, 1921. Expériences préliminaires. Annales de Parasitologie humaine et comparée, 54, 673–682. [Google Scholar]
  31. Rioux JA, Killick-Kendrick R, Périères J, Turner DP, Lanotte G. 1980. Écologie des leishmanioses dans le Sud de la France. 13. Les sites de « flanc de coteau », biotopes de transmission privilégiés de la leishmaniose viscérale en Cévennes. Annales de Parasitologie humaine et comparée, 55, 445–453. [Google Scholar]
  32. Rioux JA, Périères J, Killick-Kendrick R, Lanotte G, Bailly M. 1982. Écologie des leishmanioses dans le Sud de la France. 17. Échantillonnage des Phlébotomes par le procédé des pièges adhésifs. Comparaison avec la technique de capture sur appât humain. Annales de Parasitologie humaine et comparée, 57, 631–648. [Google Scholar]
  33. Siegel S, Castellan NJ. 2000. Nonparametric statistics for the behavioral sciences, 2nd ed. McGraw-Hill, USA, 399 p. [Google Scholar]
  34. Thioulouse J, Chessel D, Dolédec S, Olivier JM. 1997. ADE-4: a multivariate analysis and graphical display software. Statistics and Computing, 7, 75–83. [Google Scholar]
  35. Zar JH. 1999. Biostatistical Analysis, 4th ed. Prentice-Hall: USA, 663 p. [Google Scholar]
  36. Zeraia L. 2007. Inventaire floristique, écologique et cartographique des groupements végétaux des sites « Aussières » et « Quatourze » en vue de la mise en place de deux puits de carbone par reboisement adapté. Rapport de l’Office National des Forêts (ONF), 49 p. [Google Scholar]

Cite this article as: Rioux J.-A., Carron S, Dereure J, Périères J, Zeraia L, Franquet E, Babinot M, Gállego M & Prudhomme J: Ecology of leishmaniasis in the South of France. 22. Reliability and representativeness of 12 Phlebotomus ariasi, P. perniciosus and Sergentomyia minuta (Diptera: Psychodidae) sampling stations in Vallespir (eastern French Pyrenees region). Parasite, 2013, 20, 34.

All Tables

Table 1.

Frequency (% cover per 2,000 m2) of Corine Biotope habitats in the 12 sampling stations (including trapping walls and access roads). These stations were highly modified by human activities, but holm oaks were still well represented in most of them (20–40%).

In the text
Table 2.

Annual frequencies of the three sandfly species (Phlebotomus ariasi, P. perniciosus, Sergentomyia minuta) caught at the 12 selected sampling stations (“rational choice” method). There were considerable differences in sampled sandfly abundances between these stations (1.36–268.57 P/m2).

In the text

All Figures

thumbnail Figure 1.

Variations in mean monthly temperatures at the Montpellier-Fréjorgues (France) meteorological station between 1946 and 2004 (blue triangle: 1981, year of the study). The data have a normal distribution and the regression is significant (trend curve: Y = 0.0024, X + 13.511). The temperature increase over the considered period was 1.895 ± 0.068 °C.
In the text
thumbnail Figure 2.

Map of botanical successions around Perpignan (in: H. Gaussen : Carte de la végétation de la France au 1 / 200 000e, CNRS). In orange: cork oak. In yellow: holm oak. In green: white oak. In light blue: beech. In dark blue: fir. In purple: Scots pine. In red: mountain pine. In pink: alpine storey. Sampling area, , located in the Mediterranean holm oak and white oak succession. There were patches of cork oak.
In the text
thumbnail Figure 3.

Station No. 3. Trapping wall with weep holes (or so-called “barbacanes” in French). Inset: a weep hole containing a sticky trap. Forefront: Brachypodium phoenicoides meadow along the edge of a road. Background: Robinia pseudacacia and Quercus ilex.

In the text
thumbnail Figure 4.

Monthly frequency distribution of Phlebotomus ariasi ♂ + ♀ (sampling with sticky-paper traps): in the Cévennes region in 1960 and in Pyrénées-Orientales region in 1981. The subhumid bioclimatic conditions in the Pyrénées-Orientales could explain the bimodal distribution pattern and the autumn frequency spread.
In the text
thumbnail Figure 5.

Monthly frequency dynamics of Phlebotomus perniciosus in Pyrénées-Orientales region (1981) and in Tunisia (1960). In the vicinity of Tunis, under a semiarid Mediterranean bioclimate, these frequencies had a biphasic distribution pattern (summer diapause?). At Vallespir, in subhumid bioclimatic conditions, a unimodal distribution was noted.
In the text
thumbnail Figure 6.

10-day frequency distribution for Phlebotomus ariasi at the 12 sampling stations. The stations with the highest abundance (No. 4 and No. 12) showed a marked shift in maximum peak abundance: in July-June for No. 14 and July for No. 4. There was a similar shift in the lower second peak. This shift was likely related to the characteristics of the two trapping walls.
In the text
thumbnail Figure 7.

Phlebotomus ariasi: mean annual density per station (P ♂ + ♀ / m2). These frequencies did not have a normal distribution (Kolmogorov-Smirnov test, p < 0.01). The Friedman test, p < 0.001 (nonparametric, matched by date), showed that the frequencies were significantly different between the 12 stations, i.e. each station had its own specific sandfly density.
In the text
thumbnail Figure 8.

10-day variations in Phlebotomus ariasi densities-sampling results for stations No. 3 (red) and No. 11 (blue). The 10-day variation patterns did not significantly differ between stations (Cochran’s Q test, p = 0.949, nonparametric, matched by date).
In the text
thumbnail Figure 9.

Circle of correlation from a normalized PCA based on the data in Table 1 (19 Corine Biotope codes × 12 sampling station records). These correlations led to the identification of the Corine Biotope habitats most frequently associated in each of the 12 records. The F1 axis thus compares records containing wild Robinia stands (83.324), western Mediterranean riparian forests (44.5) and brambles (31.831) with those containing Osyris alba brush (32.216), tree rows (84.1), and holm oak thickets (45.3) and vegetation cover on siliceous rocks (62.2). The F2 axis compares records that associate orchards (83.15) and vineyards (83) with those that do not. The method used to differentiate the stations according to their phytoecological profile … and nothing else!
In the text
thumbnail Figure 10.

Projection of Phlebotomus ariasi (a), P. perniciosus (b) and Sergentomyia minuta (c) densities at sampling stations on the normalized PCA F1 axis. Among the readings grouped at the origin on the F1 axis, there are two habitat groups that are opposed in Figure 9 (circle of correlations). There is therefore no gradual relationship between the vector abundance and the environmental (habitat) trends. Corine Biotope habitats thus do not seem to be suitable for drawing up a vector sampling plan.
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.