**5. Conclusions**

Antimicrobial-resistant in *E. coli* has become a serious and complex problem worldwide in clinical treatment as well as in veterinary medicine. *E. coli* is intrinsically vulnerable to all clinically important antimicrobial agents, but it has great potential to accumulate resistance genes, through acquired resistance (HGT).

Acquired resistance plays an essential role in the acquisition of new properties, such as antimicrobial resistance, emphasizing the remarkable adaptive potential of *E. coli*. In addition, among all the MGEs, Transposons and plasmid have a significant role in the spread of antimicrobial resistance with high potential in resistance gene transmission. In *E. coli* plasmid and transposons mediated genes are involved in the spread of quinolone and *Mcr* resistance genes. The epidemiological study of AMR in *E. coli* revealed that *CTX-M* beta-lactamase and ST-131 clone have emerged as the main cause of hospital and community-acquired infections across the globe noteworthy in developing countries. This is being linked with lack of proper prescription of antibiotics and no such strict policy is in place. There are several challenges to implement sustainable and effective AMRs monitoring programs in Africa as well as in Asian countries to encounter the rapid dissemination of AMR. There is a dire need to support and develop antimicrobial policy, standard therapy guidelines for control of AMR in hospitals as well as in the community**.** To promote and regulate the balanced use of medicines and ensure proper patient care at all stages, antibiotics without doctor's prescription should be discouraged and ensure continuous access to essential medicines of guaranteed quality at the hospital and community.


**5. Conclusions**

When moved to and cultivated in a glasshouse having supplementary lighting, light intensities of *100* and *150* had significantly improved qualities of grafted tomato seedlings as compared to light intensity of *50*. However, considering improvement of quality of grafted seedlings, there were no significant differences between light intensities of *100* and *150*. Owing to enhanced photosynthesis, the upregulated gene expression levels of *PsaA* and *PsbA* and the corresponding protein expression levels indicated that light intensity of *100* might be the most suitable supplementary lighting to improve the quality of grafted tomato seedlings. That is, supplementary lighting with a light intensity of *100* would be the best choice to improve the quality of grafted vegetable seedlings when both power consumption and economic benefits are taken into account. This study provides new thoughts on supplemental lighting strategies that might be applied in greenhouses and lays the foundation for further studies on the utilization of light energy during early development stages of tomato seedlings.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4395/9/6/339/s1, Table S1: Sequences of Primers used in the expressions of photosynthesis-related genes.

**Author Contributions:** Conceptualization, B.R.J.; Methodology, B.R.J. and H.W.; Formal Analysis, H.W., J.Z., and J.H.; Resources, B.R.J.; Data Curation, H.W. and J.Z.; Writing—Original Draft Preparation, H.W.; Writing—Review & Editing, B.R.J.; Project Administration, B.R.J.; Funding Acquisition, B.R.J.

**Funding:** This research was funded by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry, Project No.319008-01. H.W., J.Z., and J.H. were supported by a scholarship from the BK21 Plus Program, Ministry of Education, Republic of Korea.

**Acknowledgments:** This study was carried out with support from the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (Project No.319008-01). H.W., J.Z., and J.H. were supported by a scholarship from the BK21 Plus Program, Ministry of Education, Republic of Korea.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*


**2. Node dynamic model for control**

controls for throttle/brake actuators of each vehicle [4]. These naturally lead to a hierarchical control structure, including an upper level controller and a lower level controller [5, 6]. The upper one is to retain safe and string stable operation, whereas the lower one is to track the

The upper level control of a platoon of CAVs has been investigated extensively. An earlier work done by Shladover [2] introduced many known control topics, among which the most famous is the concept of string stability. The string stability ensures that range errors decrease as propagating along downstream [7]. Stankovic et al. [8] proposed a decentralized overlap‐ ping control law by using the inclusion principle, which decomposes the original system into multiple ones by an appropriate input/state expansion. Up to now, many other upper level control topics have already been explored, including the influence of spacing policies, information flow topologies, time delay and data loss of wireless communications, etc.

The lower level controller determines the commands for throttle and/or brake actuators. The lower level controller, together with vehicle itself, actually plays the role of node dynamics for upper level control. Many research efforts have been attempted on acceleration control in the past decades, but still few gives emphasis on the request of platoon level automation. Most platoon control relies on one critical assumption that the node dynamics are homogeneous and approximately linear. Then, the node dynamics can be described by simple models, e.g. double-integrator [9, 10] and three-order model [3, 7, 8, 11]. This requires that the behaviour of acceleration control is rather accurate and consistent, which is difficult to be achieved. One is because the salient non-linearities in powertrain dynamics, both traditional [12, 13] and hybridized [14], and any linearization, will lead to errors; the other is that such uncertainties as parametric variations and external disturbances significantly affect the consistence of

One of the major issues of acceleration control is how to deal with non-linearities and uncer‐ tainties. The majority to handle non-linearities are to linearize powertrain dynamics, including exact linearization [15, 16], Taylor linearization [17] and inverse model compensation [12, 18]. Fritz and Schiehlen [15, 16] use the exact linearization technique to normalize node dynamics for synthesis of cruising control. After linearization, a pole placement controller was employed to control the exactly linearized states. The Taylor expansion approach has been used by Hunt et al. [17] to approximate the powertrain dynamics at equilibrium points. The gain-scheduling technique was then used to conquer the discrepancy caused by linearization. The inverse model compensation is widely used in engineering practice, for example [12] and [19]. This method is implemented by neglecting the powertrain dynamics. For the uncertainties, the majority rely on robust control techniques, including sliding model control (SMC) [19], **H**∞ control [20, 21], adaptive control [22–24], fuzzy control [25, 26], etc. Considering parametric variations, an adaptive SMC was designed by Swaroop et al. [19] by adding an on-line estimator for vehicle parameters, such as mass, aerodynamic drag coefficient and rolling resistance. Higashimata and Adachi [20] and Yamamura and Seto [21] designed a Model Matching Controller (MMC) based controller for headway control. This design used an **H**∞ controller as feedback and a forward compensator for a faster response. Xu and Ioannou [23] approximated vehicle dynamics to be a first-order transfer function at equilibrium points, and

desired acceleration by determining throttle/brake commands.

control behaviour.

40 Autonomous Vehicle

This chapter proposes a robust acceleration control method for consistent node dynamics in a platoon of CAVs. This design is able to offer more consistent and approximately linear node dynamics for upper level control of platoons even under large uncertainties, including vehicle parametric variation, varying road slop and strong environmental wind. The controlled node in the platoon is a passenger car with a 1.6 L gasoline engine, a 4-speed automatic transmission, two driving and two driven wheels, as well as a hydraulic braking system. **Figure 1** presents the powertrain dynamics. Its inputs are the throttle angle **αthr** and the braking pressure *P***brh**. Its outputs are the longitudinal acceleration *a*, vehicle velocity *v*, as well as other measurable variables in the powertrain. When driving, the engine torque is amplified by the automatic transmission, final gear, and then acts on two frontal driving wheels. When braking, the braking torque acts on four wheels to dissipate the kinetic energy of vehicle body.

**Figure 1.** Vehicle longitudinal dynamics.

## **2.1. Vehicle longitudinal dynamics**

For the sake of controller design, it is further assumed that (1) the dynamics of intake manifold and chamber combustion are neglected, and overall engine dynamics are lumped into a firstorder inertial transfer function; (2) the vehicle runs on dry alphabet roads with high road-tyre friction, and so the slip of tire is neglected; (3) the vehicle body is considered to be rigid and symmetric, without vertical motion, yaw motion and pitching motion; (4) the hydraulic braking system is simplified to a first-order inertial transfer function without time delay. Then, the mathematical model of vehicle longitudinal dynamics is

$$\begin{aligned} T\_{\rm es} &= \mathbf{MAP}(o\_{\rm c}, o\_{\rm thr}), T\_{\rm c} = \frac{1}{\tau\_{\rm c}s + 1} T\_{\rm cs}, J\_c \dot{o}\_{\rm c} = T\_{\rm c} - T\_{\rm p}, \\\\ T\_{\rm p} &= C\_{\rm TC} o\_{\rm c}^2, T\_i = K\_{\rm TC} T\_{\rm p}, T\_d = \eta\_t i\_{\rm g} i\_0 T\_i, o\_l = i\_{\rm g} i\_0 \frac{\nu}{r\_w}, \\\\ M\dot{\mathbf{v}} &= \frac{T\_{\rm d}}{r\_w} - \frac{T\_{\rm b}}{r\_w} - F\_{\rm i} - F\_{\rm i} - F\_{\rm i}, T\_{\rm b} = \frac{K\_{\rm b}}{r\_{\rm b}s + 1} P\_{\rm tat}, F\_{\rm i} = M\mathbf{g} \cdot \sin\{\rho\}, \\\\ F\_{\rm a} &= \text{sign}\left(\mathbf{v} + \mathbf{v}\_{\rm w}\right) C\_{\rm A} \left(\mathbf{v} + \mathbf{v}\_{\rm w}\right)^2, F\_{\rm f} = M\mathbf{g} \cdot f, \end{aligned} \tag{1}$$

where *ω***e** is the engine speed, *T***es** is the static engine torque, *τ***e** is the time constant of engine dynamics, **Te** is the actual engine torque, **MAP(.,.)** is a non-linear tabular function representing engine torque characteristics, *T***p** is the pump torque of torque converter (TC), *J***e** is the inertia of fly wheel, *T***t** is the turbine torque of TC, *C***Tc** is the TC capacity coefficient, *K***TC** is the torque ratio of TC, *i***g** is the gear ratio of transmission, *i***o** is the ratio of final gear, *η***T** is the mechanical efficiency of driveline, *r***w** is the rolling radius of wheels, *M* is the vehicle mass, *T***d** is the driving force on wheels, *T***b** is the braking force on wheels, *v* is the vehicle speed, *F***i** is the longitudinal component of vehicle gravity, *F***a** is the aerodynamic drag, *F***f** is the rolling resistance, *K***b** is the total braking gain of four wheels, *τ***b** is the time constant of braking system, *C***A** is the coefficient of aerodynamic drag, *g* is the gravity coefficient, *f* is the coefficient of rolling resistance, *ϕ* is the road slope and *v***wind** is the speed of environmental wind. The nominal values of vehicle parameters are shown in **Table 1**.

**Table 1.** Nominal parameters of vehicle model.

#### **2.2. Inverse vehicle model**

( )

w a

2

w

&

parameters are shown in **Table 1**.

of fly wheel, *T***t**

42 Autonomous Vehicle

( ) ( )

a wind A wind f

sign + +

**Symbol Units Nominal value**

*i*g – [2.71, 1.44, 1, 0.74]

*M* Kg 1300 *J*e kg·m2 0.21 *η*T – 0.89 *τ*e Sec 0.3 *i*o – 4.43

*r*w M 0.28 *K*b N·m/MPa 1185 *τ*b Sec 0.15 *C*A kg/m 0.2835 *f* – 0.02 *g* m/s2 9.81

**Table 1.** Nominal parameters of vehicle model.

w w b

es e thr e es e e e p e

= = = - +

t

**MAP , , , ,**

 hw

**,, , ,**

= - -- - = = × +

*T T K Mv F F F T P F Mg r r s*

*T T TJ T T s*

1 1

i a fb brk i

t

where *ω***e** is the engine speed, *T***es** is the static engine torque, *τ***e** is the time constant of engine dynamics, **Te** is the actual engine torque, **MAP(.,.)** is a non-linear tabular function representing engine torque characteristics, *T***p** is the pump torque of torque converter (TC), *J***e** is the inertia

ratio of TC, *i***g** is the gear ratio of transmission, *i***o** is the ratio of final gear, *η***T** is the mechanical efficiency of driveline, *r***w** is the rolling radius of wheels, *M* is the vehicle mass, *T***d** is the driving force on wheels, *T***b** is the braking force on wheels, *v* is the vehicle speed, *F***i** is the longitudinal component of vehicle gravity, *F***a** is the aerodynamic drag, *F***f** is the rolling resistance, *K***b** is the total braking gain of four wheels, *τ***b** is the time constant of braking system, *C***A** is the coefficient of aerodynamic drag, *g* is the gravity coefficient, *f* is the coefficient of rolling resistance, *ϕ* is the road slope and *v***wind** is the speed of environmental wind. The nominal values of vehicle

1

**, ,**

w

&

2

p TC e t TC p d T g 0 t t g 0

=== =

*v T C T K T T iiT ii r*

d b b

*F v v C v v F Mg f*

= = ×

( )

(1)

j

sin

w

**,, ,**

is the turbine torque of TC, *C***Tc** is the TC capacity coefficient, *K***TC** is the torque

One major challenge of acceleration control is the salient non-linearities, including engine static non-linearity, torque converter coupling, discontinuous gear ratio, quadratic aerodynamic drag and the throttle/brake switching. These non-linearities can be compensated by an inverse vehicle model. The inverse models of engine and brake are described by Eqs. (2) and (3), respectively [22, 31]. The design of the inverse model assumes that (i) engine dynamics, torque converter coupling, etc. is neglected; (ii) vehicle runs on dry and flat road with no mass transfer; (iii) the inverse model uses nominal parameters in **Table 1**.

$$T\_{\rm edes} = \frac{r\_w}{i\_{\rm g} i\_{\rm f} \eta\_{\rm r}} \left( M a\_{\rm des} + C\_{\rm A} \mathbf{v}^2 + M \mathbf{g} f \right), \alpha\_{\rm thads} = \text{MAP}^{-1} \left( a\_{\rm c}, T\_{\rm edes} \right), \tag{2}$$

$$F\_{\text{bdss}} = Ma\_{\text{des}} + C\_{\text{A}} \text{v}^2 + M \text{gf}, \\ P\_{\text{bdss}} = \frac{1}{K\_{\text{bdss}}} F\_{\text{bdss}}, \tag{3}$$

where *a***des** is the input for the inverse model, which is the command of acceleration control, *T***edes**, *α***thrdes**, *F***bdes** and *P***brkdes** are corresponding intermittent variables or actuator commands. Note that throttle and braking controls cannot be applied simultaneously. A switching logic with a hysteresis layer is required to determine which one is used. The switching line for separation is not simply to be zero, i.e. *a***des = 0**, because the engine braking and the aerodynamic drag are firstly used, and followed by hydraulic braking if necessary. Therefore, the switching line is actually equal to the deceleration when coasting, shown in **Figure 2**. The use of a hysteresis layer is to avoid frequent switching between throttle and brake controls.

**Figure 2.** Switching between throttle and brake controls.


**Abstract**

Most resource-constrained smallholder farmers rely on ethnoveterinary medicine to treat village chicken diseases. An in-depth literature review and empirical study was conducted to establish ethnoveterinary practices used in indigenous chicken health management. The study showed that most village chicken farmers were female (70%). Most farmers kept chickens for socio-economic purposes; food, status and income. Common birds' ailments treated and controlled using EVPs include Fowl Cholera; Pullorum; Diarrhea; Gumboro; Avian Influenza; Ngorok & Snot (Infectious Coryza); Bloody & watery diarrhea (Coccidiosis). Variety of materials were used to treat and control chicken diseases either as purely indigenous methods or in combination with conventional medicines. Among the practices, medicinal plants like *Aloe vera* emerged as the most commonly used botanical plant. More so, both literature and the empirical study, showed that farmers prepared and applied *A. vera* uniquely depending on the region and type of *A. vera*. Majority of remedies are used to treat more than one ailment. It is recommended that these practices be preserved and considered for new drug advancement and commercialization to promote cheaper and environmentally friendly options for poultry health management.

**Keywords:** chicken diseases, ethnoveterinary medicine, medicinal plants, village chickens, smallholder farmers

#### **1. Introduction**

The use of traditional medicinal practices in animal health management also referred to as Ethnoveterinary Practices (EVPs) dates back to the time in memorial [1, 2]. Since the onset of industrial revolution, the use of EVPS like medicinal plants and herbs began to decline as the preferred alternative to conventional medicine. Recently, their use is gaining attention and importance in modern day

practices that pursue organic solutions to health problems emanating from the use of the populous conventional medicine [3]. This shift is prompted by sustainability, comprehensiveness, cost effectiveness, availability and environmental friendliness of medicinal plants, traditional practices and herbs [4]. Resultantly, this necessitated a revisit to traditional grassroots medicines and paradigm shift to modern day animal health practices. This chapter critically reviews literature and empirical documents on medicinal plants or herbs and other traditional practices and approaches used in the control and treatment of indigenous village chicken diseases. Village chickens are domestic fowls of either indigenous type or any genetic stock, improved or unimproved [5, 6]. Almost every household in rural sector owns a chicken. The chickens play a significant role towards rural household income, food security and rural development [7]. Despite their substantial importance, they are significantly affected by diseases and parasites infections and infestations. Specifically, an inventory of common indigenous chicken disease types as well as medicinal plants or herbs resources used, their availability, parts used, harvesting methods, preparation and their therapeutic value are investigated and documented. This is key to promote awareness and avoid extinction of valuable information.


**6. Model scenarios: shifting population or death rates by education**

As was described in previous parts of the chapter, mortality in the Czech Republic is significantly different according to various levels of education. The aim of the following analysis was to transform the possible future changes of the education population structure or mortality rates into potential changes in temporary life expectancy. There are three different model scenarios defined with the purpose to illustrate the theoretically possible impacts on overall mortality caused by the changes in the education structure.

#### **6.1. Scenario 1**

The first model scenario could be indicated as a reference model. In this model, we suppose the education, gender, and age structures to remain unchanged. The population structure by education, gender, and age is that from the census 2011 (adjusted for January 1, 2011) and deaths rates those observed in the period 2009–2012. The population structure is graphically expressed by the population pyramid in **Figure 4** and mortality rates by education in **Figure 3a** and **b**.

The education structure from the 2011 census is characterized by a large proportion of people with only basic education at higher ages, especially for females. For males at the highest age groups, the proportion of basic education was only around 18% (in comparison to the proportion for females at the highest ages, which was almost 50%). At the youngest ages, the proportion of people with basic education decreased to only 7% for both the sexes. The largest proportion of males reached the vocational education (40–50% and this proportion is nearly invariant also at lower ages, except the youngest age group). For females, the vocational education was the second of importance at ages 65 and older. Women aged 30–49 most frequently had the secondary education. Moreover, for females, the proportion of basic education rapidly decreased from the highest age groups to the youngest ones and the proportion of secondary and university graduates increased with decreasing age. For males, almost the same trends can be depicted (see **Figure 4**).

#### **6.2. Scenario 2**

population pyramid, many important differences could be pointed out. There is a clear increase in the proportion of the population with tertiary education with decreasing age. In the youngest age groups, the proportion of tertiary educated reached 22% for females and 18% for males. On the other side, with increasing age, the proportion of the population with only basic education rises, especially for females. At the highest age group (75–79 years), almost 50% of females had only basic education. In the case of males, the proportion was significantly lower—less than 20% at the oldest age groups and around 10% for the middle ages, and for the youngest age groups, the proportion was even lower. The most common education level for males is the vocational one (almost 50%

**Figure 4.** Czech Republic, population structure by gender, age, and education from the census 2011, ages 30–79 years.

**6. Model scenarios: shifting population or death rates by education**

mortality caused by the changes in the education structure.

As was described in previous parts of the chapter, mortality in the Czech Republic is significantly different according to various levels of education. The aim of the following analysis was to transform the possible future changes of the education population structure or mortality rates into potential changes in temporary life expectancy. There are three different model scenarios defined with the purpose to illustrate the theoretically possible impacts on overall

share).

148 Senescence - Physiology or Pathology

The second model scenario is characterized by only changes among people with the lowest levels of education. In this scenario, we suppose that 60% of males with only basic education will be moved into the higher category—vocational education. This assumption, in fact, reflects the decrease in the proportion of basic education with decreasing age as well as the high importance of vocational education among males.

For females, we suppose that 60% of females with basic and vocational education will move into the secondary education. Also, in this case, the reason for this assumption could be found in the decrease of the proportion of basic education with decreasing age and moreover the significant increase of the proportion of females with secondary education.

The modeled education structure corresponding with the Scenario 2 is illustrated by the population pyramid in **Figure 5**. According to this model scenario, the proportion of basic education nearly diminishes. On the other side, the proportion of vocational education for males would significantly increase together with the share of secondary education for females would significantly increase. It could be hypothesized that especially for females, this shift in educational structure could have a significant effect on the overall mortality level because the mortality rates of secondary education are visibly lower in comparison to basic and vocational education (see **Figure 3b**). Also in case of males, the effect of modeled changes in education attainment could be expected in order to lower overall mortality, however, not as significant as for females because the modeled change affects only the proportion of basic education which was low already in the real population (see the model Scenario 1).

**Figure 5.** Population structure by gender, age, and education in the model Scenario 2, population aged 30–79 years.

#### **6.3. Scenario 3**

The last model scenario modifies all death rates by education while preserving population structure by education. In this model, we suppose shifts in sex age education‐specific mortality rates upwards by one level (basic = vocational, vocational = secondary, secondary = university, new university = 0.80\*university). The assumptions of this scenario are consistent with recent rapid changes in the educational structure of the Czech Republic, with a significant shift toward higher education levels. The proposed changes are supposed to impact overall mortality, above all in the case of males. The modeled shift according to this third scenario would lead to significant male mortality improvements due to their clear education gradient. The effect of change could be rather contradictory for females because women with vocational education experience slightly higher mortality compared to those with basic attainment (the above‐mentioned anomaly in female mortality gradient by education, see **Figure 3b**). The shift from vocational education to secondary as well as changes connected with shifts toward university education would lower mortality levels for both genders.


**Proof.**

1. Let Γ *φ* be derived in H*s*(**ND**PL).

> Then, Γ, ¬*φ* ⊥ is derived in H*s*(**ND**PL), by (HRAA).

> Hence, Γ, ¬*φ φ* is derived in H*s*(**ND**PL), by (¬HE2).

Since (*φ* → *ψ*) → ((*ψ* → *θ*) → (*φ* → *θ*)) is a classical tautology, Γ (*φ* → *ψ*) → ((*ψ* → *θ*) → (*φ* → *θ*)) is derivable in H*s*(**ND**PL).

Hence, by Modus Ponens, Γ (*ψ* → *θ*) → (*φ* → *θ*) is derivable in H*s*(**ND**PL). (\*)

Now, suppose that Γ, *φ θ* is derivable in H*s*(**ND**PL).

Then, by item 2, Γ *φ* → *θ* is derivable in H*s*(**ND**PL).

Therefore, Γ *ψ* → *θ* is derivable in H*s*(**ND**PL) by (→ HE2) applied to the latter and (\*). Then, finally, Γ, *ψ θ* is derivable in H*s*(**ND**PL), by (→ HE1).

4. Let Γ *φ* ↔ *ψ* be derivable in H*s*(**ND**PL).

> Suppose that Γ, *θ φ* is derivable in H*s*(**ND**PL).

Then, Γ, *θ* → *φ* is derivable in H*s*(**ND**PL), by claim 2. (\*\*)

Since (*φ* ↔ *ψ*) → ((*θ* → *ψ*) → (*θ* → *φ*)) is a classical tautology, Γ (*φ* ↔ *ψ*) → ((*θ* → *ψ*) → (*θ* → *φ*)) is derivable in H*s*(**ND**PL).

Therefore, Γ (*θ* → *ψ*) → (*θ* → *φ*) is derivable in H*s*(**ND**PL).

Hence, Γ, *θ* → *ψ* is derivable in H*s*(**ND**PL), by (→ HE2) applied to the latter and (\*\*).

Then, finally, Γ, *θ ψ* is derivable in H*s*(**ND**PL), by (→ HE1).

5. It suffices to prove the claim when *k* = 2 and then apply a straightforward induction.

Suppose Γ, *ψ*1, *ψ*2 *θ* is derivable in H*s*(**ND**PL).

Then, Γ (*ψ*1 → (*ψ*2 → *θ*)) is derivable in H*s*(**ND**PL), by applying claim 2 twice.

Since (*ψ*1 → (*ψ*2 → *θ*)) ↔ ((*ψ*1 ∧ *ψ*2) → *θ*) is a classical tautology, Γ (*ψ*1 ∧ *ψ*2) → *θ* is derivable in H*s*(**ND**PL), by claim 4.

Then, finally, Γ, *ψ*1 ∧ *ψ*2 *θ* is derivable in H*s*(**ND**PL), by (→ HE1).

The converse direction is similar.

Given a truth assignment *δ* : Prop → {f,t}, for any propositional variable *p* ∈ Prop, let us define *pδ* := *p* if *δ*(*p*) = t, else *pδ* := ¬*p*.

**Lemma 2.** *Let* Γ *be a finite set of propositional formulae and let* {*p*1, ..., *pn*} *contain all propositional variables occurring in formulae in* Γ*. Suppose δ is a truth assignment satisfying* Γ *and let* Γ*δ* = Γ ∪ {*pδ*1, ..., *pδn*}*. Then,* Γ*δ* ⊥ *is derivable in* H*s*(*ND*PL)*.*

**Proof.** By items 3 and 5 of Lemma 1, it suffices to prove the claim assuming that all formulae in Γ are transformed to equivalent ones in CNF and then replaced by the list of elementary disjunctions occurring as conjuncts in that CNF. Thus, without loss of generality, we can assume that Γ = {*γ*1, ..., *γk*}, where all *γi* are elementary disjunctions.

Take the satisfying assignment *δ*. By definition, *δ* also satisfies all literals in {*pδ*1, ..., *pδn*}. Furthermore, *pδ*1, ..., *pδn* ⊥ is an atomic refutation axiom, hence derivable in H*s*(**ND**PL).

Now, select from each *γi* in Γ a literal disjunct *αi* that is satisfied by *δ*. Then, *αi* must be in {*pδ*1, ..., *pδn*}. Hence, {*pδ*1, ..., *pδn*, *α*1, ..., *αn*} = {*pδ*1, ..., *pδn*}.

Therefore, *pδ*1, ..., *pδn*, *α*1, ..., *αn* ⊥ is an atomic refutation axiom, hence derivable in H*s*(**ND**PL). (\*) In addition, *αi* → *γi* is derivable in H*s*(**ND**PL), for each *i* = 1, ..., *n*. Therefore, by applying repeatedly item 3 of Lemma 1, we can replace successively each *αi* by *γi* in (\*), thereby eventually proving the claim.

By Anti-Monotonicity of , Lemma 2 immediately implies the following.

**Corollary 2.** *Let* Γ *be a finite satisfiable set of propositional formulae. Then,* Γ ⊥ *is derivable in* H*s*(*ND*PL)*.*

*5.2. Ł-Completeness and Ł-Adequacy of* H*s*(*ND*PL)

**Theorem 1.** *The hybrid derivation system* H*s*(*ND*PL) *is Ł-complete for the classical propositional logic* PL*.*

**Proof.** Due to the deductive completeness of **ND**PL, of which H*s*(**ND**PL) is a deductively conservative extension, it suffices to prove the R-completeness of H*s*(**ND**PL), i.e., that the refutation of every non-valid in PL sequent is derivable there. Let Γ |= *θ*. Then, there is a truth assignment *δ* satisfying Γ and falsifying *θ*. Therefore, *δ* satisfies Γ ∪ {¬*θ*}. By Corollary 2, it follows that Γ, ¬*θ* ⊥ is derivable in H*s*(**ND**PL). Then, by Rule (¬HE2), Γ, ¬*θ θ* is derivable in H*s*(**ND**PL). Finally, by the Anti-Monotonicity Rule Mon , we obtain that Γ *θ* is derivable in H*s*(**ND**PL). QED.

Proposition 3 and Theorem 1 together imply the following.

**Corollary 3.** *The hybrid derivation system* H*s*(*ND*PL) *is Ł-adequate for* PL *and, therefore, it provides a syntactic decision procedure for* PL*.*

The system H*s*(**ND**PL) and the ND-style refutation system developed in [2] are equivalent in terms of formal refutability, by virtue of the respective Ł-soundness and Ł-completeness results. Still, they are fairly different in style and it would be instructive to compare their proof-theoretic features, strengths and weaknesses, for the sake of possibly designing a better structured system of practical derivations based on H*s*(**ND**PL).

**Remark 8.** *Note that only some of the derived hybrid refutation rules were used in the proofs of Ł-soundness and Ł-completeness, hence the others must be derivable, or at least admissible, in the reduction of* H*s*(*ND*PL) *obtained by removing them. I leave the question of identifying a minimal Ł-complete subsystem of* H*s*(*ND*PL) *to future investigation. In particular, however, the rule* HRAA is *used in the proof of Lemma 1, hence that proof is not applicable to the system* H*s*(*ND*PL) *of Natural Deduction for the intuitionistic propositional logic* IPL*. Of course, it should not be applicable for* IPL*, e.g., because the refutation axiom* (*p* ∨ ¬*p*) *ought to be derivable there, while it is not in* H*s*(*ND*PL)*.*

#### **6. Towards a Meta-Proof Theory of Hybrid Derivation Systems**

Adding the relation for syntactic refutation and building systems of formal derivations that involve it together with the standard provability relation can be regarded as first steps towards internalising the notion of hybrid derivation into the logical language and then developing a theory for that notion that mirrors the proof theory of . In particular, derivability and refutability can now be treated on a par, as two related primitive concepts rather than as complementary ones where refutability is to be represented syntactically by non-provability. (Note, however, that, for any complete logic or theory, and applied to sequents of sentences are readily inter-reducible as complementary relations.) Thus, a proof theory of hybrid derivation systems emerges, extending and combining both the traditional proof theory and the theory of refutation systems.

Furthermore, the basic logical concepts of soundness, completeness, consistency, and satisfiability that relate syntax and semantics of a given logical system can now be all expressed and treated purely syntactically in terms of and . Thus, a "meta-proof theory" of hybrid derivation systems now emerges too, studying the meta-logic of these concepts respective to the given logical system L. Here, I will only set the stage for development of such meta-proof theory and will raise some generic questions, but I leave its systematic study to future work.

To begin with, let us add a new meta-symbol **F**, for "absurd", "falsum", or "contradiction", to the meta-language of hybrid derivation systems. Now, new hybrid derivation rules can be added to the thus extended framework, in order to reflect basic meta-properties of the given hybrid derivation system:

✄ **Cons**, stating consistency:

$$\frac{-\oint\_{\gamma} + \phi}{\mathbf{F}}$$

,

✄ "Ex (meta-)falso quodlibet", **EFQ**:

$$\frac{\mathbf{F}}{\!\!\!\!\!\!-\phi'} \qquad \frac{\mathbf{F}}{\!\!\!\!-\phi'} $$

✄ **Ł-Comp**: "Ł-completeness":

$$\begin{array}{c} \left[ \vdash \phi \right] \\ \vdots \\ \left[ \begin{array}{c} \text{F} \\ \hline \end{array} \right] \\ \end{array}$$

,

.

✄ Ł-**RAA**: "Ł-Reductio ad absurdum"

$$\begin{aligned} [\![\![\phi]\!] \!] \\ \vdots \\ \mathbf{F} \\ \hline \mathbf{\dot{}} \end{aligned} $$

Deductive completeness and Ł-completeness can now be *internalised* and stated as additional hybrid rules:

$$\begin{array}{ccccc} \left[\neg\Phi\right] & \left[\neg\Phi\right] & \left[\neg\Phi\right] & \left[\neg\Phi\right] \\ \vdots & \vdots & \vdots & \vdots & \vdots \\ \left[\neg\Psi\right] & \left[\neg\Psi\right] & \left[\neg\Psi\right] & \left[\neg\Psi\right] & \left[\neg\Psi\right] \\ \end{array}$$

$$\begin{array}{ccccc} \left(\textbf{Ded}\right) & \begin{array}{ccc} \neg\Psi & \vdash\psi & \neg\Psi \\ \hline \vdash\psi & \neg\Psi & \end{array} \end{array}$$

Some natural questions arise:

A next natural step would be to strengthen the meta-language even further, to a full-fledged logical meta-language, involving meta-variables and quantification over derivable and refutable formulae (or, sequents). Then, for instance, the semantic relationship between and can be postulated in the meta-language as Γ *φ* ⇔ ∼ Γ *φ* (where ∼ is the meta-negation). (Some initial steps into studying propositional meta-theory of acceptance and rejection of formulae (sequents with empty lists of premises) in a similar spirit can be found in [14].) I leave the general study of the meta-proof theory of hybrid derivation system to future work.

**Remark 9.** *It should be noted that what I call here 'meta-proof theory' has essentially been studied in great depth for theories of the arithmetic in the context of Gödel's incompleteness theorems and, more generally, in the context of* axiomatic theories of truth*; see [24]. However, the general meta-proof theory proposed here makes no assumptions about the expressiveness of the object logic regarding definability of truth predicates in it, or in general, and consequently it has a much wider scope.*

## **7. Conclusions**

#### *7.1. Some Applications of Hybrid Derivation Systems*

Arguably, hybrid derivation systems have a number of potential applications, both conceptual and technical, including:

## *7.2. Current and Future Work*

Due to space and time limitations, this paper leaves many open ends and related questions, some of which have already been mentioned so far. In addition, here are some topics of current and follow-up work:

**Funding:** This work was partly supported by research grant 2015-04388 of the Swedish Research Council.

**Acknowledgments:** I thank Sara Negri and Tom Skura, as well as the anonymous referees for careful reading, helpful suggestions, and some important corrections. I also thank the participants in the Refutation Symposium in Pozna ´n 2018 and in the CLLAM seminar at the Philosophy Department of Stockholm University for some useful comments on earlier versions of this work presented at these events.

**Conflicts of Interest:** The author declares no conflicts of interest.


**1. Introduction**

The European forests harbour biological wealth of international importance (circa 6,000 species are of conservation importance according to IUCN). Changes to come in climate are challenging science, governments, and local communities in order to sustain the health of its ecosystems, which will, in turn, also help protect the quality of life.

European climate system are supported by various factors such as soils, topography, available plant species. Some of these factors are contributing to both natural ecosystems and their fire regimes. Long-term patterns of temperature and precipitation determine the moisture available to grow the vegetation that fuels wildfires (Stephenson, 1998). Climatic inconsistency on inter-annual and shorter scales governs the flammability of these fuels (Westerling, 2003; Heyerdahl et al., 2001). Flammability and fire frequency in turn affect the amount and continuity of available fuels. Therefore, long-term trends in climate can have profound implications for the location, frequency, extent, and severity of wildfires and for the character of the ecosystems that support them (Westerling, 2006a). Human determined climatic change may, over a relatively short time period (< 100 years), give rise to climates outside anything experienced in Europe, since the establishment of an industrial civilization, currently sustaining a population that has increased approximately 270% since 1850. Changes in wildfire regimes driven by climate change are likely to impact ecosystem services that European citizens rely on, including carbon sequestration; water quality and quantity; air quality; wildlife habitat; and recreational facilities. In addition to climate change, the continued growth of continent's population and the spatial pattern of development that accompanies that growth are consequently affecting wildfire regimes through their impact on the availability and continuity of fuels and the availability of ignitions.

South East Europe ecosystems are a vast mosaic of different habitat types. The biodiversity patterns we encounter today are a result of millions of years of climatic and geologic change.

Climate Change: Wildfire Impact 3

impacts may have undermined the resilience of some species to adapt to the change (e.g., by lowering their overall population). Human land uses also may have disconnected the ecological connectivity in the landscape that would provide the movement corridor from the

This degree of alteration of ecological processes and jeopardise of native species will complete the transformation of the entire region to a "managed ecosystem"(Ioras, 2009). This reality will require that the local politicians articulate what the wanted future condition is for the area in question. Only with an informed thorough assessment of the current and future challenges confronting native species, and a clear articulation of ecological and socioeconomic goals, we will be able to manage South East Europe native species and systems

Climate increases wildfire risks primarily through its effects on moisture availability. Wet conditions during the growing season promote fuel—especially fine fuel—production via the growth of vegetation, while dry conditions during and prior to the fire season increase the flammability of the live and dead vegetation that fuels wildfires (Swetnam and Betancourt 1990, 1998; Veblen et al. 1999, 2000; Donnegan 2001). Moisture availability is determined by both precipitation and temperature. Warmer temperatures can reduce moisture availability via an increased potential for evapo-transpiration (evaporation from soils and surface water, and from vegetation), a reduced snowpack, and an earlier snowmelt. Snowpack at high altitude is an important mean of making water available as runoff in late spring and early summer (Sheffield et al. 2004), and a reduced snowpack and earlier snowmelt potentially lead to a longer, drier summer fire season in many mountain

For wildfire risks in most Eastern European forests, inter-annual variability in precipitation and temperature appear to be determinant on forest wildfire through their short-term effects on fuel flammability, as opposed to their longer-term affects on fuel production. One way of illustration this is with the use of average Palmer Drought Severity Index (PDSI). The Palmer Drought Severity Index (PDSI) was developed by Palmer (1965) based on monthly temperature and precipitation data as well as the soil-water holding capacity at that location to represent the severity of dry and wet spells over the U.S. The global PDSI data (Dai et al., 2004) consist of the monthly surface air temperature (Jones and Moberg 2003) and precipitation (Dai et al., 1998; Chen et al., 2002) over global land areas from 1870 to 2006.

The time series of the PDSI variations are determined by the mean values from all grid data from the selected area. The mean values are computed by means of the robust Danish method (Kegel, 1987). This method allows to detect and isolate outliers and to obtain accurate and reliable solution for the mean values. The global PDSI variations for the period 1870-2006 are between +1 in the beginning and -2 in 2002. The Palmer classification of drought conditions is in terms of minus numbers: between 0.49 and -0.49 - near normal conditions; -0.5 to -0.99 -incipient dry spell; -1.0 to -1.99 - mild drought; -2.0 to -2.99 moderate drought; -3.0 to -3.99 - severe drought; and -4.0 or less - extreme drought. The

The PDSI variations over the South-East Europe are determined for area between longitude 10°30' E and latitude 32.5°50' N (Fig.1). This area consists of 44 grids of the global PDSI data.

current to the future range.

through the transformation ahead.

**2.1 Climate and forest wildfire**

forests (Westerling, 2006b).

**2.1.1 Moisture, fuel availability, and fuel flammability**

These date is represented as PDSI values in 2.5x 2.5 global grids.

positive values are similar about the wet conditions.

Over years, populations of their native biota expanded and contracted in range – some at local scales, others at hemispheric scales, some up and others down slopes – to find and adapt to the local conditions that allowed them to persist to this day. During drier periods, for example, some species seek out the refuge of mountaintops that provided the conditions necessary for survival; on contrary during wetter periods, those species may have moved from those refuges to re-sort across the landscape that is now found in Europe.

What this dynamism demonstrates us is that change occurs at various temporal and spatial scales, and that while today's climate may be our baseline, our climate has not been and will not be static. It also highlights how critical connectivity is in our landscape: the extraordinary biological richness is to a great degree a product of species being able to shift in their range and adapt to changing climatic conditions. If that landscape connectivity is lost, or if the climate changes overtakes the ability of species to respond, or if populations are already reduced or stressed by other factors, species may be unable to survive through the climate changes to come.

In the case of many species and ecological processes, the effect of past and future land use change may induce significant stresses, that left unmanaged could see species to extinction. Some of these land use impacts may have a more significant impact than a changing climate. The challenge for South East Europe is to describe out the anticipated effects of past and future land use change from those of climate change – so that we can better plan our strategies to protect ecosystem health and conserve the native biodiversity for future generations.

This chapter endeavours to investigate what impact has the climate change, with specific reference to wildfire, on biodiversity and ecological processes in South East Europe and is presenting some considerations on how species native to the region will have to adapt.

### **2. Climate change**

A changing climate will interact with other drivers in pertaining ways and generate feedback cycles with significant consequences. The effects of habitat fragmentation on native species may be dependent on intra- and inter-annual variation in rainfall (Morrison, 2000); so changes in rainfall and development patterns may deepen impacts. Increasing fires, in combination with increasing nitrogen deposition as a result of ash deposition on soil, may facilitate invasive of non-native weeds that in turn increase fire risk. Decreasing water supplies due to human pressure may have negative effects on native plants and animals, like species found in rivers. Meanwhile, increased irrigation run-off from non-porous soil in an urbanized watershed can fundamentally alter hydrological regimes in other ways (White et al, 2002).

These threats may lead to population pressure for native species, and possibly lead to extinction. The urbanization stress on southern part of South East Europe has increased recently, and most of the direct impacts to resources have occurred in the recent past. This means that the indirect effects have yet to be seen. Once these changes have occurred, it is expect that in some areas of South East Europe (eg Croatia, Bulgaria) it will only accelerate. Compounding the ecological impacts of land use change is perhaps an unprecedentedly rapid change in climate. The "climatic envelopes" species need (the locations where the temperature, moisture and other environmental conditions are suitable for persistence) will shift. For many species, a changing climate is not the problem, per se. The problem is the pace of the change: the envelope may shift faster than species are able to follow. For some species, the envelope may shift to areas already changed to human land use. Human

Over years, populations of their native biota expanded and contracted in range – some at local scales, others at hemispheric scales, some up and others down slopes – to find and adapt to the local conditions that allowed them to persist to this day. During drier periods, for example, some species seek out the refuge of mountaintops that provided the conditions necessary for survival; on contrary during wetter periods, those species may have moved

What this dynamism demonstrates us is that change occurs at various temporal and spatial scales, and that while today's climate may be our baseline, our climate has not been and will not be static. It also highlights how critical connectivity is in our landscape: the extraordinary biological richness is to a great degree a product of species being able to shift in their range and adapt to changing climatic conditions. If that landscape connectivity is lost, or if the climate changes overtakes the ability of species to respond, or if populations are already reduced or stressed by other factors, species may be unable to survive through

In the case of many species and ecological processes, the effect of past and future land use change may induce significant stresses, that left unmanaged could see species to extinction. Some of these land use impacts may have a more significant impact than a changing climate. The challenge for South East Europe is to describe out the anticipated effects of past and future land use change from those of climate change – so that we can better plan our strategies to

This chapter endeavours to investigate what impact has the climate change, with specific reference to wildfire, on biodiversity and ecological processes in South East Europe and is

A changing climate will interact with other drivers in pertaining ways and generate feedback cycles with significant consequences. The effects of habitat fragmentation on native species may be dependent on intra- and inter-annual variation in rainfall (Morrison, 2000); so changes in rainfall and development patterns may deepen impacts. Increasing fires, in combination with increasing nitrogen deposition as a result of ash deposition on soil, may facilitate invasive of non-native weeds that in turn increase fire risk. Decreasing water supplies due to human pressure may have negative effects on native plants and animals, like species found in rivers. Meanwhile, increased irrigation run-off from non-porous soil in an urbanized watershed can fundamentally alter hydrological regimes in other ways (White

These threats may lead to population pressure for native species, and possibly lead to extinction. The urbanization stress on southern part of South East Europe has increased recently, and most of the direct impacts to resources have occurred in the recent past. This means that the indirect effects have yet to be seen. Once these changes have occurred, it is expect that in some areas of South East Europe (eg Croatia, Bulgaria) it will only accelerate. Compounding the ecological impacts of land use change is perhaps an unprecedentedly rapid change in climate. The "climatic envelopes" species need (the locations where the temperature, moisture and other environmental conditions are suitable for persistence) will shift. For many species, a changing climate is not the problem, per se. The problem is the pace of the change: the envelope may shift faster than species are able to follow. For some species, the envelope may shift to areas already changed to human land use. Human

protect ecosystem health and conserve the native biodiversity for future generations.

presenting some considerations on how species native to the region will have to adapt.

from those refuges to re-sort across the landscape that is now found in Europe.

the climate changes to come.

**2. Climate change**

et al, 2002).

impacts may have undermined the resilience of some species to adapt to the change (e.g., by lowering their overall population). Human land uses also may have disconnected the ecological connectivity in the landscape that would provide the movement corridor from the current to the future range.

This degree of alteration of ecological processes and jeopardise of native species will complete the transformation of the entire region to a "managed ecosystem"(Ioras, 2009). This reality will require that the local politicians articulate what the wanted future condition is for the area in question. Only with an informed thorough assessment of the current and future challenges confronting native species, and a clear articulation of ecological and socioeconomic goals, we will be able to manage South East Europe native species and systems through the transformation ahead.

#### **2.1 Climate and forest wildfire**

#### **2.1.1 Moisture, fuel availability, and fuel flammability**

Climate increases wildfire risks primarily through its effects on moisture availability. Wet conditions during the growing season promote fuel—especially fine fuel—production via the growth of vegetation, while dry conditions during and prior to the fire season increase the flammability of the live and dead vegetation that fuels wildfires (Swetnam and Betancourt 1990, 1998; Veblen et al. 1999, 2000; Donnegan 2001). Moisture availability is determined by both precipitation and temperature. Warmer temperatures can reduce moisture availability via an increased potential for evapo-transpiration (evaporation from soils and surface water, and from vegetation), a reduced snowpack, and an earlier snowmelt. Snowpack at high altitude is an important mean of making water available as runoff in late spring and early summer (Sheffield et al. 2004), and a reduced snowpack and earlier snowmelt potentially lead to a longer, drier summer fire season in many mountain forests (Westerling, 2006b).

For wildfire risks in most Eastern European forests, inter-annual variability in precipitation and temperature appear to be determinant on forest wildfire through their short-term effects on fuel flammability, as opposed to their longer-term affects on fuel production. One way of illustration this is with the use of average Palmer Drought Severity Index (PDSI). The Palmer Drought Severity Index (PDSI) was developed by Palmer (1965) based on monthly temperature and precipitation data as well as the soil-water holding capacity at that location to represent the severity of dry and wet spells over the U.S. The global PDSI data (Dai et al., 2004) consist of the monthly surface air temperature (Jones and Moberg 2003) and precipitation (Dai et al., 1998; Chen et al., 2002) over global land areas from 1870 to 2006. These date is represented as PDSI values in 2.5x 2.5 global grids.

The time series of the PDSI variations are determined by the mean values from all grid data from the selected area. The mean values are computed by means of the robust Danish method (Kegel, 1987). This method allows to detect and isolate outliers and to obtain accurate and reliable solution for the mean values. The global PDSI variations for the period 1870-2006 are between +1 in the beginning and -2 in 2002. The Palmer classification of drought conditions is in terms of minus numbers: between 0.49 and -0.49 - near normal conditions; -0.5 to -0.99 -incipient dry spell; -1.0 to -1.99 - mild drought; -2.0 to -2.99 moderate drought; -3.0 to -3.99 - severe drought; and -4.0 or less - extreme drought. The positive values are similar about the wet conditions.

The PDSI variations over the South-East Europe are determined for area between longitude 10°30' E and latitude 32.5°50' N (Fig.1). This area consists of 44 grids of the global PDSI data.

Climate Change: Wildfire Impact 5

Fig. 3. Variations of the PDSI for South-East Europe (source Chapanov and Gambis, 2010). Positive values of the index represent wet conditions, and negative values represent dry conditions. This is used here as an indicator of the moisture available for the growth and

This analysis included all fires over 400ha -large wildfires threshold (Running, 2006) that have burned since 1970, and account for the majority of large forest wildfires in South East Europe. The fires have been aggregated for each country using the European Forest Institute

**Country/Decade** 1970-1979 1980-1989 1990-1999 Albania 009 Austria 100 Bosnia 0 0 12 Bulgaria 0 2 29 Croatia 10 37 66 Czech 846 Cyprus 12 10 4 Greece 60 33 46 Hungary 5 5 15 Italy 58 50 102 Macedonia 0 0 25 Moldova 000 Romania 006 Slovakia 13 1 3 Slovenia 0 0 13 Yugoslavia 12 17 2 Table 1. Number of forest fire that affected an area over 400ha in South East Europe between

wetting of fuels.

1970-2000.

Note: 0 means no reported data

Database on Forest Disturbances in Europe (Table 1).

The maximal errors are below 0.08 and the mean value of the all PDSI points is 0.02 (Fig.2). The PDSI variations over the South-East Europe from Fig.3 show several severe wet and dry events.

Fig. 1. Area of South-East Europe between longitude 10°-30° E and latitude 32°.5-50° N.

Fig. 2. Number of the grid points and errors of PDSI for South-East Europe (source Chapanov and Gambis, 2010).

The maximal errors are below 0.08 and the mean value of the all PDSI points is 0.02 (Fig.2). The PDSI variations over the South-East Europe from Fig.3 show several severe wet and dry

Fig. 1. Area of South-East Europe between longitude 10°-30° E and latitude 32°.5-50° N.

Fig. 2. Number of the grid points and errors of PDSI for South-East Europe (source

Chapanov and Gambis, 2010).

events.

Fig. 3. Variations of the PDSI for South-East Europe (source Chapanov and Gambis, 2010).

Positive values of the index represent wet conditions, and negative values represent dry conditions. This is used here as an indicator of the moisture available for the growth and wetting of fuels.

This analysis included all fires over 400ha -large wildfires threshold (Running, 2006) that have burned since 1970, and account for the majority of large forest wildfires in South East Europe. The fires have been aggregated for each country using the European Forest Institute Database on Forest Disturbances in Europe (Table 1).

Table 1. Number of forest fire that affected an area over 400ha in South East Europe between 1970-2000.

Note: 0 means no reported data

Climate Change: Wildfire Impact 7

Comparing fire seasons for the earliest versus the latest third of years by snowmelt date, the length of the wildfire season (defined here as the time between the first report of a large fire ignition and last report of a large fire controlled) was 45 days (71 percent) longer for the earliest third than for the latest third. Sixty-six percent of large fires in South East Europe occur in early snowmelt years, while only nine percent occur in late snowmelt years. Large wildfires in early snowmelt years, on average, burn 25 days (124 percent) longer than in late snowmelt years. As a consequence, both the incidence of large fires and the costs of suppressing them are highly sensitive to spring and summer temperatures. Both large fire frequency and suppression expenditure appear to increase with spring and summer average temperature in a highly non-linear fashion. In the case of Albania, Bosnia Herzegovina and Romania (Hoxhaj, 2005; Alexandru et al, 2007; Ciobanu and Ioras ed, 2007) suppression expenditure in particular appears to undergo a shift near 15°C during of 2007 (Figure 4 and 5). Year 2007 was used as reference year due to the significant increase of wildfire (Figure 6) and also this year was known to have had a heat wave. Temperatures taken separately above and below that threshold are not significantly correlated with expenditures, but the

mean and variance of expenditures increase dramatically above it.

Fig. 4. The annual number of large forest fires in Albania, Bosnia and Romania versus

average March–August temperature in 2007.

In the South, the frequency of large wildfires peaks in Italy and Greece, in the East in Bulgaria and Croatia often ignited by lightning strikes before the summer rains wet the fuels (Swetnam and Betancourt, 1998). Since the lightning ignitions are associated with subsequent precipitation, it is possible that the monthly drought index may tend to appear to be somewhat wetter than conditions were at the time of ignition.

In the two northern countries - Slovak and Check Republic-conditions also tended to be drier than normal in the 70s: extended drought increased the risk of large forest wildfires in these wetter northern forests for fires above 1700 meters in elevation, the importance of surplus moisture in the preceding year was greatest for the southern countries. According to Swetnam and Betancourt (1998) moisture availability in predecessor growing seasons was important for fire risks in open conifer forests as fine fuels play an important role in providing a continuous fuel cover for spreading wildfires, but not in mixed conifer forests. Looking at the western part of South East Europe more generally, the moisture necessary to support denser forest cover tends to increase with latitude and elevation. Consequently, the shift in forest fire incidence as one moves from the forests of the SW to those of the NE is broadly consistent with a decreasing importance of fine fuel availability—and an increasing importance of fuel flammability— as limiting factors for wildfire as moisture availability increases on average.