1
SUPPORTING INFORMATION
2
3
Identification of limiting climatic and geographical variables for the
4
distribution of the tortoise Chelonoidis chilensis (Testudinidae)
5
6
7
Authors: Alejandro Ruete1*, Gerardo C. Leynaud2
8
1
9
Appendix S1. Bayesian spatially expanded logistic (BSEL) model and model
10
selection procedure
11
We developed a Bayesian spatially expanded logistic (BSEL) model (Casetti 1997;
12
Congdon 2003) to obtain the probability of observation at non-visited locations. Non-
13
visited locations were randomly located with the same density as the observed locations
14
(~0.0004/km2). Given the nature of presence-only data, predicted probabilities combine
15
the probability of the species being at the location, the probability of an observer being at
16
the same location, and the probability of the observer finding the species (Lobo et al.
17
2010). We assume that observations at every non-visited location i are distributed
18
according to a Bernoulli distribution Obsi ~ Bernoulli(p*i), where p*i is an a priori
19
probability distribution generated from confirmed observations (Fig. 2b). We generated
20
the a priori probability distribution as a quadratic density kernel raster layer using the R
21
package “splancs” (Rowlingson et al. 2013). By generating a prior distribution from the
22
observations, we assume that the entire study region has been sampled with the same
23
intensity.
24
We then modelled observations Obsi according to a logistic model, Obsi ~
25
Bernoulli(pi), and logit(pi) = φ + αv · V + βv,i · V, where pi is the probability of observing
26
the species at location i, logit the logistic link function, φ is the regression intercept, and
27
Vv×i is a matrix for the environmental variables v. The spatially expanded model (Casetti
28
1997; Congdon 2003) assumes that the effect of an explanatory variable v on the response
29
variable pi varies among the observed locations. This assumption is particularly
30
convenient when fitting species distribution model along large ranges, where the species
31
can be locally adapted to e.g. temperature ranges (Turchin & Hanski 1997; Nilsson-
32
Örtman et al. 2013). The model allows estimating fixed parameters (αv; i.e. without
33
spatial variability) and flexible parameters (βv,i) that correct for the spatial variation of the
34
effect parameter αv, as well as for spatial autocorrelation on the observations. The
2
35
parameter βv,i is further modelled as βv,i = xi · γxv + yi · γyv, where γxv y γyv are correction
36
parameters for coordinates xi and yi. The combined effect (fixed and flexible) of variable
37
v varies for every location, and is described as δv,i = αv + βv,i, with an average of 𝛿𝑣̅ =
38
𝛼𝑣 + 𝛽𝑣̅ .
39
The final model presented (Table 1) is the result of a selection procedure based on
40
the deviance information criterion (DIC), an information-theoretic criterion that is
41
appropriate for Bayesian hierarchical modelling (Spiegelhalter et al. 2002). The lower the
42
DIC, the better the model is able to predict a new data set, and thus, the DIC penalizes for
43
increasing model complexity just as the commonly used Akaike’s information criterion
44
(AIC; Burnham & Anderson 2002). First, we compared models for each independent
45
environmental variable with a null model (i.e. only including the intercept parameter φ).
46
Then, we built the final model with a forward stepwise procedure, following the DIC
47
ranking of the variables (Tables S2). We used a threshold of 10 DIC units to consider
48
model improvement. A model could include correlated variables if the later added
49
variable improved the model fit, according to the over-parameterized models criterion
50
(Reichert & Omlin 1997).
51
52
53
54
55
References
Casetti, E. (1997). The expansion method, mathematical modeling, and spatial
econometrics. International Regional Science Review, 20, 9–33. Retrieved August
18, 2012,
56
57
Congdon, P. (2003). Applied Bayesian modelling. John Wiley and Sons, West Sussex,
England.
58
59
60
Lobo, J.M., Jiménez-Valverde, A. & Hortal, J. (2010). The uncertain nature of absences
and their importance in species distribution modelling. Ecography, 33, 103–114.
Retrieved September 23, 2013,
61
62
63
Nilsson-Örtman, V., Stoks, R., De Block, M., Johansson, H. & Johansson, F. (2013).
Latitudinally structured variation in the temperature dependence of damselfly
growth rates. Ecology Letters, 16, 64–71. Retrieved March 15, 2013,
3
64
65
Reichert, P. & Omlin, M. (1997). On the usefulness of overparameterized ecological
models. Ecological Modelling, 95, 289–299. Retrieved January 11, 2010,
66
67
68
Rowlingson, B., Diggle, P., Bivand, R., Petris, G. & Eglen, S. (2013). splancs: Spatial
and space-time point pattern analysis. Retrieved from http://CRAN.Rproject.org/package=splancs
69
70
71
Turchin, P. & Hanski, I. (1997). An empirically based model for latitudinal gradient in
vole population dynamics. The American Naturalist, 149, 842–874. Retrieved
May 23, 2012,
72
73
4
74
Table S1. Complete list of observations
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
Source
1
4
1
2
4
4
4
1
9
9
1
3
3
3
3
3
4
4
3
9
3
3
3
3
4
3
2
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
X
-62.883
-60.283
-62.618
-64.250
-64.760
-62.453
-65.541
-66.669
-67.615
-64.564
-62.816
-60.000
-62.980
-62.680
-65.470
-61.200
-60.450
-60.430
-61.170
-60.620
-61.280
-60.220
-64.230
-63.970
-63.930
-64.570
-63.430
-63.620
-63.580
-63.580
-58.680
-67.500
-63.580
-67.680
-67.620
-66.930
-66.230
-65.920
-66.400
-65.650
-64.600
-66.820
-68.000
-68.070
-68.000
-67.970
-68.400
-67.900
-67.900
-68.250
-67.920
-67.880
-67.900
-68.780
-68.100
-65.680
-65.480
Y
-38.483
-22.350
-39.810
-27.733
-40.600
-23.467
-41.042
-37.155
-37.353
-37.285
-41.030
-22.500
-40.800
-39.480
-28.050
-27.220
-26.780
-26.350
-26.520
-25.950
-27.320
-26.870
-31.480
-31.620
-31.650
-31.480
-30.750
-31.330
-30.350
-30.400
-32.470
-30.000
-30.150
-37.800
-37.370
-36.250
-37.550
-38.150
-38.720
-37.320
-37.380
-28.550
-33.470
-33.580
-33.850
-34.050
-34.580
-34.830
-34.830
-34.830
-34.220
-34.480
-34.600
-32.970
-38.030
-39.270
-39.100
5
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
3
3
3
3
3
3
3
3
2
2
3
3
3
3
3
3
3
3
3
1
3
3
9
2
3
3
3
4
3
3
3
3
3
3
3
3
3
3
4
4
3
4
5
1
1
1
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
-64.430
-65.250
-65.250
-63.970
-67.330
-67.780
-65.370
-62.830
-63.000
-63.470
-64.500
-63.450
-63.700
-63.950
-64.900
-62.850
-64.830
-65.280
-57.920
-62.580
-64.250
-64.480
-60.620
-64.270
-58.680
-64.970
-64.080
-63.000
-64.630
-68.080
-64.230
-64.080
-68.770
-64.180
-58.500
-68.400
-65.500
-60.670
-59.300
-57.880
-60.000
-65.006
-65.373
-62.249
-67.085
-64.692
-62.646
-63.006
-63.677
-65.169
-65.405
-64.535
-65.367
-64.883
-65.430
-65.367
-63.777
-62.820
-64.684
-64.932
-65.206
-40.100
-40.400
-40.400
-24.870
-31.480
-32.200
-32.770
-28.470
-28.470
-29.370
-27.400
-29.550
-29.520
-29.050
-27.520
-26.580
-28.000
-26.220
-22.330
-39.770
-27.730
-30.200
-25.980
-27.780
-32.470
-40.750
-38.970
-40.750
-37.420
-38.920
-27.730
-39.100
-33.000
-31.400
-34.670
-34.580
-40.500
-32.950
-23.100
-22.600
-22.500
-40.616
-41.565
-40.555
-39.623
-40.088
-40.184
-39.587
-38.879
-39.227
-39.500
-40.171
-43.291
-42.322
-41.688
-40.992
-40.880
-40.743
-40.631
-40.457
-40.333
6
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
-65.492
-65.827
-66.623
-66.536
-67.170
-66.349
-65.840
-65.541
-65.343
-64.423
-66.026
-67.766
-68.239
-67.393
-67.530
-66.660
-68.189
-67.431
-67.915
-67.741
-67.990
-68.276
-68.089
-67.766
-68.189
-68.015
-67.688
-67.436
-67.791
-67.643
-67.836
-68.192
-68.563
-67.851
-67.658
-67.050
-66.575
-67.169
-67.539
-67.228
-66.739
-67.361
-67.495
-67.791
-67.895
-67.213
-66.783
-66.546
-66.457
-65.315
-66.442
-67.035
-67.080
-67.124
-67.510
-67.777
-66.709
-66.724
-66.620
-66.397
-66.071
-40.495
-40.606
-39.786
-39.277
-39.214
-38.605
-38.146
-38.046
-37.425
-37.400
-37.412
-37.885
-37.822
-37.773
-37.251
-36.343
-36.244
-35.772
-35.809
-35.548
-35.424
-35.299
-35.063
-34.902
-34.765
-37.524
-34.380
-34.068
-34.157
-33.950
-33.757
-33.638
-33.401
-33.505
-33.253
-33.460
-33.223
-33.104
-32.912
-32.778
-33.015
-32.585
-32.422
-32.467
-32.274
-32.348
-32.511
-32.852
-32.556
-32.363
-32.096
-31.696
-31.236
-30.880
-30.376
-30.138
-29.501
-28.922
-28.655
-28.922
-29.634
7
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
2
2
2
2
2
2
7
4
4
6
5
-65.211
-64.143
-63.669
-63.832
-63.965
-63.165
-63.150
-63.135
-63.016
-63.209
-64.025
-64.929
-64.470
-65.048
-63.565
-63.076
-62.764
-62.616
-63.995
-64.677
-65.389
-64.484
-64.811
-63.728
-64.484
-63.921
-62.838
-62.571
-61.296
-61.163
-60.169
-60.347
-60.406
-59.383
-61.326
-60.584
-60.733
-61.459
-62.735
-63.639
-64.054
-64.292
-64.069
-64.025
-63.965
-63.951
-62.067
-59.442
-58.612
-59.027
-63.491
-67.400
-68.217
-68.751
-68.066
-64.427
-62.462
-62.084
-61.248
-60.034
-68.796
-31.518
-31.547
-31.710
-31.577
-31.206
-30.761
-30.539
-30.302
-30.020
-29.723
-30.420
-30.435
-30.213
-29.649
-29.530
-29.530
-29.100
-28.567
-28.507
-28.285
-28.240
-27.647
-27.528
-27.603
-26.802
-26.105
-26.120
-26.639
-27.617
-27.410
-27.054
-26.965
-26.520
-26.223
-26.550
-25.897
-25.526
-25.215
-24.310
-25.586
-25.289
-25.171
-24.652
-24.385
-24.103
-23.762
-23.065
-23.213
-22.160
-21.908
-24.358
-32.450
-32.134
-33.304
-33.300
-39.363
-21.651
-18.472
-20.546
-20.489
-37.162
8
241
242
243
244
75
76
77
78
79
80
81
82
83
84
85
86
5
5
5
9
-68.478
-67.835
-66.660
-69.916
-37.480
-30.863
-31.837
-40.029
Sources
1: Buskirk (1993)
2: Cabrera (1998)
3: The EMYSystem (http://emys.geo.orst.edu/cgi-bin/emysmap?tn=138&cf=ijklmno)
4: Ernst (1998)
5: Fritz et al. (2012)
6: Gonzales et al. (2006)
7: Ergueta & Morales (1996)
8: Richard (1999)
9: Waller (1986)
9
87
Table S2. Explanatory variables and model selection
Explanatory variables included in the analysis. Each row belongs to an
independent model, showing the deviance information criterion (DIC), and
its difference (Δ) with the DIC of a null model (i.e. a model with only
intercept parameters). Color shades are a visual help to order ΔDIC from
gratest (red) to smallest (green).
Variable
DIC
ΔDIC
Null
1487.2 0.0
bio1
Annual Mean Temperature
851.2 636.0
1
bio2
Mean Diurnal Range
1253.6 233.6
2
bio3
Isothermality
1186.0 301.2
bio4
Temperature Seasonality3
1019.0 468.3
bio5
Max Temperature of Warmest Month
891.5 595.7
bio6
Min Temperature of Coldest Month
1163.4 323.8
4
bio7
Temperature Annual Range
1113.5 373.8
bio8
Mean Temperature of Wettest Quarter
1002.9 484.3
bio9
Mean Temperature of Driest Quarter
1385.8 101.4
bio10
Mean Temperature of Warmest Quarter
945.0 542.2
bio11
Mean Temperature of Coldest Quarter
988.5 498.7
bio12
Annual Precipitation
1304.1 183.1
bio13
Precipitation of Wettest Month
1321.9 165.3
bio14
Precipitation of Driest Month
1274.6 212.6
5
bio15
Precipitation Seasonality
1281.8 205.5
bio16
Precipitation of Wettest Quarter
1293.4 193.8
bio17
Precipitation of Driest Quarter
1291.1 196.1
bio18
Precipitation of Warmest Quarter
1213.4 273.8
bio19
Precipitation of Coldest Quarter
1251.5 235.8
himpact
Areas of human impact over the biosfere6
1368.6 118.6
globedem Altitude
1406.2 81.0
LAIm
Leaf Area Index
1296.2 191.0
iflworld
World intact forest
1479.4 7.8
1
Mean of monthly (max temp - min temp)
2
(bio2/bio7) * 100
3
standard deviation *100.
4
bio5-bio6
5
coefficient of variation
6
binomial
88
10
89
90
91
92
Figure S1: BSEL model uncertainty. Mode of probability of observation and length of
the 95% CI over the study area, for the final BSEL model.
11
93
94
95
96
97
Figure S2: Predictions of the final BSEL model. The colour scale indicates the
probabilities of observation. Red and blue lines show protected areas where the species
has and has not been reported, respectively.
98
12
99
100
Table S3. Presence of Chelonoidis chilensis on protected areas in Argentina and Bolivia.
Table S3.1: Argentinean protected areas and probabilities of observation (p) of Chelonoidis
chilensis predicted by the Bayesian Spatially Expanded Logistic (BSEL) model. The length of
the 95% credible interval is (L 95% CI) shown.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Protected Area
Monte de las Barrancas
Sierra de las Quijadas
Valle Fértil
Telteca
Quebracho de la Legua
Chancani
Copo
Guasamayo
Limay Mahuida
La Reforma (Univ.)
Bahía de San Antonio
La Humada
La Reforma
Talampaya
Pichi Mahuida
Punta Delgada
Ischigualasto (o Valle de La Luna)
Salitral Levalle
Caleta Valdés
Auca Mahuida
Lihué Calel
Meseta de Somuncurá
Formosa
El Payén
Mar Chiquita
Santa Ana
Parque Luro
Pampa del Indio
Los Palmares
Laguna La Felipa
Laguna Guatrache
Agua Dulce
El Mangrullo
Vaquerías
Laguna de Llancanelo
Lagunas y Palmares
La Florida P.
Presidencia de la Plaza
Chaco
La Quebrada
Independent
p(BSEL) L 95% CI Observation
0.89
0.08
0
0.88
0.11
1
0.87
0.27
1
0.86
0.13
1
0.85
0.09
0
0.84
0.15
0
0.82
0.16
1
0.74
0.16
0
0.71
0.2
1
0.7
0.18
1
0.7
0.24
0
0.69
0.18
0
0.69
0.19
0
0.68
0.32
1
0.67
0.19
1
0.66
0.43
0
0.65
0.27
1
0.64
0.2
0
0.63
0.22
0
0.61
0.28
1
0.61
0.2
1
0.59
0.19
0
0.59
0.18
1
0.56
0.26
1
0.56
0.19
0
0.52
0.2
0
0.45
0.2
0
0.45
0.2
1
0.45
0.18
0
0.43
0.2
0
0.42
0.23
0
0.41
0.24
0
0.4
0.32
0
0.39
0.23
0
0.39
0.25
0
0.37
0.2
0
0.35
0.23
0
0.3
0.18
0
0.29
0.18
0
0.26
0.23
0
13
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
Don Guillermo
La Norma
Quebrada del Portugués
El Rey
Potrero 7-B (Los Quebrachales)
Aguas Chiquitas
La Loca
Iberá
Del Medio - Los Caballos
Río Pilcomayo
Calilegua
Laguna Hu
Quebrada del Condorito
Mburucuyá
General Obligado
El Leoncito
Baritú
Campo General Belgrano
Bosques Petrificados de Jaramillo
Sierra de San Javier
Acambuco
Campo Salas
Pre Delta Diamante
Virá Pitá
Potrero de Yala
Los Cardones
Lago Urugua-í
Bosque Petrificado Sarmiento
Iguazú
Otamendi
El Tromen
Laguna Blanca (Neuquén)
Laguna Blanca
Litoral Chaqueño
Cabo Blanco
Cerro Currumahuida
Laguna Aleusco
Los Andes
Volcán Tupungato
Laguna de los Pozuelos BioRes
(National)
Campo de los Alisos
Lago Puelo
Olaroz-Caucharí
Salto Encantado del Valle del Cuñá
Pirú
Apipé Grande
0.26
0.25
0.23
0.22
0.22
0.22
0.21
0.2
0.19
0.18
0.17
0.17
0.14
0.12
0.12
0.12
0.1
0.1
0.09
0.08
0.08
0.08
0.079
0.06
0.06
0.06
0.05
0.05
0.05
0.04
0.04
0.04
0.04
0.03
0.03
0.02
0.02
0.02
0.02
0.19
0.18
0.23
0.21
0.18
0.21
0.18
0.18
0.16
0.17
0.17
0.12
0.21
0.15
0.12
0.21
0.23
0.18
0.12
0.19
0.11
0.1
0.1
0.09
0.11
0.13
0.18
0.09
0.16
0.07
0.13
0.12
0.11
0.07
0.09
0.09
0.05
0.07
0.09
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.02
0.02
0.01
0.01
0.16
0.14
0.06
0.04
0
0
0
0
0.01
0.01
0.03
0.05
0
0
14
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
Esperanza
Saltito
Laguna Los Juncos
Punta Lara
Nahuel Huapi Par1
Rio Limay
Lanín
Los Alerces
Copahue-Caviahue
El Destino (P. Costero del Sur)
Urugua-í
Nahuel Huapi Parque
Nahuel Huapi Reserva
Selva Marginal de Hudson
Divisadero Largo
El Manzano Histórico
Lago Baggilt
Aconcagua
Cañada Molina
Nant y Fall (Arroyo Las Caídas)
Papel Misionero
Laguna Brava
Alto Andina de la Chinchilla
Colonia Benítez
Laguna La Salada
La Loma del Cristal
Moconá
Yacuy
Florencio de Basaldua
Carpincho
Los Sosa
Guaraní
Cruce Caballero
Cerro Azul (E.E.A.)
E.E.A. Anexo Cuartel Río Victoria
De la Sierra Crovetto
Guardaparque Horacio Foerster
Piñalito
General Belgrano
Los Arrayanes
Isla Botija
Sierra del Tigre
Punta Márquez
Lago Guacho
Punta Norte
Cerro Alcazar
La Florida R.
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.12
0.03
0.03
0.03
0.07
0.04
0.11
0.06
0.17
0.03
0.09
0.04
0.05
0.03
0
0
0
0.03
0
0
0
0.03
0.03
0
0
0
0
0
0
0
0
0.02
0
0
0
0
0.02
0.01
0.01
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
15
133
134
135
136
137
138
139
140
141
142
143
144
El Estero
Isla Laguna Alsina
Yabotí
Golfo San José
Perito Moreno
Los Glaciares
Ira Hiti
Cabo dos Bahías
Cayastá
El Pozo
Punta Pirámides
Punta Loma
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.02
0
0.01
0.03
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
101
102
Table S3.2: Bolivian protected areas and probabilities of observation (p) of Chelonoidis
chilensis predicted by the Bayesian Spatially Expanded Logistic (BSEL) model. The length of
the 95% credible interval is (L 95% CI) shown.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Protected Area
Area de proteccion del Quebracho
Colorado
Kaa-iya del Gran Chaco
Isiboro Securé
Tariquía
Madidi
Area de proteccion del Pino del Cerro
Otuquis
Pilón Lajas
Carrasco
Cordillera de Sama
Iñao
Apolobamba
Cotapata
Cotapata
Amboró
Amboró
El Palmar
Estancias San Rafael
Noel Kempff Mercado
Ríos Blanco y Negro
San Matías
Toro Toro
Tunari
Cavernas del Repechón
Cerro Tapilla
Eduardo Avaroa
p(BSEL)
0.28
0.16
0.09
0.09
0.05
0.04
0.04
0.04
0.03
0.03
0.03
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0
0
0
L 95% CI
0.27
0.23
0.8
0.14
0.35
0.13
0.08
0.35
0.3
0.19
0.04
0.28
0.12
0.12
0.04
0.05
0.02
0.03
0.02
0.03
0.01
0.02
0.05
0
0
0.02
Independent
Observation
0
1
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16
27
28
29
30
31
32
33
34
35
36
37
38
Estación Biológica del Beni
Flavio Machicado Viscarra
Huancaroma
Incacasani Altamachi
Las Barrancas
Llica
Madidi
Mallasa
Mirikiri
Sajama
Tuni Condoriri
Yura
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.04
0
0
0
0
0
0.01
0.01
0.01
0
0
0
0
0
0
0
0
0
0
0
0
103
17