Espace dual d'une algèbre modale complète et atomique

CÂNDIDA GOMES VALE1,2,∗,PEDRO TARROSO1,2,JOÃO CARLOS CAMPOS1,DUARTE VASCONCELOS GONÇALVES1,2,JOSÉ CARLOS BRITO1,2

1 - CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto. Instituto de Ciências Agrárias de Vairão, R. Padre Armando Quintas, 4485-661 Vairão, Portugal

2 - Departamento de Biologia da Faculdade de Ciências da Universidade do Porto, Rua Campo Alegre, 4169-007 Porto, Portugal

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Vale CG, Tarroso P, Campos JC, Gonçalves DV, Brito JC (2012). Distribution, suitable areas and conservation status of the Boulenger’s agama (Agama boulengeri, Lataste 1886). Amphibia-Reptilia 33: 526-532.

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Agama boulengeri is a West African endemic lizard. It occurs in arid rocky areas in the Mauritanian mountains and Kayes region of Mali. Data on the distribution of Agama boulengeri is however very coarse, and the contribution of climatic and habitat factors for population isolation are unknown. Using Maxent, GLM, and high resolution data, we generated environmental niche models, and quantified suitable areas for species occurrence. Field observations and predicted suitable areas were used to evaluate the conservation status of Agama boulengeri. Results revealed the species occurs preferentially close to gueltas, bare areas, and rocky deserts and in areas of increasing rainfall. Suitable cells were mostly located in Mauritania, and four potentially fragmented subpopulations were identified. The conservation status of Agama boulengeri was determined to be of Least Concern.

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The Boulenger’s agama (Agama boulengeri Lataste 1886) is a Sahelo-Saharan agamid, endemic to West Africa. The species is restricted to Mauritanian mountains (Adrar Atar, Tagant, Assaba and Afollé; Padial, 2006) and a few localities of the Kayes region in Mali (Joger and Lambert, 1996). Agama boulengeri has been associated with very arid rocky areas lacking vegetation cover (Geniez et al., 2004), being observed in rocky walls (de La Riva and Padial, 2008), but the species probably occupies more productive environments in the extreme southern range. To date, studies are lacking on local distribution and fragmentation levels, which results in a lack of knowledge regarding range size, and population size and number. A recent work about genetic variation of North African agamas reported two clusters of A. boulengeri restricted to northern and southern mountains of Mauritania (Gonçalves et al., 2012). Reproductive isolation between populations may be related to unsuitable habitats, such as permanently dune-covered areas separating Mauritanian mountains. Nevertheless, the contribution of climatic and habitat factors for population isolation remains unknown and high accuracy mapping of suitable areas for species occurrence should be considered a priority for the development of optimized local conservation strategies. Agama boulengeri was proposed to be included in the Lower Risk – Near Threatened (LR-NT) category of the IUCN red list (Geniez et al., 2004), but the species remains unlisted, probably due to the lack of knowledge about its biology, ecology, distribution and population trends.

The aims of this study were to identify environmental factors related to the occurrence of A. boulengeri, quantify suitable areas for species occurrence, and evaluate its conservation status. We combined high-resolution presence data (1x1km) with environmental factors to derive ecological niche-based models of species occurrence. Field observations and predicted suitable areas were used to evaluate conservation status.

METHODS

The study area was located in West Africa between 12.5ºN and 23.5ºN, and west of 5.0ºW, covering Mauritania, southern Morocco, Senegal, The Gambia, and south- western Mali (Fig. 4.5). In Mauritania, there are four main mountain massifs: the Adrar Atar in the central region, and the Tagant, Assaba and Afollé in the southern regions of the country. The Adrar Atar is separated from the remaining mountains by the El Khatt

river basin, while the Tagant-Assaba mountains are separated from the Afollé by the Karakoro river basin. The two river basins lack significant rock outcrops and they are dune-covered, but while the El Khatt is permanently dry, the Karakoro is subjected to seasonal run-offs. Most of the study area is covered by sandy, stony and bare deserts (30.0%, 17.9%, 10.0%, respectively; Bicheron et al., 2008). Cropland and cropland- vegetation mosaics (17.6%), and closed to open shrubland and grassland (11.8%), are present in the southern region.

A total of 166 observations (localities) of A. boulengeri were used to develop ecological niche-based models (Fig. 4.5). From these, 147 observations were collected during 10 fieldwork missions to Mauritania (http://cibio.up.pt/crocodilos/en/missions) that sampled 813 localities (Brito, 2003; authors’ unpublished data). The geographic locations of fieldwork observations were recorded using a Global Positioning System (GPS). The remaining 19 bibliographic observations included georeferenced localities or clear toponomies from which coordinates were collected (Institut Géographique National, IGN) to a precision of 1 km (Dekeyser and Villiers, 1956; Valverde, 1957; Joger, 1979; Le Berre, 1989; Joger and Lambert, 1996; Pleguezuelos et al., 2004; Geniez et al., 2004; Geniez and Arnold 2006; Padial 2005, 2006). From the initial dataset of observations, data were selected to develop ecological niche-models from clusters of species occurrence. Therefore, models were built using a dataset of 94 non-spatially aggregated observations (the minimum distance between observations was 10km), according to the Nearest Neighbour Index of ArcGIS 10.0 (ESRI, 2011). The remaining 72 observations were used for model validation and to calculate the threshold for species presence and the extent of occurrence (see below). Two datasets of pseudo- absences were built: a random dataset of pseudo-absences (RAbs; N=2000) and an absence dataset informed by fieldwork (FAbs; N=94). FAbs dataset was randomly created and then corrected by fieldwork information, to insure that all absences were located in areas where the species was not detected. Both datasets were created within a buffer of 100 km around the presence dataset and distant from the presence data by at least 20km.

Ecogeographical variables (hereafter EGV) included: 1) one topographical grid (USGS, 2006) that was used to derive Slope, with the “Slope” function of ArcGIS; 2) three climate grids (Hijmans et al., 2005); 3) four distance to habitats grids derived from a land-cover grid for the years 2004-2006 (Bicheron et al., 2008); and 4) distance to rock pools (locally known as gueltas), digitised from the IGN maps, and ground-validated in Mauritania by fieldwork (Table 4.2). For converting categorical land cover and presence of gueltas EGVs into continuous variables, one binary grid was created for each habitat type that covered more than 5% of the study area and for the presence of

gueltas. The distance to variable layers were processed using Euclidean distance of each grid cell to the closest habitat-type cell (Brito et al., 2009) using the “Euclidean Distance” tool of ArcGIS. All EGVs were used at the original square pixel size of 30'' (~1km). Correlation coefficients indicated low correlation (r<0.73) between EGVs, with the exception of distance to rock and precipitation (r = 0.86).

Table 4.2 - Environmental variables used for modelling the distribution of Agama boulengeri. Percentage of contribution

(%cont) derived from maximum entropy models. The coefficient (β) and the maximum (Max), minimum (Min), average (Avg) and standard deviation (SD), and significance (signif.) of each variable for generalized linear models (GLM) derived with random pseudo-absences (RAbs) are given. The coefficient (β), standard error (SE) and significance (signif.) of each variable for GLM model derived absences supervised by fieldwork (FAbs) are given. Significance codes are: ‘***’ p<0.001; ‘**’ p<0.01; ‘*’ p<0.05.

EGVs description West

Africa GLM RAbs GLM Fabs % Cont Max _(β) Min _(β) Avg β

(SD) Signif β SE Signif Annual precipitation 0.62 0.03 0.01 _(0.00) 0.02 *** 0.02 0.02 *** Maximum temperature of warmest month 0.20 0.14 -0.04 0.04

(0.05) 0.14 0.06 Annual average potential

evapotranspiration 0.26 0.00 -0.02 -0.01

(0.01) -0.02 0.01 Distance to mosaic cropland /vegetation 1.08 1.36 0.53 0.94

(0.23) 0.96 0.40 ** Distance to bare areas 4.21 24.00 12.51 17.98

(3.33) *** -19.83 6.92 *** Distance to consolidated bare areas

(rocky deserts) 1.47 0.38 -6.08 -3.86

(1.63) -4.55 1.83 Distance to seasonal rivers 0.96 0.11 -1.28 -0.62

(0.40) -1.14 0.49 * Distance to gueltas 90.12 -2.69 -4.81 -3.59

(0.49) *** -3.48 0.82 *** Slope 1.07 1.56 0.15 0.61

Fig. 4.5 - Distribution of Agama boulengeri observations (a). Binary predictions of species presence according to

maximum entropy and generalized linear models (GLMs) and according to two thresholds: maximum sensitivity plus specificity threshold calculated with the training and validation datasets (MaxSS Train-Test and MaxSS Valid, respectively). GLM models were derived with random pseudo-absences (RAbs) and absences supervised by fieldwork (FAbs) (b). Consensus prediction (six out of six models) was derived from the ensemble of binary predictions (c). This figure is published in color in the online version.

Ecological niche-based models were developed using the Maximum Entropy approach, implemented in Maxent 3.3.3 beta software (Phillips et al., 2006), and the generalized linear model (GLM; McCullagh and Nelder, 1989). A total of 20 Maxent model replicates were built with 20% of test data (19 observations) chosen by bootstrap with random seed, auto-features, and logistic output (Phillips et al., 2006). Area under the curve (AUC) of the receiver-operating characteristics (ROC) plot was taken as a measure of model fitness (Fielding and Bell, 1997). The 20 replicates were averaged to generate a forecast of species presence probability, which is a robust procedure to derive consensus predictions of species likelihood of presence (Marmion et al., 2009). Percentage of contribution of EGVs to the models was used to identify variables most related to species occurrence (Brito et al., 2009, 2011). Twenty replicate GLM models were built using the RAbs dataset and one GLM model with the FAbs datasets. An ANOVA was performed to determine the importance of EGVs for explaining species distribution and their significance for each model (chi-squared test) and coefficients were checked to identify relationships between species occurrence and environmental variation. The analysis was done in R software v. 2.13 (R Development Core Team, 2011).

Probability models (Maxent, GLM RAbs and GLM FAbs) were reclassified to display grid cells of probable absence and presence. The maximum training sensitivity plus specificity threshold (MaxSS) was used since it minimises both omission and commission errors (Liu et al., 2005). The best cut-off value corresponds to the point on the ROC curve where sensitivity and specificity are maximised, i.e. where the total amount of misclassification is minimised (Braunisch and Suchant, 2010). Max SS thresholds were calculated for both training and validation datasets (MaxSS Train and MaxSS Valid, respectively) resulting in a total of six models. The six binary models were added to derive an ensemble prediction of probable presence and absence. Consensus predictions were validated by calculating correct classification rates of both presence and absence data.

The conservation status assessment followed the methodology and criteria of IUCN guidelines for red lists (IUCN SPWG, 2008). Criteria of population reduction, geographic range, small population size and decline, and very small or restricted population (IUCN SPWG 2008) were applied using Ramas Red List software (Akçakaya and Ferson, 2001). These criteria were estimated using: 1) population number, from the number of mature individuals found during field sampling, the number of locations where the species was observed (see below 4), and the area of occupancy predicted for the species by models (see below 3); 2) extent of occurrence, by a minimum convex polygon method, which determines the area contained within the

shortest continuous boundary which can be drawn to encompass all observations (N=166) and the suitable area predicted by modelling (Vale, Álvares and Brito, 2012); 3) area of occupancy, from the number of suitable cells predicted by six models in the consensus map × area of a grid cell (1x1 Km2); and 4) population fragmentation, evaluated based on the number of subpopulations, which were quantified by the number of isolated suitable patches forecasted by consensus predictions, and the number of locations, quantified from the number of unclustered observations (training dataset). Population reduction and continuous decline estimates were input into Ramas with values below the thresholds for Threatened taxa classification (30% of reduction and 10 to 25% of continuous decline, according to future time periods).

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The Maxent ROC plots exhibited high average AUCs for both training and test datasets: 0.97 (± 0.01 sd) and 0.96 (± 0.01 sd), respectively. Maxent and GLM models identified distance to gueltas as the most important EGVs related to the distribution of A. boulengeri, followed by distance to bare areas (Table 4.2). Distance to rocky deserts and annual precipitation were also relevant according to Maxent and GLMs, respectively. The coefficients (β) of these EGVs on GLM models suggested positive relationships between species presence with increasing annual precipitation and negative relationships with increasing distances to gueltas and to rocky deserts (Table 4.2).

The correct classification rate of presences and absences according to the consensus prediction were 93.4% and 81.9%, respectively. Predicted suitable areas for A. boulengeri were consistent between model types and thresholds (MaxSS Train and MaxSS Valid, Fig. 4.5). Consensus predictions were mostly restricted to Mauritanian mountains, while four individual models also predicted presence in south-western Mali. Consensus predictions identified about 5.3% of the study area as suitable for the occurrence of A. boulengeri, of which 99.8% of suitable cells were located in the mountains and escarpments of Mauritania (84,514km2), 0.1% in scattered grid cells in Senegal (~85 km2), 0.07% in localized areas in Mali (~58km2) and 0.03% in Morocco (~24km2).

The extent of occurrence and area of occupancy were estimated to be 291,741 km2 and 84,664 km2 respectively, and four potentially fragmented subpopulations were identified: Adrar Atar, Tagant-Assaba and Afollé in Mauritania, and Kayes at Mali (Fig. 4.5). Taking into account the number of un-clustered localities (N=94) and the number

of different locations (~281) where Agama boulengeri was observed, they clearly exceed the threshold for Threatened classification (10 localities). The total number of mature individuals should be also much higher than 10,000 individuals (threshold for Threatened classification), given the area of occupancy and the count number of localities where the species was observed and the detectability of the species during the fieldwork. The input of these parameters in Ramas software gave the conservation status of Least Concern (LC).

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Ecological models allowed understanding of probable relationships between Agama boulengeri occurrence and environmental gradients. Results suggested that presence probability of the species increases near gueltas, bare areas and rocky deserts, and with annual precipitation. Indeed, the species has been associated with arid rocky areas lacking vegetation and to wet rocky gorges (gueltas) in the mountain areas (Geniez et al., 2004; Padial, 2005). All model types identified distance to gueltas as the most important environmental variable related to species presence and it is probably associated with the presence of rock walls, where the species has been observed (de La Riva and Padial, 2008).

Predicted suitable areas followed the expected distribution pattern for the species (Joger and Lambert, 1996; Geniez et al., 2004; Padial, 2006). Yet, the combination of fine-scale ecological models and two model techniques allowed the definitions of accurate suitable areas for species presence. For instance, Yelimane at Mali was predicted to be suitable for occurrence, but there are no records of species presence. Future sampling is needed to assess the species’ presence in the region.

The suitable areas of A. boulengeri predicted by consensus between six models are mostly restricted to isolated mountain areas. Relatively small distances between patches of suitable areas were predicted by the consensus. Suitable areas in the Adrar Atar are separated from the Tagant-Assaba, in the narrowest fringe, by 5 km of unsuitable habitat, in the region of the dry and dune-covered El Khatt river basin. Yet, suitable areas in the Tagant-Assaba are separated from the Afollé by at least 3 km, along the Karakoro river basin, which lacks rock outcrops. On the other hand, relatively large distances between suitable patches in southern Mauritanian mountains and Mali were forecasted by the consensus of the six models. Suitable areas in southern Assaba were predicted to be separated from Kayes by a 60 km wide-band corresponding to the unsuitable lower Senegal river which supports the likely isolation

of Malian populations. Although A. boulengeri home range and dispersal capability are unknown, its relatively small body size (total length ~30cm) and habitat specialization may hamper dispersal and gene flow between the potential subpopulations identified. Molecular studies point to the existence of at least two lineages mostly restricted to the Adrar Atar-Tagant and Assaba mountains (Gonçalves et al., 2012); but additional studies are needed to determine if genetic sub-structuring occurs among the four distinct subpopulations predicted here.

Agama boulengeri was categorised as Least Concern, given that all parameters analysed exceeded the thresholds for categorization as Threatened. The species may be susceptible to climate change and natural disasters, such as drought, and quantitative data on population size and trends are needed to better estimate population parameters and assess species vulnerability to climate change.

Results from this study emphasize the biological value of Mauritanian mountains, and further support the importance of these island-like mountains for conservation of Sahelo-Saharan biodiversity (Tellería et al. 2008; Padial and Tellería, 2009; Trape, 2009; Brito et al., 2010, 2011; Vale, Álvares and Brito, 2012; Padial et al., in press). Distribution and habitat selection patterns observed may give indications about other mountain-restricted species in the region, with fragmented distributions and similar habitat requirements, such as Tarentola parvicarinata, Pristurus adrarensis or Ptyodactylus ragazzi. The methodological approach used here should be applied to other desert isolated species and particularly to other mountain endemic species.

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This study was partially supported by a grant from the Mohammed bin Zayed Species Conservation Fund to CGV (11052707), a grant from National Geographic Society to JCB (8412-08), and by Fundação para a Ciência e Tecnologia (PTDC/BIA- BEC/099934/2008) through the EU programme COMPETE. CGV, PT and DVG have

PhD grants (SFRH/BD/72522/2010, SFRH/BD/42480/2007, and

SFRH/BD/78402/2011, respectively) and JCB has a contract (Programme Ciência 2007), all from FCT. We acknowledge F. Martínez-Freiría, P. Sierra, N. Sillero, and A.S. Sow for the fieldwork help. Logistic support for fieldwork was given by Pedro Santos Lda (Trimble GPS), Off Road Power Shop, and P.N. Banc d’Arguin (Mauritania).

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Akçakaya, H.R., Ferson, S. (2001): Ramas Red List: Threatened Species Classifications Under Uncertainty. User Manual for Version 2.0. Applied Biomathematics, New York.

Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc, M., Bontemps, S., Leroy, M., Achard, F., Herold, M., Ranera, F., Arino, O. (2008): GLOBCOVER: Products description and validation report. Medias-France and Postel. http://postel.mediasfrance.org/

Braunisch, V., Suchant, R. (2010): Predicting species distributions based on incomplete survey data: the trade-off between precision and scale. Ecography. 33: 826-840.

Brito, J.C. (2003): Observations of amphibians and reptiles from North and West Africa – Morocco, Mauritania and Senegal. Bol. Asoc. Herp. Esp. 14: 2-6.

Brito, J.C., Acosta, A.L., Álvares, F., Cuzin, F. (2009): Biogeography and conservation of taxa from remote regions: An application of ecological-niche based models and GIS to North-African Canids. Biol. Conserv. 142: 3020–3029.

Brito, J.C., Álvares, F., Martínez-Freiría, F., Sierra, P., Sillero, N., Tarroso, P. (2010): Data on the distribution of mammals from Mauritania, West Africa. Mammalia. 74: 449–455

Brito, J.C., Fahd, S., Geniez, P., Martínez-Freiría, F., Pleguezuelos, J.M., Trape, J.-F. (2011): Biogeography and conservation of viperids from North-West Africa: an application of ecological niche-based models and GIS. J. Arid Environ. 75: 1029- 1037.

Dekeyser, P.L., Villiers, A. (1956): Contribution à l'étude du peuplement de la Mauritanie. Notations écologiques et biogéographiques sur la faune de l'Adrar. Mémoires de l'Institut Français d'Afrique Noire. 44: 9-222.

de La Riva, I., Padial, J.M. (2008): First record of the genus Ptyodactylus Goldfuss, 1820 (Sauria: Gekkonidae) for Mauritania (West Africa). Salamandra. 44: 51-53. ESRI (2011): ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems

Research Institute. Inc, USA.

Fielding, A.H., Bell, J.F. (1997): A review of methods for the assessment of prediction errors in conservation presence/absence models. Envir. Conserv. 24: 38-49. Geniez, P., Mateo, J A., Geniez, M., Pether, J. (2004): The Amphibians and Reptiles of

Geniez, P., Arnold, E.N. (2006): A new species of Semaphore gecko Pristurus (Squamata: Gekkonidae) from Mauretania, represents a 4700km range extension for genus. Zootaxa. 1317: 57-68.

Gonçalves, D.V., Brito, J.C., Crochet, P.-A., Geniez, P., Padial, J.M., Harris, J. (2012): Phylogeny of North African Agama lizards (Reptilia: Agamidae) and the role of the Sahara desert in vertebrate speciation. Mol. Phylogenet. Evol. http://dx.doi.org/10.1016/j.ympev.2012.05.007

IUCN Standards and Petitions Working Group (2008): IUCN Standards and Petitions Working Group, 2008. Guidelines for Using the IUCN Red List Categories and Criteria. Version 7.0. Prepared by the Standards and Petitions Working Group of the IUCN SSC Biodiversity Assessments Sub-Committee. IUCN, Gland, Switzerland and Cambridge, UK.

Joger, U. (1979): Zur ökologie und Verbreitung wenig bekannter Agamen Westafrikas (Reptilia: Sauria: Agamidae). Salamandra. 15: 31-52.

Joger, U., Lambert, M.R.K. (1996): Analysis of the herpetofauna of the Republic of Mali, I. Annotated inventory, with description of a new Uromastyx (Sauria: Agamidae). Journal of African Zoology. 110: 21-51.

Le Berre, M. (1989): Faune du Sahara. 1. Poissons, Amphibiens et Reptiles. Lechevalier, R. Chabaud, Paris.

Liu, C., Berry, P.M., Dawson, T.P., Pearson, R.G. (2005): Selecting thresholds of occurrence in the prediction of species distributions. Ecography. 28: 385-393. Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R.K., Thuiller, W. (2009):

Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15: 59-69.

McCullagh, P., Nelder, J.A. (1989): Generalized linear models. Chapman and Hall. Padial, J.M. (2005): A new species of Agama (Sauria: Agamidae) from Mauritania.

Herpetological Journal. 15: 27-3

Padial, J.M. (2006): Commented distributional list of the reptiles of Mauritania (West Africa). Graellsia. 62(2): 159-178

Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006): Maximum entropy modelling of species geographic distributions. Ecological Modelling. 190: 231–259.

Pleguezuelos, J.M., Fariña, B., Mateo, J.A. Geniez, P. (2004): Nuevos datos sobre los anfibios y reptiles de Mauritania y el Sáhara Occidental. Bol. Asoc. Herpetol. Esp. 15 (2): 76-80

R Development Core Team (2011): R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: R-project.org.

USGS (2006): Shuttle Radar Topography Mission (SRTM): Mapping the world in 3 dimensions. – United States Geological Survey. http://srtm.usgs.gov/index.html Valverde, J.A. (1957): Aves del Sahara Español. Estudio ecológico del desierto.

Instituto de Estudios Africanos, Madrid. 487p.

Vale, C.G., Álvares, F., Brito, J.C. (2012): Distribution, suitable areas and conservation status of the Felou gundi (Felovia vae Lataste 1886). Mammalia 76: 201-207

Chapter 5