The most well-known and vast Calabrian pine forests are in the Sila mountain range, southern Italy. In this paper, present-day distribution of Calabrian pine in the Sila district was analyzed and compared with forest maps dating back to 1935 in order to assess the changes in land use. Main ecological and anthropogenic factors affecting Calabrian pine forest distribution were investigated by logistic regression models to identify the most important predictors of Calabrian pine persistence, expansion, and transition over the period 1935-2006. In 2006, the area covered by Calabrian pine forests and mixed Calabrian pine-beech forests was 36 100 ha and 20 221 ha, respectively. Overall, pine forest area increased by 38% between 1935 and 2006. Logistic regression revealed that Calabrian pine distribution in the Sila district was affected by both ecological (bioclimate, soil, and elevation) and anthropogenic factors (management, fire). Based on our results, four different potential dynamics of Calabrian pine stands are discussed in the perspective of the sustainable management and conservation of this important mountain forest habitat.
The Mediterranean basin is one of the major plant diversity hotspots and among the richest in endemism over the world (
Around the Mediterranean Basin, changes in land-use and different forms of forest utilization over several millennia have shaped diverse landscape mosaics with different and often impacting consequences on biodiversity (
Laricio pine (
Calabrian pine is endemic to southern Italy with a natural range extending from Calabria to Sicily. Calabrian pine forests (Cod. Eunis, G3.55, Calabrian
During the last ice age (the Würm period in the Alps) Calabrian pine covered vast areas of the Apennines. In the Boreal period, Calabrian pine expansion slowed down due to competition with silver fir (
Today, Calabrian pine grows in Sicily in fragmented areas on the slopes of Mount Etna between 1000 and 2000 m a.s.l., covering approximately 3000 ha (
The largest area covered by Calabrian pine is in the Sila mountain range, where this species characterizes the forest landscape. Together with soil conservation and watershed protection, Calabrian pine has an important role in the local forest economy. In public properties (townships and State forests), management of Calabrian pine has usually been based on various types of clear felling (strip or patch), whereas on private properties, pine stands have generally been managed according to traditional and locally developed forms of selection cutting, which have contributed to the maintenance of pure pine stands with complex structures (
In order to understand the ecological and anthropogenic factors driving Calabrian pine distribution in the Sila mountain range, we address 4 objectives: (1) to analyze present-day Calabrian pine distribution; (2) to determine forest cover changes in the period 1935-2006; (3) to characterize climate and soil conditions where Calabrian pine grows; and (4) to identify the most significant variables that have led to species’ persistence, expansion or transition in the last 70 years. We outline the potential dynamics of Calabrian pine forests for the near future in relation to changes in ecological and anthropogenic drivers. Our aim is to provide a tool for the decision-making process in forest planning, specifically where conservation of pine habitat is the main management objective.
The Sila mountain range (16° 20′ 9.5″ - 16° 45′ 44.4″ E, 39° 00′ 30.9″ - 39° 33′ 45.4″ N) covers an area of approximately 1302 km2 (
Present-day forest cover was obtained from Corine Land Cover 2000 map (CLC 2000). We used the fourth thematic level for maps of Italy at a nominal scale of 1:100.000, which consist of 23 forest classes (
To identify the main drivers of change in land cover, we analyzed elevation, aspect, slope, climate, and soil type. Elevation data were extracted from a digital terrain model (DTM) available for Italy with a pixel size of 250 m (
Soil types according to the USDA soil classification system (
Shape and classification of forest polygons extracted from the CLC 2000 map were verified by visual interpretation of digital orthophotos acquired in 2006. We discriminated between pure and mixed forests using 75% of total crown cover as the threshold and the minimum Corine Land Cover (CLC) mapping unit. After this procedure, no forest changes were detected in the study area between 2000 and 2006. We then reclassified the CLC into 9 forest classes: Calabrian pine forest (both plantations and natural stands); mixed Calabrian pine-beech forest (pure natural pine stands alternating with beech on small areas); beech forest with sparse Calabrian pine (beech forest with different sized and shaped pine groups); beech forest; mixed fir-beech forest; chestnut forest; other coniferous forest; other broadleaved forest; and non-forest. In this way, we produced a forest map for the Sila mountain range updated to 2006.
The present-day distribution of forest classes was compared with the forest distribution reported in 1935 by the National Forest Service. A cross-tabulation analysis was used to detect land cover changes (
Geographical information system tools (overlay and summary) were used to produce graphs and scatter-plots for a visual interpretation of the ranges of ecological variables. Analyses were carried out for every ecological factor with all forest classes. Graphs were created to interpret the relationships between single ecological variables and forest classes. Scatter-plots were used to analyze the bi-dimensional relationships between elevation and aspect.
To identify the explanatory variables affecting Calabrian pine distribution we used a binary logistic regression analysis. This type of statistical model is used to describe the relationship between a binary (presence-absence or 1-0) dependent variable (
In our case, 3 logistic regressions were performed after analysis of land-use change to identify the most important explanatory variables for Calabrian pine persistence, expansion, and transition during 1935-2006 (
The vector data format was transformed into a raster data format using a pixel size of 250 m. Then we established a systematic, 1-km sampling network over the study area, and obtained a 916-pixel sample, which reduced the effect of spatial autocorrelation in the spatial distribution of observation points (
High collinearity between the independent variables was verified by the variance inflation factor (VIF), and the tolerance was verified using linear regression (
The independent variables were included in a logistic regression using the forward stepwise algorithm (entry 0.50, removal 0.10), based on the maximum likelihood criterion used to reduce the number of independent variables to the most explicative (
The distribution of forest classes in the Sila mountain range are presented in
Pine plantations and natural pine forests were not distinguished from each other in our study. Nevertheless, field observations and literature data show that natural pine forests grow between 800-1000 m a.s.l., and 1500-1600 m a.s.l., while plantations were located between 900 and 1200-1300 m a.s.l., as a consequence of the reforestation activities carried out from 1950 to 1970 (
Mixed Calabrian pine-beech forests and beech forests with sparse Calabrian pine were spatially adjacent to pure pine forests. These 2 forest classes reflect different beech forest dynamic stages: the former occurred where beech was partially replaced by pine due to intensive cuts in the past; the latter intermingled with pure beech forests especially in the southern, western, and central areas of the Sila mountain range. In the Sila Greca and the Sila Grande, beech forests with sparse Calabrian pine covered 6% and 13% of forest area, respectively; the area covered by this forest class in the Sila mountain range was equal to 13% of the total forest area (
Pure beech forests prevailed in the southern area and in the western and central areas from 1400-1500 m a.s.l. up to the mountain top (
The most important area for spontaneous fir vegetation was in the Sila Piccola (
At present, the area covered by Calabrian pine forests and mixed Calabrian pine-beech forests is equal to 36 100 ha and 20 221 ha, respectively (
Calabrian pine forests grew in an oceanic Mediterranean bioclimate from the lower limit (800 to 900 m a.s.l.) up to 1200 m a.s.l., and in a temperate oceanic bioclimate at higher elevations. Thermotype and ombrotype for 19 weather stations in the Sila mountain range are reported in
The average yearly precipitation exceeded 1000 mm with an increasing trend from the northern and eastern areas to the southern area of the Sila mountain range. The maximum precipitation was 1631 mm. The mean monthly precipitation between October and April exceeded 100 mm; most precipitation (>70-75%) occurred between autumn and spring (
The mean temperature ranged between 12.8 and 8.6 °C. The mean temperature at the lower (900 m a.s.l.) and higher limits (1650 m a.s.l.) of pine growth were 11.9 and 6.5 °C, respectively. The mean monthly temperature between January and March varied between -2.6 and 0 °C, while the mean monthly temperature in July and August ranged from 16 to 18 °C. Between November and April, the daily temperature reached a minimum value of -10 °C, while in summer reached a maximum of 30 to 32°C.
Pine forests grew on different soils (
In the central zone, Calabrian pine was found on soils originated on moderate slopes from altered granitic rocks of the following soil type: Humic Psammentic Dystrudept, Humic Dystrudept, and Typic Dystrudept (HPD_HD_TD). The 2 most common profiles were: (i) Oi-A-Bw-Cr, moderately deep with a skeleton varying from scarce to common; and (ii) Oi-A-Bw-BC-Cr, moderately deep with a common skeleton, a medium coarse texture, acidic, a moderate water reserve, and good drainage (
In the southern zone, Calabrian pine grew on slight slopes on soils originated from metamorphic rocks: Typic Dystrudept and Humic Dystrudept (TD_HD); and Humic Lithic Dystrudept, Humic Dystrudept, and Rock outcrop (HLD_HD_RO). In the TD_HD soils, the most common profiles were: (i) A-Bw-C, moderately deep, with frequent skeleton, high stoniness, medium texture, acid to sub-acid, moderate water reserve, and good drainage; (ii) Oi-A-Bw-BC, moderately deep, with common skeleton, medium texture, acidic, water reserve from moderate to high, and good drainage. The HLD_HD_RO had very thin soils with an A-R profile, frequent skeleton, coarse texture, acidic, very low water reserve, and fast drainage (
The relationships between each ecological variable (geographic zones, elevation classes, bioclimate, and soil types) and forest classes are shown in
The logistic regression analysis shows that among the 5 independent variables describing the study area, bioclimate, elevation, and soil type were the variables best explaining the persistence of Calabrian pine. The Hosmer-Lemeshow test revealed that the model accounted for significant portion of variance for the pine persistence in 1935-2006 (
For Calabrian pine expansion, the logistic regression indicates that the most important predictors were elevation and soil type. The Hosmer-Lemeshow test shows that the model fit was acceptable (
For Calabrian pine transition, only elevation (p<0.05) was included in the model with an odds ratio > 1,
Despite forest changes between 1935 and 2006, Calabrian pine covers today extensive areas in the Sila mountain range. Our results show that the most important ecological drivers of Calabrian pine distribution are elevation, soil type and bioclimate. The probability of pine-persistence during the period 1935-2006 is significantly correlated with all of these explanatory variables. As shown in
The likelihood of pine-expansion is significantly correlated with elevation and soil type. In the last 70 years, pine-expansion occurred especially below 1400 m a.s.l., on thin and very eroded soils with low water-holding capacity (HLD_HD_RO), and on moderately deep soils with good drainage, and water-holding capacity from moderate (TD_HD) to high (HPD_HD_TD -
Pine transition seems related to elevation, though our model prediction for transition did not adequately fit the data. Nonetheless, our results shows that transition prevails above 1200 m a.s.l. in moderately deep soils with high water content (HPD_HD_TD).
In the Sila mountain range there is historical evidence that intense soil erosion following forest destruction for agriculture and grazing started between the first and second millennium BC, with the Bruzi occupation (
Based on our results, 4 potential dynamics can be outlined for Calabrian pine forests in the Sila mountain range. These dynamics are driven by ecological (elevation, bioclimate, and soil) and anthropogenic factors.
Below 1200 m a.s.l., in an oceanic Mediterranean bioclimate:
(A) In thin and very eroded soils, with coarse texture, fast drainage, low cation-exchange capacity, and low water-holding capacity (HPD_HD_RO), pure Calabrian pine forests grow at the lower limit of their distribution. Under these conditions, Calabrian pine is expected to dominate also in the future following both management or disturbance factors such as fire (
(B) In moderately deep soils with a scarce to common skeleton, moderately coarse texture, acid pH, high water content, and good drainage (HPD_HD_TD), pure Calabrian pine forests may be maintained by forest management methods such as clear felling or the small group selection method described by
Above 1200 m a.s.l., in a temperate oceanic bioclimate:
(C) In thin and very eroded soils, with coarse texture, fast drainage, low cation-exchange capacity, and low water-holding capacity (HLD_HD_RO), pure and natural Calabrian pine forests curently prevail (
(D) In moderately deep soils with common skeleton and a superficial stoniness, medium texture, acid to sub-acid pH, moderate water-holding capacity and good drainage (TD_HD), pure Calabrian pine forests can be maintained by appropriate forest management methods (
Forest dynamics corresponding to (A) and (B) are expected for pine forests growing both in the northern and eastern sectors of the Sila mountain range (
In conclusion, identifying potential Calabrian pine dynamics on the basis of ecological and anthropogenic factors can help forest managers define priorities for forest planning, such as landscape and habitat conservation, and management goals for pine plantations in a forest-priority habitat of community importance. Furthermore, our results may help understanding the possible response and adaptation potential of Calabrian pine to changes both in management and in environmental factors, such as expected climate change.
We thank four anonymous reviewers for their helpful suggestions and comments on an early version of the manuscript.
Outline of a pedostratigraphic section near Lake Cecita of the Sila Plateau (modified from
The study area.
(a) Distribution of forest classes (2006) in the Sila mountain range. (b) Distribution of soil types (
Distribution of the most important forest classes (for each class, the area is a percentage of total forest class area) into geographical zones, elevation, bioclimate, and soil (see
Changes in Calabrian pine cover between 1935 and 2006.
Climate diagrams of 6 weather stations: Cecita, Longobucco, San Giovanni in Fiore, Camigliatello Silano, Vivoli, and Trepidò.
Scatter-plots depicting the relationships between elevation in m above sea level (m a.s.l.) and aspect (degrees): (a) Calabrian pine forest, (b) Mixed Calabrian pine-beech forest, (c) Beech forest with sparse Calabrian pine, (d) Beech forest, and (e) Mixed fir-beech forest.
Calabrian pine changes during 1935-2006 (for each type of change, the area is a percentage of the total change) in relation to elevation, bioclimate, and soil (see
Potential Calabrian pine forest dynamics according to ecological (bioclimate, elevation, and soil), and anthropogenic (forest fire and forest management) drivers in the Sila mountain range: (a) pure Calabrian pine forest naturally originated: (b) Calabrian pine forest that might evolve into pure or mixed broadleaved forest dominated by oaks; (c) Calabrian pine plantations; and (d) Calabrian pine forest naturally originated that can be either maintained or transformed in beech dominated forest or in mixed fir-beech forest, according to forest management methods (see
(A) Pure naturally originated Calabrian pine forest; (B) beech regeneration in a Calabrian pine forest (Photo: F. Iovino).
Potential Calabrian pine forest dynamics (including mixed Calabrian pine-beech forest) according to ecological (bioclimate, elevation and soil), and anthropogenic (forest fire and forest management) drivers in the Sila mountain range (see also
Dependent and independent variables used in the binary logistic regression. (*): HD_HPD = Humic Dystrudept, and Humic Psammentic Dystrudept; HD_TD = Humic Dystrudept and Typic Dystrudept; TD_HD = Typic Dystrudept and Humic Dystrudept; HPD_HD_TD = Humic Psammentic Dystrudept, Humic Dystrudept and Typic Dystrudept; HLD_HD_RO = Humic Lithic Dystrudept, Humic Dystrudept and Rock outcrop; HPD_HD_RO = Humic Psammentic Dystrudept, Humic Dystrudept and Rock outcrop.
Role | Variables | Source | Variable type | Notes |
---|---|---|---|---|
Dependent | Calabrian pine persistence | Analysis of land-use change (this study) | Binary | 1 = yes; 0 = no |
Calabrian pine expansion | Analysis of land-use change (this study) | Binary | 1 = yes; 0 = no | |
Calabrian pine transition | Analysis of land-use change (this study) | Binary | 1 = yes; 0 = no | |
Independent | Bioclimate | Own elaboration from monthly temperature and precipitation maps ( |
Binary | 1 = oceanic Mediterranean; 2 = temperate oceanic |
Elevation | DTM ( |
Ordinal | Reclassified into 100 m intervals | |
Aspect | Own elaboration from DTM | Categorical | 1 = N; 2 = NE; 3 = E; 4 = SE; 5 = S; 6 = SW; 7 = W; 8 = NW; 9 = flat | |
Slope | Own elaboration from DTM | Ordinal | Reclassified into 20% intervals | |
Soil type* | Soil map ( |
Categorical | 1 = HD_HPD; 2 = HD_TD; 3 = TD_HD; 4 = HPD_HD_TD; 5 = HLD_HD_RO; 6 = HPD_HD_RO |
Forest classes in the Sila mountain range.
Forest class | Sila Greca | Sila Grande | Sila Piccola | Total | ||||
---|---|---|---|---|---|---|---|---|
ha | % | ha | % | ha | % | ha | % | |
Calabrian pine forest | 7 348 | 39 | 20 722 | 26 | 8 030 | 25 | 36 100 | 28 |
Mixed Calabrian pine-beech forest | 1 495 | 8 | 14 059 | 18 | 4 668 | 15 | 20 221 | 16 |
Beech forest with sparse Calabrian pine | 791 | 4 | 7 257 | 9 | 5 125 | 16 | 13 173 | 10 |
Beech forest | 0 | 0 | 10 908 | 14 | 9 883 | 31 | 20 791 | 16 |
Mixed fir-beech forest | 0 | 0 | 0 | 0 | 1 187 | 4 | 1 187 | 1 |
Chestnut forest | 258 | 1 | 1 621 | 2 | 394 | 1 | 2 273 | 2 |
Other coniferous forest | 86 | 0 | 511 | 1 | 0 | 0 | 597 | 0 |
Other broadleaved forest | 3 357 | 18 | 788 | 1 | 217 | 1 | 4 362 | 3 |
Non-forest | 5 583 | 30 | 23 190 | 29 | 2 689 | 8 | 31 462 | 24 |
Total | 18 918 | 100 | 79 056 | 100 | 32 193 | 100 | 130 166 | 100 |
Forest cover changes of most important forest classes (in hectares) between 1935 (columns) and 2006 (rows).
2006 | 1935 | |||||||
---|---|---|---|---|---|---|---|---|
Calabrianpine | Otherconiferous | Beech | Chestnut | Otherbroadleaved | Mixedfir-beech | Non-forest | Total | |
Calabrian pine | 26 006 | 0 | 4 181 | 844 | 5 738 | 0 | 19 756 | 56 525 |
Other coniferous | 19 | 0 | 0 | 0 | 56 | 0 | 525 | 600 |
Beech | 6 756 | 0 | 158 383 | 944 | 2 638 | 0 | 7 806 | 33 981 |
Chestnut | 200 | 0 | 263 | 144 | 475 | 0 | 1 213 | 2 294 |
Other broadleaved | 725 | 0 | 0 | 94 | 2 531 | 0 | 963 | 4 313 |
Mixed fir-beech | 0 | 0 | 988 | 0 | 181 | 0 | 6 | 1.175 |
Non-forest | 7 181 | 0 | 1 600 | 306 | 3 469 | 0 | 18 722 | 31 278 |
Total | 40 888 | 0 | 228 694 | 2 331 | 15 088 | 0 | 48 991 | 130 166 |
Thermotype and ombrotype for 19 weather stations distributed across the study area. (E): Elevation; (P): average yearly precipitation; (T): average yearly temperature; (It): 10(T + M + m), where M = mean of maximum temperature of the cold months, and m = mean of minimum temperature of the cold months; (Ic): Tmax - Tmin, where Tmax = mean temperature of the warm months, and Tmin = mean temperature of the cold months. (Iov3): Ppsv3 / Tpsv3, where Ppsv3 = mean precipitation in June, July and August, and Tpsv3 = mean temperature in June, July and August; (Iov4): Ppsv4 / Tpsv4, where Ppsv4 = mean precipitation in May, June, July and August, and Tpsv4 = mean temperature in May, June, July and August). (*): 1 = oceanic Mediterranean, 2 = Temperate oceanic; (**): 1 = Meso-Mediterranean higher, 2 = Supra-Mediterranean medium, 3 = Supra-Mediterranean higher, 4 = Mountain higher, 5 = Supra-Mediterranean lower, 6 = Mountain lower. (***): 1 = Humid higher, 2 = Humid lower, 3 = Iper-humid lower.
Weatherstation | E(m a.s.l.) | P(mm) | T(°C) | Rivas Martinez | Bioclimate* | Thermotype** | Ombrotype*** | |||
---|---|---|---|---|---|---|---|---|---|---|
It | Ic | Iov3 | Iov4 | |||||||
Parenti | 830 | 1394 | 11.9 | 196.9 | 17.7 | 1.3 | - | 1 | 1 | 1 |
Pinutello C.C. | 1005 | 1136 | 11.1 | 162.4 | 17.9 | 1.7 | - | 1 | 2 | 2 |
S. Giovanni in Fiore | 1050 | 1153 | 10.7 | 151.3 | 17.9 | 1.7 | - | 1 | 2 | 2 |
Cecita (ex Acquacalda) | 1180 | 1083 | 8.9 | 113.4 | 17.0 | 1.6 | - | 1 | 3 | 2 |
Stratalati C.C. | 1200 | 1324 | 9.7 | 118.6 | 18.0 | 1.2 | - | 1 | 3 | 1 |
Savuto C.C. | 1205 | 1355 | 9.7 | 117.5 | 18.0 | 1.3 | - | 1 | 3 | 1 |
Lorica | 1290 | 1229 | 9.1 | 98.4 | 18.1 | 2.0 | 2.5 | 2 | 4 | 1 |
Camigliatello Silano | 1291 | 1631 | 9.3 | 100.5 | 18.1 | 2.0 | 2.9 | 2 | 4 | 3 |
Quaresima C.C. | 1300 | 1577 | 9.0 | 96.2 | 18.1 | 2.0 | 3.3 | 2 | 4 | 3 |
Nocelle | 1322 | 1205 | 8.9 | 91.3 | 18.1 | 2.0 | 2.6 | 2 | 4 | 1 |
Longobucco | 770 | 1258 | 12.8 | 215.1 | 17.7 | 1.3 | - | 1 | 1 | 2 |
Bocchigliero | 870 | 1257 | 12.1 | 192.6 | 17.7 | 1.3 | - | 1 | 5 | 2 |
Fiorenza | 1126 | 1272 | 10.3 | 135.2 | 18.0 | 2.1 | - | 2 | 6 | 2 |
Casa Pasquale | 1246 | 1398 | 9.4 | 108.3 | 18.1 | 1.8 | 2.3 | 2 | 4 | 1 |
Casa Jolanda | 1250 | 1615 | 9.4 | 107.4 | 18.1 | 2.2 | - | 2 | 4 | 3 |
Barberano C.C. | 1280 | 1346 | 9.2 | 100.7 | 18.1 | 2.2 | - | 2 | 4 | 1 |
Trepidò | 1295 | 1305 | 9.0 | 97.3 | 18.1 | 1.8 | 2.3 | 2 | 4 | 1 |
Vivoli C.C. | 1300 | 1311 | 9.0 | 96.2 | 18.1 | 1.8 | 2.9 | 2 | 4 | 1 |
Sculca | 1358 | 1318 | 8.6 | 83.2 | 18.1 | 1.8 | 2.8 | 2 | 4 | 1 |
Results of the binary logistic regression (n = 364) explaining pine-persistence during 1935-2006 (see
Independentvariable | B | SE | Wald | Exp (B) or Odds ratio | |
---|---|---|---|---|---|
Constant | 7.568 | 1.233 | 37.699 | 0.000 | 1934.750 |
Bioclimate (1) | -2.322 | 0.475 | 23.888 | 0.000 | 0.098 |
Elevation | -0.469 | 0.085 | 30.438 | 0.000 | 0.625 |
Soil (HD_HPD) | -0.977 | 0.449 | 4.745 | 0.029 | 0.376 |
Soil (HD_TD) | -2.267 | 0.647 | 12.275 | 0.000 | 0.104 |
Soil (TD_HD) | -1.543 | 0.360 | 18.328 | 0.000 | 0.214 |
Soil (HPD_HD_TD) | -0.235 | 0.345 | 0.464 | 0.496 | 0.790 |
Soil (HLD_HD_RO) | -1.658 | 0.361 | 21.073 | 0.000 | 0.190 |
Hosmer-Lemeshow test | 0.595 | - | - | - | - |
Results of the binary logistic regression (n = 308) explaining pine-expansion during 1935-2006 (see
Independent variable | B | SE | Wald | Exp (B) or Odds ratio | |
---|---|---|---|---|---|
Constant | 7.110 | 1.104 | 41.464 | 0.000 | 1224.098 |
Elevation | -0.644 | 0.089 | 52.812 | 0.000 | 0.525 |
Soil (HD_HPD) | 2.529 | 0.661 | 14.627 | 0.000 | 12.535 |
Soil (HD_TD) | 2.107 | 0.636 | 10.955 | 0.001 | 8.221 |
Soil (TD_HD) | 2.293 | 0.471 | 23.662 | 0.000 | 9.906 |
Soil (HPD_HD_TD) | 1.303 | 0.491 | 7.046 | 0.008 | 3.681 |
Soil (HLD_HD_RO) | 1.388 | 0.468 | 8.785 | 0.003 | 4.006 |
Hosmer-Lemeshow test | 0.908 | - | - | - | - |
Results of the binary logistic regression (n = 256) explaining pine-transition during 1935-2006 (see
Independent variable | B | SE | Wald | Exp (B) or Odds ratio | |
---|---|---|---|---|---|
Constant | -4.096 | 1.054 | 15.101 | 0.000 | 0.017 |
Elevation | 0.289 | 0.074 | 15.387 | 0.000 | 1.335 |
Hosmer-Lemeshow test | 0.000 | - | - | - | - |