Limitations of Species Distribution Models Based on Available Climate Change Data: A Case Study in the Azorean Forest
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data source
2.2.1. Species Data
2.2.2. Climate Data
2.2.3. Topographic Data
2.3. Modelling Approach
2.4. Model Validation
2.5. Model Projections
2.6. Variable Importance
3. Results
3.1. Summary of the Best Models
3.2. Relative Importance of EGVs
3.3. Present and Future Projections
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Environmental Variables | Code | Units | Data Type | Data Source |
---|---|---|---|---|
Annual minimum temperature | TMIN | °C | Climatic | CIELO model 1,2 |
Annual maximum temperature | TMAX | |||
Annual mean temperature | TM | |||
Annual minimum relative humidity | RHMIN | % | ||
Annual maximum relative humidity | RHMAX | |||
Total annual precipitation | PT | mm | ||
Elevation | DEM | m | Topographical | CIELO model DEM |
Aspect | ASP | ° | ||
Slope | SLP | % | ||
Curvature | CRV | |||
Flow accumulation | FLA | |||
Summer hill shade | SHS | |||
Winter hill shade | WHS |
Variables | Pico Island | Terceira Island | São Miguel Island | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Min. | Q1 | Median | Mean | Q3 | Max. | Min. | Q1 | Median | Mean | Q3 | Max. | Min. | Q1 | Median | Mean | Q3 | Max. | |
Present | ||||||||||||||||||
TMIN | 1.1 | 10.8 | 12.7 | 12.6 | 14.7 | 17.6 | 7.8 | 11.5 | 12.6 | 12.6 | 14.0 | 15.6 | 6.6 | 10.8 | 12.2 | 12.0 | 13.3 | 15.7 |
TMAX | 7.4 | 16.1 | 17.9 | 17.9 | 19.8 | 22.9 | 13.5 | 16.9 | 18.0 | 18.0 | 19.3 | 20.9 | 13.3 | 17.3 | 18.6 | 18.4 | 19.7 | 22.2 |
TM | 4.3 | 13.5 | 15.3 | 15.2 | 17.2 | 20.2 | 10.7 | 14.2 | 15.3 | 15.3 | 16.7 | 18.2 | 10.0 | 14.0 | 15.4 | 15.2 | 16.5 | 18.9 |
RHMIN | 67.8 | 81.1 | 87.5 | 86.9 | 93.2 | 100.0 | 77.6 | 84.3 | 89.4 | 89.1 | 93.3 | 100.0 | 73.7 | 84.9 | 89.0 | 89.0 | 93.1 | 100.0 |
RHMAX | 82.6 | 93.7 | 97.5 | 95.9 | 98.9 | 100.0 | 89.2 | 95.4 | 97.9 | 97.2 | 99.3 | 100.0 | 85.7 | 96.2 | 98.3 | 97.4 | 99.2 | 100.0 |
PT | 974.0 | 1397.0 | 2301.0 | 2510.0 | 3346.0 | 6817.0 | 1126.0 | 1168.0 | 1621.0 | 1831.0 | 2280.0 | 4258.0 | 1027.0 | 1105.0 | 1382.0 | 1531.0 | 1864.0 | 2988.0 |
Future | ||||||||||||||||||
TMIN | 4.5 | 13.7 | 15.5 | 15.4 | 17.4 | 20.4 | 10.8 | 14.3 | 15.4 | 15.4 | 16.8 | 18.4 | 9.6 | 13.7 | 15.1 | 14.8 | 16.1 | 18.5 |
TMAX | 10.7 | 18.9 | 20.6 | 20.6 | 22.5 | 25.7 | 16.3 | 19.6 | 20.7 | 20.7 | 22.0 | 23.6 | 16.3 | 20.1 | 21.4 | 21.2 | 22.4 | 25.0 |
TM | 7.6 | 16.3 | 18.1 | 18.0 | 20.0 | 23.1 | 13.5 | 17.0 | 18.0 | 18.0 | 19.4 | 21.0 | 12.9 | 16.9 | 18.2 | 18.0 | 19.3 | 21.8 |
RHMIN | 68.7 | 82.0 | 88.1 | 87.4 | 93.4 | 100.0 | 78.5 | 85.2 | 90.1 | 89.7 | 93.6 | 100.0 | 74.2 | 85.5 | 89.5 | 89.4 | 93.4 | 100.0 |
RHMAX | 82.5 | 93.5 | 97.4 | 95.8 | 98.9 | 100.0 | 89.2 | 95.3 | 97.9 | 97.2 | 99.3 | 100.0 | 85.7 | 96.2 | 98.2 | 97.4 | 99.2 | 100.0 |
PT | 934.5 | 1332.0 | 2280.0 | 2511.0 | 3397.0 | 7050.0 | 1086.0 | 1121.0 | 1579.0 | 1796.0 | 2243.0 | 4372.0 | 988.0 | 1064.0 | 1337.0 | 1488.0 | 1816.0 | 3021.0 |
Modelling Technique | Description | References |
---|---|---|
GLM: Generalized linear models | This is a generalization of ordinary linear regression, allowing for response variables that have normal (i.e., Gaussian) or non-normal error distribution models, such as the binomial and Poisson, through the use of a link function. Akaike’s or Bayesian information criteria (AIC or BIC) are used to select the most informative and parsimonious model. | [107,116,117] |
GAM: Generalized additive models | A non-parametric extension of GLMs, where the linear predictors correspond to smooth nonlinear functions of the predictive variables. Typically use the same underlying distributions for the response variable as GLMs. | [108,118] |
ANN: Artificial neural networks | Models are based on a stepwise progression of multivariate and univariate analyses, which are versatile in extracting information out of complex data, and which can be effectively applicable to classification and association. | [119,120] |
GBM: Generalized boosted models | The individual models consist of classification or regression trees. In an iterative process, a final model is built by progressively adding trees while re-weighting the data poorly predicted by the previous tree. | [112,117] |
RF: Random forest | Consists in an ensemble of unpruned classification or regression trees, created by using bootstrap samples of the training data and random feature selection in tree induction. | [113,121,122] |
CTA: Classification tree analysis | Operates by recursively parsing the training observations on a binary splitting measure applied to explanatory variables, such as spectral responses. | [114,123,124] |
SRE: Surface range envelope | It is a presence-only approach, based on the environmental conditions corresponding to the occurrence data, allowing the definition of the environment where a species can be found. Uses the extreme percentiles, as recommended by Nix or Busby. | [105,115,125] |
Variables | Code | Pico Island | Terceira Island | São Miguel Island | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PU | AM | MF | PU | AM | MF | PU | AM | MF | |||||||||||
EGV set | EGV set | EGV set | EGV set | EGV set | EGV set | EGV set | EGV set | EGV set | |||||||||||
Climatic | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |
Temperature | |||||||||||||||||||
Annual minimum temperature | TMIN | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | ||
Annual maximum temperature | TMAX | + | + | + | + | + | + | + | + | + | + | + | + | + | + | ||||
Annual mean temperature | TM | + | + | + | + | + | + | + | + | ||||||||||
Humidity | |||||||||||||||||||
Annual minimum relative humidity | RHMIN | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | ||
Annual maximum relative humidity | RHMAX | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | ||
Precipitation | |||||||||||||||||||
Total annual precipitation | PT | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | ||
Topographic | |||||||||||||||||||
Elevation | DEM | + | + | + | + | + | + | ||||||||||||
Aspect | ASP | + | |||||||||||||||||
Slope | SLP | + | + | + | + | + | + | + | + | ||||||||||
Curvature | CRV | + | |||||||||||||||||
Flow accumulation | FLA | + | + | + | |||||||||||||||
Summer hill shade | SHS | + | + | + | |||||||||||||||
Winter hill shade | WHS | + | + | + | + | + |
Species | Island | Scenario | Q1 | Q2 | Q3 | Q4 | Gain (+)/Loss (−) (%) |
---|---|---|---|---|---|---|---|
P. undulatum | Pico | P | 7599 | 2802 | 2891 | 31238 | −85 |
F | 20246 | 19107 | 2994 | 2183 | |||
Terceira | P | 23822 | 6035 | 4453 | 5506 | 7 | |
F | 22073 | 7083 | 4971 | 5689 | |||
São Miguel | P | 46414 | 7954 | 6687 | 13208 | −46 | |
F | 40194 | 23234 | 9470 | 1365 | |||
A. melanoxylon | Pico | P | 21668 | 3604 | 2735 | 16523 | −92 |
F | 40939 | 2098 | 231 | 1262 | |||
Terceira | P | 39604 | 150 | 38 | 24 | 174 | |
F | 28501 | 11145 | 170 | 0 | |||
São Miguel | P | 42701 | 8734 | 8332 | 14496 | −6 | |
F | 35152 | 17637 | 16498 | 4976 | |||
M. faya | Pico | P | 29802 | 1785 | 1740 | 11203 | −17 |
F | 28708 | 5091 | 4952 | 5779 | |||
Terceira | P | 25506 | 3628 | 2928 | 7754 | −32 | |
F | 25024 | 7494 | 5483 | 1815 | |||
São Miguel | P | 73236 | 413 | 270 | 344 | 5534 | |
F | 28465 | 11204 | 10069 | 24525 |
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Dutra Silva, L.; Brito de Azevedo, E.; Vieira Reis, F.; Bento Elias, R.; Silva, L. Limitations of Species Distribution Models Based on Available Climate Change Data: A Case Study in the Azorean Forest. Forests 2019, 10, 575. https://doi.org/10.3390/f10070575
Dutra Silva L, Brito de Azevedo E, Vieira Reis F, Bento Elias R, Silva L. Limitations of Species Distribution Models Based on Available Climate Change Data: A Case Study in the Azorean Forest. Forests. 2019; 10(7):575. https://doi.org/10.3390/f10070575
Chicago/Turabian StyleDutra Silva, Lara, Eduardo Brito de Azevedo, Francisco Vieira Reis, Rui Bento Elias, and Luís Silva. 2019. "Limitations of Species Distribution Models Based on Available Climate Change Data: A Case Study in the Azorean Forest" Forests 10, no. 7: 575. https://doi.org/10.3390/f10070575