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Predicting the Susceptibility to Pityokteines curvidens Using GIS with AHP and MaxEnt Models in Fir Forests

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Fatih Sivrikaya, Gonca Ece Özcan and Korhan Enez

Submitted: 30 November 2022 Reviewed: 09 December 2022 Published: 10 February 2023

DOI: 10.5772/intechopen.1001074

Analytic Hierarchy Process IntechOpen
Analytic Hierarchy Process Models, Methods, Concepts, and Applications Edited by Fabio De Felice

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Analytic Hierarchy Process - Models, Methods, Concepts, and Applications

Fabio De Felice and Antonella Petrillo

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Abstract

One of the most damaging insects to coniferous trees, especially pine and fir, is Pityokteines curvidens. It may cause significant tree death and alter the composition and structure of the forest. Early detection of forest stands vulnerable to bark beetle infestations and can help reduce the harmful effects of possible infestations. The main objective of the study was to compare the susceptibility maps of P. curvidens, utilizing the analytical hierarchy process and maximum entropy approaches. The study was conducted in the fir forests of the Küre Forest Enterprise in Türkiye. P. curvidens susceptibility maps were created using the stand structure, forest stand form, site index, slope, aspect, elevation, NDVI, and solar radiation as the eight key criteria. The accuracy of the maps based on the analytical hierarchy process and maximum entropy approaches was assessed using the receiver operating characteristic curves. The most crucial variables in both models’ predictions of P. curvidens susceptibility were found to be stand structure, NDVI, and elevation. Comparing the maps produced by the two models, the MaxEnt model gave more precise predictions than AHP. This study may make it easier for decision-makers to create susceptibility maps for fir engraver bark beetles.

Keywords

  • fir engraver beetle
  • Kazdağı fir
  • analytic hierarchy process
  • maximum entropy
  • susceptibility

1. Introduction

Bark beetles (Coleoptera: Curculionidae, Scolytinae), one of the most significant forest pests [1], are natural disturbances that have severe epidemics in the last century [2]. While only 2% of these species can cause epidemics in forests [3], endemic populations in small tree groups, and epidemic populations in large forest stand areas cause tree deaths [4, 5]. Host tree resistance prevents increasing of bark beetle populations [6]. However, attacks are easier on trees weakened by biotic and abiotic factors [7]. Epidemics significantly affects forest ecosystem values and causes deterioration of the functioning and characteristics of natural ecosystems [8, 9].

Abies (Miller) genus, which has two species and four subspecies in Türkiye [10], covers a total area of 511.703 ha [11]. Kazdağı fir (Abies nordmanniana ssp. equi-trojani (Asc. & Sint. ex Boiss.) Coode & Cullen), one of the endemic fir taxa for Türkiye, is economically and ecologically important and is very sensitive to climate variables [12]. Due to its declining population size and limited geographic range, the species has been given endangered status by the International Union for Conservation of Nature [13].

One of the most important of the bark beetles that damage fir is the fir engraver beetle (Pityokteines curvidens (Germar, 1824) Coleoptera: Curculionidae: Scolytinae) [14]. Although the main host of the species is Abies alba, it can also cause significant infestations in other Abies species (A. equi-trojani, A. sibirica, A. cephalonica, A. balsamea, A. bornmülleriana, A. borisii-regis, A. cilicica, A. nordmanniana, A. firme, A. sachalinensis, and A. fraseri). In addition, Pinus silvestris, P. brutia, P. strobus, Pseudotsuga menziesii, Picea orientalis, P.abies, Larix decidua, L. kaempferi, Cedrus libani, are other hosts [15, 16]. Abies ssp., Cedrus ssp., Pinus nigra, P. brutia and Picea pungens are hosts in Türkiye [17, 18, 19, 20].

P. curvidens, one of the important pests of fir forests in the Western Black Sea region of Türkiye [21, 22], prefers forests and monoculture stands where suitable habitat conditions are not available [23]. Although it is generally a secondary pest, it also becomes a primary pest depending on the increase in its population [24, 25]. It has been recorded that bark beetles caused a total damage of 2,353,057 m3 in 1987–2004 in this region [22].

Although it is not possible to prevent natural disasters, risks can be reduced by taking the necessary precautions [26]. Conducting risk assessment and modeling of the damages caused by bark beetles will help decision-makers in managing forest ecosystem [27]. In addition, there is a need for accurate and reliable estimation of pest distributions in forest ecosystems in today’s forestry [28]. Many different modeling techniques are used to estimate the distributions of bark beetle. Models that predict the geographical distribution of species are important for various applications in the field of ecology and conservation [29, 30]. Maximum Entropy (MaxEnt), which is one of the machine learning methods, is widely preferred in modeling species distributions and habitat suitability [31]. Spatial distribution of Dendroctonus mexicanus [28], potential distribution of D. ponderosae, D. brevicomis [32], Ips calligraphus, I. grandicollis [33], I. mannsfeldi [34] and I. aminutus [35], according to current and future climate scenarios and tree death events caused by bark beetles were estimated by MaxEnt [36].

The analytic hierarchy process (AHP), one of the multicriteria decision analysis methods (MCDA), is a widely used method in solving complex decision-making problems in many different disciplines [37]. While AHP is used effectively in forestry [38, 39, 40, 41, 42, 43], studies on bark beetles are limited [44, 45, 46].

In this study, AHP and MaxEnt approaches were used to predict the susceptibility of P. curvidens in Kazdağı fir forests. The accuracy of the models was calculated by the receiver operating characteristic (ROC) analysis. In the theory part of this book chapter, the bark beetle’s infestation, their effects on the ecosystem, and the importance of bark beetle susceptibility maps, according to AHP and MaxEnt models were explained. In the material and methods section, the conceptual framework, dataset, model parameters, and models used were described. In the result and discussion section, the model results obtained according to two different models were evaluated and the results were compared with the literature. In the last section, current bottlenecks were mentioned and suggestions for models were presented.

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2. Material and methods

2.1 Study area

Küre Forest Enterprise (FE), a case study area, is situated on the İsfendiyar mountain in the Kastamonu Regional Directorate of Forestry in the Western Black Sea region at 41° 42′ N and 33° 42′ E (Figure 1). 52% of the Küre FE (73694.9 ha) is covered with forest areas. The dominant tree species in the study area are Abies ssp., Fagus ssp., Quercus ssp., Pinus ssp., Alnus ssp., Carpinus ssp., and Ulmus ssp. Pure, and mixed fir forest stands in the study area is 29911.6 ha and 21% of the fir forests (6217.1 ha) were under attack by Pityokteines curvidens. In the study area, fir stands were infested intensively by Pityokteines curvidens between 2008 and 2017. The average elevation of the study area above sea level is 960 meters. The study area is in the Black Sea climate zone. The relative humidity level is high in all seasons. The weather is cool in summer and not very cold in winter, but rainy.

Figure 1.

The study area.

2.2 Parameters selection and dataset

A review of the literature helped determine the criteria that should be used to assess a forest stand’s vulnerability to Pityokteines curvidens infestation. Eight parameters were thought to be essential indicators for forecasting P. curvidens epidemics: stand structure, forest stand form, site index (m), slope (%), aspect (degree), elevation (m), NDVI, and solar radiation (WH/m2) [27, 45].

A digital forest stand map was acquired from the Küre FE [47] and was processed using ArcGIS 10.6 to create the database. The forest stand form, stand structure, and site index layers were created using forest stand maps. To evaluate the damage caused by P. curvidens, a field study was conducted in Küre FE between 2008 and 2017, the most intense period of damage. The beginning of an attack by P. curvidens on a tree can be noticed in the bark by scum emission coming from the reproductive galleries’ entryways. The entrance holes have a 1.5 mm diameter. It usually takes about 2 months for the normal canopy color to change to pale yellow and then reddish. The trees then turn yellow and brown, and eventually die old or weakened trees [48, 49]. Tress that displayed these symptoms were regarded as infected and stands with infested trees were also considered as infested stands. The digital forest stand map was used to create the infested stand map once the P. curvidens-infested forest stands were added to the database.

The digital elevation model (DEM) and Landsat images (Landsat 7 ETM+ and Landsat 8 OLI between 2008 and 2017) were downloaded from the U.S. Geological Survey website [50]. DEM data were used to create the elevation, aspect, slope, and solar radiation maps, and Landsat images were used to compute Normalized Difference Vegetation Index (NDVI). The following formula was used to calculate the NDVI:

NDVI=(NIR−Red)/(NIR+Red)E1

where NIR is the near-infrared and red is the red band.

The resolutions of all layers have to match to provide results that are more reliable. Therefore, using the bilinear resampling method in Spatial Analyst, the NDVI map at 30 m resolution was transformed to 25 m resolution. Table 1 shows the spatial database for the case study area.

Data descriptionSourceData typeResolution
DEMUSGSRaster25 m
Landsat 7 ETM+ and Landsat 8 OLIUSGSRaster30 m
Forest Stand MapKüre FEVector25 m

Table 1.

The data and its sources used in the study.

2.3 Analytical hierarchy process (AHP)

Analytical hierarchy process (AHP) is one of the multicriteria decision analyses [51, 52]. AHP analyzes descriptive, qualitative, and quantitative parameters with pairwise comparison and weighting. In AHP, parameters are compared with each other in pairs, a pairwise comparison matrix is created, and then the weights of these parameters are determined [53]. The AHP allows the interpretation of experience and knowledge by modeling not only the basic parameters but also the relationship of the sub-parameters with each other in a certain hierarchical structure. These features make the method more flexible, and its systematic, hierarchical, and adaptive features make the AHP method very popular nowadays, where environmental problems are constantly increasing. For this reason, it is used effectively in various disciplines, such as landslide mapping [54], flood hazard mapping [53], forest fire risk mapping [55, 56], and bark beetle mapping [45, 57]. The knowledge of certain specialists is used to evaluate the comparative magnitudes of variables through pairwise comparisons at the first stage in AHP. Coefficients ranging from 1/9 to 9 are given to compare the relative importance of parameters for each pair in pairwise comparison (Table 2) [54, 58]. Consistency Ratio (CR) is an important validation parameter for the judgment matrix in AHP. The Random Index (RI) should be known before proceeding to the calculation of the CR. RI values were gathered from Saaty [52, 58] in this study. CR was calculated according to Eqs. (2) and (3) to determine the weight of the parameters and to demonstrate their consistency. If the CR is <0.10, it can be said that the generated judgment matrix is consistent. It is necessary to reevaluate the pairwise comparison matrix when the CR is >0.1.

Importance scaleDefinitions of importanceDescription
1EqualThe objective is equally benefited by two actions.
3ModerateJudgment and experience are slightly preferred to one another.
5StrongJudgment and experience are strongly preferred to one another.
7Very strongJudgment and experience are very strongly preferred to one another.
9ExtremeKnowledge and judgment are the strongest conceivable arguments.
2, 4, 6, 8IntermediateWhen a compromise is necessary.

Table 2.

The description of pairwise comparison in AHP.

CI=λmax−n/n−1E2
CR=CI/RIE3

where ƛmax is the biggest value, n is the number of criteria, and CI is the consistency index.

2.4 Maximum entropy modeling (MaxEnt)

Maximum entropy (MaxEnt) is one of the machine learning methods and estimates the probability distribution of species habitats using presence-only data [59]. MaxEnt uses environmental variables as the independent variable and the distribution points of the species as the independent variable. MaxEnt 3.4.4 program was used to determine the potential distribution of fir engraver bark beetle [30, 59, 60]. To create the MaxEnt model, first of all, data sets were created. In this context, stand structure, forest stand form, site index, slope, aspect, elevation, NDVI, and solar radiation were used as variables. These variables have been converted to ASCII format. Stand structure, forest stand form, and site index were considered categorical data, while the others were considered continuous data. The jackknife test was used to determine the contribution rates of the variables in the model to be developed, and the AUC was used to evaluate the model performance. Implementation of the cross-validation method included 10 repetitions, a maximum of 5000 iterations, 1 regularization multiplier, and retention of other parameters at their default values. The logistic output grid format was chosen.

2.5 Fir engraver beetle susceptibility mapping

The risk of a bark beetle infestation is estimated by the fir engraver beetle susceptibility index (FEBSI). Infestation risk is increased by a high FEBSI and vice versa. The significance of each parameter in the AHP model was added up to estimate the FEBSI. All parameter layers were overlaid and the FEBSI values were computed in the AHP model using Raster Calculator tool in ArcGIS 10.6. FEBSI was calculated automatically, according to the selected parameters in MaxEnt model. Based on AHP and MaxEnt models, the susceptibility maps for the fir engraver bark beetle were created. FEBSI were categorized into four groups as low, moderate, high, and extreme based on the natural breaks’ categorization technique in ArcGIS. Two fir engraver beetle susceptibility maps were created according to AHP and MaxEnt models.

2.6 Validation of fir engraver beetle susceptibility mappings

One of the most important stages of predictive models is assessing their performance. Therefore, the accuracy of the AHP and MaxEnt-based beetle susceptibility maps was assessed with the Receiver Operating Characteristic (ROC) method in this study. One of the most popular methods for verifying model-based forecasting is the ROC curve analysis, which ranks the precision of probabilistic and deterministic models, as well as compares them [42]. It is a graphic technique for investigating the trade-off between specificity and sensitivity with the x-axis and the y-axis, respectively. The area under the curve (AUC) is a commonly used indicator of prediction accuracy in ROC curve analysis. The best outcome is to have the highest AUC score, which varies between 0 and 1. An AUC score of 1 indicates a perfect prediction of the model. Five categories are used to categorize the AUC score: poor (0.5–0.6), medium (0.6–0.7), good (0.7–0.8), very good (0.8–0.9), and perfect (0.9–1.0) [33, 42, 45, 61, 62, 63, 64]. For the development of the model, 75% of the infested stands were utilized as training data, while the remaining 25% were used as validation data.

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3. Results and discussion

Fir engraver beetle susceptibility maps were produced in a GIS environment using AHP and MaxEnt models. Many different parameters that affect the sensitivity of the beetle have been taken into account to prepare susceptibility maps. The CR was calculated as 0.053 and all pairwise comparison matrices were found to be consistent and satisfactory in the AHP method (Table 3). Stand structure, NDVI, solar radiation, and elevation were found to be more important than other parameters for fir engraver beetle susceptibility with weights of 2.431, 2.354, 1.443, and 1.315, respectively.

FactorsWeightsCRClassesWeightsCR
Stand structure2.4310.053Pure0.6670.085
Mixed0.333
Forest stand form0.555Even-aged0.2500.073
Uneven-aged0.750
Site index (m)0.689>37.60.0910.023
28.3–37.60.160
23.3–28.30.203
17.0–23.30.245
<17.00.301
Aspect0.778North0.4120.098
West0.281
East0.191
South0.116
Slope (%)0.4350–200.3120.046
20–400.490
>400.198
Elevation (m)1.315<10000.1220.089
1000–12000.333
1200–14000.390
>14000.155
NDVI2.354<0.250.0890.079
0.25–0.350.144
0.35–0.450.395
>0.450.372
Solar radiation (WH/m2)1.443<750,0000.1400.054
750,000–800,0000.177
800,000–850,0000.264
>850,0000.419

Table 3.

The weights and consistency ratios for all variables in AHP.

The percent contribution and permutation importance results of the estimation parameters based on the Jackknife test were given in Table 4 and Figure 2. Parameters were ranked based on percentage contribution as stand structure (74.4%), site index (10.9%), elevation (5.4%), and NDVI (2.6%). According to these results, stand structure, NDVI, and elevation were determined as the most important parameters in fir engraver beetle susceptibility in both models. This situation reveals that the stand structure, NDVI, and elevation should be taken into account while preparing the beetle risk map.

VariablePercent contributionPermutation importance
Stand structure74.465.7
Site index10.98.2
Elevation5.47.6
NDVI2.64.3
Solar radiation2.56.9
Slope2.34.8
Aspect1.62.3
Forest stand form0.30

Table 4.

Selected parameters and their percentage contribution.

Figure 2.

The Jackknife test of selected parameters for fir engraver beetle.

According to the ROC analysis for AHP model, the accuracy of the fir engraver beetle susceptibility mapping was calculated as 0.710, indicating a good prediction of the model. In the MaxEnt model, satisfactory results were also obtained for the fir engraver beetle susceptibility mapping with an AUC value of 0.739, which is higher than 0.5 of a random model (Figure 3). The maps produced according to both models gave reliable results. When both models were compared, it was revealed that the MaxEnt model produced more precise forecasts than AHP method. It can say that both AHP and MaxEnt give consistent results when preparing a beetle risk map.

Figure 3.

The ROC curve of the fir engraver beetle susceptibility maps created by (a) AHP and (b) MaxEnt models.

Fir engraver beetle susceptibility maps were created according to AHP and MaxEnt models (Figure 4). According to the AHP method, 5.1%, 14.2%, 27.4%, and 53.3% of the study area are low, moderate, high, and extreme susceptibility classes (Table 5). To verify the results, the maps of infested forest stands and fir engraver beetle susceptibility overlapped. It showed that 80.7% of the infested stands were high and extreme susceptibility classes. According to the MaxEnt model, 40.0% of the study area was in moderate susceptibility classes, while 28.5% is extreme susceptibility classes. 60.5% and 12.8% of infested stands were in extreme and high susceptibility classes, respectively. According to both models, fir forest stands are susceptible to fir engraver beetle.

Figure 4.

Pityokteines curvidens susceptibility maps produced by (a) AHP and (b) MaxEnt models.

ISCAHPMaxEnt
SAISSAIS
Area (ha)%Area (ha)%Area (ha)%Area (ha)%
Low4054.513.6315.95.15576.918.6384.36.2
Moderate10473.535.0882.514.211952.840.01272.720.5
High7729.725.81706.627.43852.112.9796.612.8
Extreme7653.925.63312.153.38529.828.53763.560.5
Total29911.61006217.110029911.61006217.1100

Table 5.

The class areas in susceptibility maps based on the AHP and MaxEnt models.

ISC: infestation susceptibility class, SA: study area, and IS: infested stands.

Our results are compatible with other studies. González-Hernández et al., [28] estimated the geographic distribution of Mexican pine bark beetle (Dendroctonus mexicanus) in conifer forests in Mexico using the MaxEnt model. They used bioclimatic (temperature, precipitation, etc.), and topographic variables (altitude, slope, and aspect) for bark beetle mapping. The potential distribution of the bark beetle was reliable, according to the AUC (0.93). The key component in identifying suitable bark beetle habitats was found to be stand structure. Sarıkaya et al. [34] tried to model the current and future habitats of Ips mannsfeldi in Türkiye using MaxEnt. RCP4.5 and RCP8.5 climate change scenarios, as well as bioclimatic factors were used to estimate bark beetle habitats. They stated that elevation was the most important variable and estimated habitat distribution showed high accuracy with an AUC (0.88). Sivrikaya et al., [45] produced I. sexdentatus susceptibility map based on AHP, Frequency Ratio (FR), and Logistic Regression (LR) models. They stated that crown closure was the most important variable for three models. The accuracy of I. sexdentatus susceptibility maps for the LR, FR, and AHP models `was evaluated using the ROC curve, and the FR model provided a more precise estimate than the others.

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4. Conclusion

The invasion of bark beetles is influenced by a variety of biotic and abiotic variables. Early detection of forest stands vulnerable to bark beetle infestations can help reduce the harmful effects of possible infestations. Thus, it is ensured that more accurate decisions are taken for future of forests in the understanding of sustainable management. In this study, susceptibility maps of Pityokteines curvidens were created using analytical hierarchy process (AHP) and maximum entropy (MaxEnt) approaches and their accuracy was compared. According to these results, stand structure, NDVI, and elevation were determined as the most important parameters in fir engraver beetle susceptibility in both models. Comparing the maps created from both models, the MaxEnt model produced more accurate predictions than the AHP. However, it was seen that susceptibility maps based on AHP have also acceptable accuracy. After all, according to both models, fir forest stands are susceptible to the fir engraver beetle and the key component in identifying suitable bark beetle habitats was found to be stand structure.

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Acknowledgments

We would like to say thank you to the Küre Forest Enterprise for providing the forest stand map and related fir engraver beetle information.

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Written By

Fatih Sivrikaya, Gonca Ece Özcan and Korhan Enez

Submitted: 30 November 2022 Reviewed: 09 December 2022 Published: 10 February 2023

© The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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