AMERICAN METEOROLOGICAL SOCIETY - AMS Journals

MARCH 2017

TEQJA ET AL.

615

A Study of the Impacts of Climate Change Scenarios on the Plant Hardiness Zones of Albania ZYDI TEQJA AND ALBERT KOPALI Agricultural University of Tirana, Koder Kamez, Tirana, Albania

ZAMIR LIBOHOVA National Soil Survey Center, Natural Resources Conservation Service, U.S. Department of Agriculture, Lincoln, Nebraska

PHILLIP R. OWENS Department of Agronomy, Purdue University, West Lafayette, Indiana, and U.S. Department of Agriculture Agricultural Research Service Dale Bumpers Small Farms Research Center, Booneville Arkansas (Manuscript received 14 March 2016, in final form 6 July 2016) ABSTRACT Maps of plant hardiness zones are useful tools for determining the extreme limits for the survival of plants. Exploration of projected climate change effects on hardiness zones can help identify areas most affected by climate change. Such studies are important in areas with high risks related to climate change, such as the Mediterranean Sea region. The objectives of this study were to (i) map plant hardiness zones for Albania and (ii) assess the projected effects of climate scenarios on the distribution of hardiness zones. Hardiness zones were affected by IPCC AR5 climate scenarios. The most extreme hardiness zone (6a) disappeared while a new, warmer zone (10b) appeared, reflecting rising temperature trends during the cold season. The shifts in spatial distribution of hardiness zones may represent opportunities for introducing new species to Albanian agriculture and forestry; however, the introduction of new species would require further studies on the variability of plant hardiness zones at local scales.

1. Introduction Many environmental factors control the survival and distribution of plant species. The most important are often weather-related ones, such as minimum and maximum temperatures, frost frequency and duration, precipitation amount and distribution, cloud cover, and relative humidity (Ouellet and Sherk 1967; McKenney et al. 2007; Pliscoff et al. 2014). There are several ways to assess the survival and adaptability of plants to climatic fluctuations, including the use of plant hardiness zones

Denotes Open Access content.

Corresponding author address: Zamir Libohova, USDA–NRCS National Soil Survey Center, 100 Centennial Mall North, Federal Building, Room 152, Lincoln, NE 68508. E-mail: [email protected] DOI: 10.1175/JAMC-D-16-0108.1 Ó 2017 American Meteorological Society

(severity classes) based on absolute annual minimum temperatures (USDA model) and heat zones (American Horticultural Society 1997) based on the average number of days each year with temperatures above 308C as well as broader climatic zone classification systems (Daly et al. 2008, 2012; Widrlechner et al. 2012; McKenney et al. 2014). However, the method using plant hardiness zones based on minimum temperatures is one of the most widely adopted in temperate regions because it reflects the long-term survival of perennial plants and the degree to which they can endure low winter temperatures (Widrlechner et al. 2012). Because of the simplicity of this method, which relies on just one climatic factor, the maps of plant hardiness zones are useful and practical (McKenney et al. 2007; Widrlechner et al. 2012). Hardiness zone maps based on the USDA model have been developed for many parts of the world, including Australia (Dawson 1991),

616

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY

VOLUME 56

FIG. 1. Physiographic regions of Albania and locations of climate stations.

China (Widrlechner 1997), Europe (Heinze and Schreiber 1984), Japan (Hayashi 1990), South Africa (Pienaar 1996), Ukraine (Widrlechner et al. 2001), and Turkey (Yalçın and Tolunay 2012). Climatic constraints represent an important link between prevailing meteorological conditions, as measured at weather stations, and their specific relevance for land-use activities (Stone and Meinke 2006). A change in climatic constraints implies that new opportunities for and/or risks to land-use management could develop. Therefore, an exploration of climate change scenarios on plant hardiness zones could identify geographic areas where the range of land-use options might be expected to change and help farmers and land managers select the best options for adapting to the expected changes. For

many years, most studies related to increased concentrations of atmospheric greenhouse gases have focused on impacts under average climate scenarios. During the last decade, however, changes in climatic variability and extreme weather events have received increased attention (Brown et al. 2008). Such studies are especially important in areas with a high risk of climate change, such as the Mediterranean region (Resco de Dios et al. 2007). Albania is a small country, approximately 28 000 km2, but because of its mountainous, high-relief terrain, proximity to the Mediterranean Sea, and overall atmospheric circulations, it encompasses diverse climate conditions (Paxian et al. 2015). This climatic diversity provides unique opportunities for the development of diverse horticultural production systems. According to

MARCH 2017

TEQJA ET AL.

TABLE 1. Mean temperature increase and range (8C) for future climate scenarios according to IPCC AR5 (IPCC 2013; Moss et al. 2008, 2010).

Scenario

2046–65 Mean and predicted range

2081–2100 Mean and predicted range

RCP3.6 RCP4.5 RCP6.0 RCP8.5

1.0 (0.4–1.6) 1.4 (0.9–2.0) 1.3 (0.8–1.8) 2.0 (1.4–2.6)

1.0 (0.3–1.7) 1.8 (1.1–2.6) 2.2 (1.4–3.1) 3.7 (2.6–4.8)

the climate classification of Albania developed by the Hydrometeorological Institute (Hydrometeorological Institute 1988; Jaho et al. 1988), there are 4 major climate zones and 13 subclimate zones. Although this is one of the most widely used climate classifications in Albania, its practical application is difficult for two major reasons. First, the interpretation of this classification for economic purposes, such as production agriculture and forestry, is difficult. Second, this classification is not compatible with other international classifications and so cannot facilitate transferability of horticultural species across borders. In addition, the existing classification is based on outdated climate data and does not reflect current and future trends under different projected climate scenarios (Moss et al. 2008, 2010). Several studies have been undertaken to better understand the Albanian climate

617

for agricultural purposes (Teqja 1994; Kopali et al. 2012). Teqja (1994) elaborated a method to assess the variability of crop yields caused by climatic factors and made a classification of Albanian territory based on this indicator. Kopali et al. (2012) identified 26 homogenous climatic zones of Albanian territory based on historic data of monthly precipitation, temperatures, and relative humidity. Both authors used 1988 historic climatic data compilation from the Hydrometeorological Institute of Albania (Hydrometeorological Institute 1988; Jaho et al. 1988). The objectives of this study were to (i) develop a classification of plant hardiness zones (severity classes) for Albania that is based on absolute annual minimum temperatures compatible with international classifications and (ii) assess the impact of future climate scenarios on the distribution and extent of plant hardiness zones and the distribution of some horticultural species.

2. Materials and methods a. Study area characteristics Albania is located in southeastern Europe and has a total area of 28 748 km2 (Fig. 1). It has a diverse climate that varies within short distances because of its mountainous, high-relief terrain. The mean elevation is about 750 m but varies from 0 to 2700 m above sea level. The western coastal area is

FIG. 2. All atmospheric CO2 forcing concentrations in parts per million by volume (ppmv) according to four RCPs. (Source: https://upload.wikimedia.org/wikipedia/commons/thumb/ 3/3e/All_forcing_agents_CO2_equivalent_concentration.png/220px-All_forcing_agents_CO2_ equivalent_concentration.png.)

618


relatively flat and is dominated by a Mediterranean climate, with mild winters and hot summers. In this area, the mean winter temperature is about 78C and the mean summer temperature is about 248C but can range to more than 308C, especially in the southwestern part. The mountainous areas, which make up close to two-thirds of the territory, are influenced by a more continental climate. Precipitation varies greatly between regions. The mean annual precipitation for the coastal areas ranges from 100 to 150 cm and falls mostly during fall and winter. The mountainous areas, especially the Albanian Alps, can receive as much as 250 cm of precipitation, with 20%–30% of it in the form of snow.

b. The analysis of climate impact on plant hardiness indicators The absolute monthly minimum temperature during the 1970–85 period was recorded in February (213.78C). Also, the month of February had the highest number of days with temperatures below 2128 and 2138C as compared with January, which was the second coldest month on record. Initially, an assessment was conducted to assess differences between calculated minimum mean February temperature and actual absolute minimum February temperature for 167 climate stations during the period 1970–85 (described in detail in section 2e). This was necessary to allow us to adjust WorldClim (http:// www.worldclim.org/; Hijmans et al. 2005) minimum temperature data predictions for the 1950–2000 period and climate scenarios to the absolute minimum temperature. The adjustment allowed for a comparative analysis of the influence of climate scenarios on the plant hardiness indicators. Ideally, a direct comparison between actual annual minimum absolute temperatures and predicted absolute minimum temperatures from climate scenarios would have been used. However, only mean monthly minimum temperatures from the WorldClim (Hijmans et al. 2005) data were available. Also, the approach based on the current climate data does not imply that the same exact relationships between mean minimum and absolute minimum temperatures will be applicable for the future climate scenarios. Increased climate fluctuation and uncertainty (Deser et al. 2012a,b) will most likely affect the strength and variability of this relationship. This analysis was followed by comparisons between different forcing scenarios from downscaled IPCC Fifth Assessment Report (AR5) phase 5 of the Coupled Model Intercomparison Project (CMIP5) data at 30-s resolution from the WorldClim data (IPCC 2013; Moss et al. 2008, 2010). The data provided four different scenarios of representative concentration pathways (RCPs) of greenhouse gas (GHG) (rcp26, rcp45, rcp60, and rcp85) for two time periods (Table 1): 2050 (average for 2041–60) and 2070 (average for

VOLUME 56

TABLE 2. Severity classes based on plant hardiness zones using air temperatures. Zone 0 1 2 3 4 5 6 7 8 9 10 11 12

From a b a b a b a b a b a b a b a b a b a b a b a b a b

To

,253.98C (2658F) 253.98C (2658F) 251.18C (2608F) 251.18C (2608F) 248.38C (2558F) 248.38C (2558F) 245.68C (2508F) 245.68C (2508F) 242.88C (2458F) 242.88C (2458F) 2408C (2408F) 2408C (2408F) 237.28C (2358F) 237.28C (2358F) 234.48C (2308F) 234.48C (2308F) 231.78C (2258F) 231.78C (2258F) 228.98C (2208F) 228.98C (2208F) 226.18C (2158F) 226.18C (2158F) 223.38C (2108F) 223.38C (2108F) 220.68C (258F) 220.68C (258F) 217.88C (08F) 217.88C (08F) 215.08C (58F) 215.08C (58F) 212.28C (108F) 212.28C (108F) 29.48C (158F) 29.48C (158F) 26.78C (208F) 26.78C (208F) 23.98C (258F) 23.98C (258F) 21.18C (308F) 21.18C (308F) 11.78C (358F) 11.78C (358F) 14.48C (408F) 14.48C (408F) 17.28C (458F) 17.28C (458F) 1108C (508F) 1108C (508F) 112.88C (558F) .112.88C (558F)

2061–80) based on forecasts (Fig. 2). The RCPs (rcp2.6, rcp4.5, rcp6, and rcp8.5) described four possible ranges of radiative forcing values in 2100 relative to preindustrial values [12.6, 14.5, 16.0, and 18.5 W m22 (Weyant et al. 2009)]. The dataset provided four climate parameters: (i) tn—monthly average minimum temperature (8C 3 10), (ii) tx—monthly average maximum temperature (8C 3 10), (iii) pr—monthly total precipitation (mm), and (iv) bc— bioclimatic variables (not used in this study). For this study, we used mean monthly average minimum temperature for February, which has been historically the coldest month in Albania (Hydrometeorological Institute 1988; Jaho et al. 1988).

c. Comparisons between the 1950–2000 period and future climate scenarios Based on the elevation gradient and topography, the study area was divided into five major physiographic regions: (i) western low plains (0–125 m MSL), (ii) western midplains (126–250 m MSL), (iii) hills and foothills (251–500 m MSL), (iv) mountain foothills (501– 1000 m MSL), and (v) mountains (.1001 m MSL). Based on the influence of low temperatures on plant community development and distribution, the mean annual temperature values (Widrlechner et al. 2012) were divided into severity classes or plant hardiness zones (Table 2).

MARCH 2017

TEQJA ET AL.

619

FIG. 3. (a) Relationship between absolute mean annual temperature (8C) and February mean temperature for the 1970–85 period, and (b) fluctuation of the difference between absolute (red) and predicted (black) mean annual temperature (8C) for the 1970–85 period for all climate stations.

Changes in spatial extent of various plant hardiness regions and trends over time under various climate change scenarios were assessed by using ArcMap software (ESRI 2010). Gridded maps of February monthly minimum temperatures from the WorldClim (Hijmans et al. 2005) data were downloaded and clipped to the extent of the study area. The WorldClim (Hijmans et al. 2005) data grids were adjusted by subtracting a predicted temperature value derived from the regression equation of the difference between the measured minimum mean February temperatures and absolute minimum February temperatures for 167 stations between 1970 and 1985, as described in the section 2e. The grids were reprojected to the European Albers Equal Area projection and classified based on the USDA plant hardiness zone classification system (Daly et al. 2012). The areal extent of plant hardiness zones was calculated for the 1950–2000 period and future climate scenarios (rcp26, rcp45, rcp60, and rcp70) for the 2041–60 and 2061–80 periods based on the predicted minimum

absolute temperatures for February rather than mean temperature.

d. Future climate scenarios influence on areal extent of selected plant species We selected seven horticultural plants (Pistacia vera, Acca sellowiana, Actinidia deliciosa, Musa acuminata, Persea americana, Ananas comosus, and Mangifera indica) that have been already introduced to the Albanian market. Their hardiness zones were determined based on work conducted by other researchers (Ferguson 1999; Herrera 1997; Gilman 2014; Gilman and Watson 1994, 2014a,b,c; Reiger 2016). In this analysis, we considered long-term benefits of introducing such species for attaining reasonable and consistent yield levels. We therefore excluded the extreme cold areas and limited our analysis to the potential plant hardiness zones above the lowest theoretical hardiness zone for these plant species. This allowed for a direct comparison of the lowest theoretical hardiness zone for these plant species

620


VOLUME 56

FIG. 4. (a) The relationship between measured absolute Tmin from climate stations and predicted absolute Tmin from SRTM, and (b) the relationship between SRTM elevation and climate station elevation.

with the hardiness zones based on current and projected future climate scenarios.

e. Statistical analysis A simple regression analysis was conducted to assess the relationship between mean absolute minimum annual temperature and February mean temperature for 167 climate stations for the 1970–85 period. The analysis was also conducted by physiographic regions to assess their influence on the relationship. An additional simple regression analysis was conducted to assess the relationship between measured elevations at the climate stations and elevations derived from Shuttle Radar Topography Mission (SRTM) data (Jarvis et al. 2008). A ‘‘robust fit’’ analysis was conducted to test for outliers. The analysis revealed the presence of 9 outliers. However, the outliers were not excluded from the analysis to raise awareness about the variability in elevation data. Also, we could not make any assumptions about elevation values from stations as being more accurate than

SRTM elevations values and vice versa. This facilitated the temperature predictions for plant hardiness zones generated from SRTM elevation grids (Jarvis et al. 2008) and WorldClim (Hijmans et al. 2005) temperature data grids. Daly et al. (2008) similarly predicted temperature and precipitation on 30-arc-s (800 m) grids for the conterminous United States using the ParameterElevation Regressions on Independent Slopes Model (PRISM). An analysis of variance (ANOVA) was conducted to compare absolute mean minimum temperature changes among climate stations, WorldClim (Hijmans et al. 2005), and IPCC AR5 climate scenarios within each physiographic region. Mean comparisons were made by using the LSMeans Tukey honestly significant difference (HSD) test. A pairwise t test was used to compare mean absolute minimum annual temperatures between WorldClim (Hijmans et al. 2005) and climate stations and between WorldClim (Hijmans et al. 2005) and IPCC AR5 climate scenarios. The null hypotheses were rejected at a significance level of 0.05.

MARCH 2017

TEQJA ET AL.

TABLE 3. Mean absolute minimum temperature differences (8C) between the WorldClim 1950–2000 period and future climate scenarios for the 2041–060 period (rcp2650, rcp4550, rcp6050, and rcp8550) and the 2061–80 period (rcp2670, rcp4570, rcp6070, and rcp8570). Climate scenarios

Mean difference

t ratio

p value

Climate stations vs WorldClim WorldClim vs rcp2650 WorldClim vs rcp4550 WorldClim vs rcp6050 WorldClim vs rcp8550 WorldClim vs rcp2670 WorldClim vs rcp4570 WorldClim vs rcp6070 WorldClim vs rcp8570

0.3

1.9

0.06

2.9 2.6 3.8 3.5 2.6 3.9 3.6 4.8

16.4 17.7 20.3 18.8 14 20.5 19.2 26.3

,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001 ,0.0001

3. Results and discussion a. Measured mean absolute Tmin from climate stations versus mean Tmin from the WorldClim data The WorldClim (Hijmans et al. 2005) data including climate scenarios provided only predicted mean minimum temperatures (Tmin). However, the severity classes of plant hardiness zones are based upon absolute annual minimum temperatures. To adjust the predicted mean Tmin from the WorldClim (Hijmans et al. 2005) data to the absolute Tmin, it was necessary to assess the relationship and the difference between measured mean Tmin and measured absolute Tmin from climate stations. The regression analysis showed a high degree of correlation (Fig. 3a) between measured mean Tmin and measured absolute Tmin from climate stations [R2 5 0.92;

621

root-mean-square error (RMSE) 5 1.3]. The degree of correlation varied by physiographic region. The mountain foothills region had the lowest R2 (0.65) followed by western low plains (R2 5 0.77). The RMSE varied from 0.98C (western low plains and hills and foothills) to 1.78C (mountain foothills). The regression equation showed an average difference of 27.38C between mean Tmin and predicted absolute Tmin; and the difference varied between 26.78 and 27.68C. On average, the differences between regions and average difference (27.38C) was only 20.38C, thus the predicted Tmin from WoldClim (Hijmans et al. 2005) were adjusted to absolute Tmin by subtracting the average difference of 27.38C. The use of predicted Tmin, from the WorldClim (Hijmans et al. 2005) data adjusted to absolute Tmin to determine the plant hardiness zones (severity classes), necessitated the assessment of the spatial trends in the difference. There were no observable trends in the difference with stations as shown by the overlap between measured absolute Tmin and mean Tmin (Fig. 3b). Also, the predicted absolute Tmin was highly correlated with measured absolute Tmin (R2 5 0.92; RMSE 5 1.3) (Fig. 4a). The WorldClim (Hijmans et al. 2005) data provide a grid that extends beyond the point data stations and use underlying assumptions on relationships between elevation and temperature. In this context, we first assessed the relationship between predicted elevation data from SRTM (Jarvis et al. 2008) and measured elevation data from the climate stations. The SRTM elevations were highly correlated with measured elevational data from the climate stations (R2 5 0.92; RMSE 5 120 m) (Fig. 4b).

FIG. 5. Changes in absolute minimum temperature between measured climate station, WorldClim data, and different climate scenarios based on predicted GHGs for the 2041–60 and 2061–80 periods.

622


VOLUME 56

FIG. 6. Summary of changes in absolute minimum temperature between the 1950–2000 period and climate scenarios based on predicted GHGs for the (a) 2041–60 and (b) 2061–80 periods for physiographic regions.

However, the robust fit analysis indicated that there were nine outlier stations. The removal of the outliers improved the relationship (R2 5 0.96; RMSE 5 81 m). The purpose of this analysis was to support the use of the SRTM elevation grid for extrapolating beyond climatic stations. We cannot prove nor assume that station elevation data are more accurate than SRTM, and the presence of the outliers highlights the variability of

elevation as another source of uncertainty in our predictions. A second assessment examined the relationship between predicted absolute Tmin and SRTM elevation. The SRTM elevation explained 65% of the variability in predicted absolute Tmin (R2 5 0.65; RMSE 5 1.9). Similar trends and relationships between elevation and Tmin have been found by other researchers. For example, Lookingbill and Urban (2003) found that Tmin is related to elevation

MARCH 2017

623

TEQJA ET AL.

TABLE 4. Mean temperature differences (8C) between physiographic regions for climate stations and WorldClim data. Significant differences (sig) at the 0.05 level are between physiographic regions within each climate scenario. Regions that share letters are not significantly different from each other. For regions that do not share a common letter, the ‘‘distance’’ between the letters indicates the relative strength or magnitude of the difference (e.g., the western low plains region is more significantly different from the mountains region than from the hills and foothills region). 1970–80 Climate stations

1950–2000 WorldClim

Physiographic regions

Mean

Sig

Mean

Sig

Western low plains Western midplains Hills and foothills Mountain foothills Mountains

24.0 24.8 27.0 29.8 212.9

a ab b c d

22.6 24.0 26.1 29.2 212.3

a a b c d

(R2 5 0.58) in the Oregon western Cascades. Relationships among measured Tmin, absolute Tmin, and elevation allowed for comparisons of climate scenario impact on the spatial distribution and extent of plant hardiness zones. As expected, there was uncertainty associated with these relationships. Although there was no clear evidence of biases or trends in Tmin errors related to spatial distribution and/or elevation, it should be noted that local variability due to physiographic characteristics does influence these relationships. Researchers have found that local- to landscape-scale physiographic characteristics do influence regional spatial patterns of temperature. For example, Dobrowski et al. (2009) found that between 70% and 80% of the variability in temperature patterns in the Lake Tahoe region in the United States was explained by regional patterns and the remaining percentage by landscape-scale physiographic characteristics. Interestingly, the RMSE associated with the difference between regional- and local-landscapedriven temperature patterns found by Dobrowski et al. (2009) was between 1.28 and 2.08C, which is very similar to the values found in our study. The magnitude of RMSE is especially important in the context of severity classes based on the range values of plant hardiness zones used to classify the study area. The threshold value for separating each plant hardiness zone is 2.88C, which is more than twice the overall 1.38C RMSE in our study area. The actual application of plant hardiness zones needs to account for these uncertainties, especially for differences in the long-term variance of extreme low-temperature events (Widrlechner et al. 2012). Local applications especially may need to be adjusted based on landscape physiographic characteristics. Also, the grid resolution of the WorldClim data is approximately 350 m 3 350 m, which warrants caution in

TABLE 5. Mean temperature differences (8C) between physiographic regions for the 2041–60 climate scenario forecasts. Climate scenario 2041–60 Physiographic regions

rcp2650

rcp4550

rcp6050

rcp8550

Mean Sig Mean Sig Mean Sig Mean Sig

Western low plains 20.6 Western midplains 22.2 Hills and foothills 24.0 Mountain foothills 27.0 Mountains 210.1

a ab b c d

20.7 22.1 23.9 26.9 29.6

a a b c d

0.4 21.2 23.0 26.1 29.0

a ab b c d

0.2 21.3 23.2 26.4 29.3

a a b c d

interpreting the exact boundary of plant hardiness zones. This is particularly important in high-relief areas where elevation changes greatly within a short distance, such as the narrow valleys in the mountainous and southern coastal areas of Albania. Because of the uncertainties associated with absolute Tmin predictions and the relatively coarse resolution of predicted Tmin from the WorldClim data, the impact of climate scenarios on the plant hardiness zones should be assessed in terms of general trends over large areas.

b. Mean absolute Tmin between climate stations, WorldClim, and IPCC AR5 climate scenarios based on physiographic regions The mean difference between climate stations and the WorldClim data was only 0.38C and was not significant (p value 5 0.06) (Table 3; Fig. 5). The differences between the WorldClim data and IPCC AR5 climate scenarios were all significant (p value , 0.0001), showing an increasing trend overall with increasing projected CO2 emissions (Figs. 6a,b). The mean absolute Tmin values difference between climate stations and the WorldClim data was 0.98C but varied according to physiographic regions, from 0.68C (mountain foothills and mountains) to 1.48C (western low plains) (Table 4). The mean absolute Tmin were significantly different among regions and decreased with IPCC AR5 climate scenarios (Tables 5 and 6). While values for the western low plains and western midplains were not significantly different for all

TABLE 6. Mean temperature differences (8C) between physiographic regions for the 2061–80 climate scenario forecasts. Climate scenario 2061–80 Physiographic regions

rcp2670

rcp4570

rcp6070

rcp8570

Mean Sig Mean Sig Mean Sig Mean Sig

Western low plains 20.7 Western midplains 22.2 Hills and foothills 24.1 Mountain foothills 27.3 Mountains 210.2

a a b c d

0.6 20.9 22.8 26.1 29.0

a a b c d

0.3 21.2 23.1 26.3 29.2

a a b c d

1.7 0.2 21.8 25.0 28.0

a a b c d

624


VOLUME 56

FIG. 7. Map of Albanian plant hardiness zones based on the WorldClim data for the 1950–2000 period.

absolute Tmin data sources, the value differences were significant for the hills and foothills. As expected, the hills and foothills physiographic region had lower Tmin, followed by the mountain foothills and mountains, which had the lowest absolute Tmin values (Figs. 6a,b). There was a wide range of variability in mean absolute Tmin, 1.78 to 210.28C, between physiographic regions within each data source. However, within each physiographic region the differences between different data sources were smaller and varied between 0.68 and 4.38C.

The smallest difference, 0.68C, was observed between rcp2650 and rcp8550, while the largest (a mean of 4.38C) was between the WorldClim data (1950–2000 period) and the rcp8570 climate scenario (2061–80 period).

c. Severity classes based on the plant hardiness zones using predicted mean absolute Tmin from the WorldClim 1950–2000 data As expected, the spatial distribution of plant hardiness zones was largely influenced by the relationship

MARCH 2017

625

TEQJA ET AL.

TABLE 7. Percent area (%) size by hardiness zone and IPCC AR5 climate scenario for the 2041–60 forecast period. Zone Climate scenario

6a

6b

7a

7b

8a

8b

9a

9b

10a

10b

WorldClim (1950–2000) rcp2650 rcp4550 rcp6050 rcp8550

0.2 0.0

2.0 0.4 0.1 0.1 0.1

6.3 2.9 1.7 1.6 1.8

13.6 7.9 6.3 5.6 5.8

21.6 15.5 14.7 11.9 13.0

16.9 23.0 23.4 20.7 21.5

17.3 16.5 18.6 19.5 18.5

17.3 18.1 19.2 16.6 17.4

4.8 15.7 16.1 18.3 18.1

5.8 3.9

between temperature and elevation (Fig. 7). Daly et al. (2012) observed similar trends when developing a new plant hardiness zone map for the United States. The distribution of plant hardiness zones corresponded, to a great extent, with physiographic regions (Fig. 1) derived from the classification of elevations. Zones 9b and 10a were generally restricted to western Albania and coincided mostly with the western low plains and western midplains. The mildest zones (9b and 10a) together covered approximately 22% of the country (Tables 7 and 8). Plant hardiness zones 9a, 8a, and 8b were the most dominant (15 600 km2) and covered approximately 56% of the country. Their distribution coincided mostly with the hills and foothills and mountain foothills. Plant hardiness zones 7a and 7b covered approximately 21% of the country, an area of 5 500 km2. These zones, along with plant hardiness zones 6a and 6b, which had a very limited extent (2.2% or 620 km2), coincided with the mountains physiographic region. A visual comparison of the plant hardiness map of Albania with the plant hardiness zone map of Europe developed by Heinze and Schreiber (1984) showed reasonable similarities with regard to the presence of severity classes and their spatial distribution. The same severity classes (6, 7, 8, 9, and 10) in the Heinze and Schreiber (1984) map were also present in the map developed from this study. However, as expected, the map from this study was more detailed spatially and with regard to subzones a and b within each severity zone, which are not present in Heinze and Schreiber (1984). The increased level of detail for plant hardiness zone and subzones would be more suitable for horticulturists

developing strategies and practices for managing current and future plant species.

d. Potential climate change impacts on Albanian plant hardiness zones and the distribution of some horticultural crops The spatial distribution and areal extent of plant hardiness zones were clearly affected by the various IPCC AR5 climate scenarios (Figs. 8 and 9) in diverse ways (Figs. 10a,b and 11a,b). Plant hardiness zone 10a experienced the greatest percent areal increase, from 4.8% to 15% on average, with a maximum of 18.4% (Tables 7 and 8). In addition, a plant hardiness zone (10b) new to the historic climate scenario, appeared and expanded to 12.6% of national coverage for the most aggressive climate scenario (rcp8570) (Table 8). In contrast, plant hardiness zones 6b, 7a, and 7b experienced the greatest percent areal decreases, from 2% to 0.1%, 6.3% to 0.6%, and 13.6% to 3.6%, respectively. Also, plant hardiness zone 6a disappeared after the rcp2650/2670 IPCC AR5 climate scenarios, indicative of rising temperatures during the cold season. The least fluctuating plant hardiness zones, in terms of areal extent, were zones 8b and 9b. Zone 8b only increased from 17% to 21.5%, while zone 9b varied slightly from 16.3% (rcp8570) to 18% (rcp2670). Shifts in the spatial pattern of plant hardiness zones may present new risks for the long-term survival of some plant species but also provide opportunities for introducing new horticultural plants. In this study, we analyzed the potential area for the cultivation of six horticultural crops in Albania (Table 9). As expected, the future climate scenarios predicted increases in area

TABLE 8. Percent area (%) size by hardiness zone and IPCC AR5 climate scenario for the 2061–80 forecast period. Zone Climate scenario

6a

6b

7a

7b

8a

8b

9a

9b

10a

10b

WorldClim (1950–2000) rcp2670 rcp4570 rcp6070 rcp8570

0.2 0.0

2.0 0.4 0.1 0.1 0.0

6.3 2.9 1.4 1.7 0.6

13.6 7.9 5.2 5.6 3.6

21.6 15.4 12.1 12.8 9.0

16.9 22.9 20.6 21.2 17.2

17.3 16.5 19.4 18.5 22.3

17.3 18.0 16.9 17.1 16.3

4.8 15.7 17.9 17.7 18.4

0.3 6.6 5.3 12.6

626


FIG. 8. Plant hardiness zones based on four climate scenarios using the WorldClim data for the 2041–60 period.

VOLUME 56

MARCH 2017

TEQJA ET AL.

FIG. 9. As in Fig. 8, but for the 2061–80 period.

627

628


VOLUME 56

FIG. 10. Areal changes in severity classes relative to the 1950–2000 period (0 line) and climate scenarios for the (a) 2041–60 period (rcp2650, rcp4550, rcp6050, and rcp8550) and (b) 2061–80 period (rcp2670, rcp4570, rcp6070, and rcp8570).

for all six horticultural crops. For example, the area extent of kiwi (Actinidia deliciosa), which is already cultivated to a limited degree in Albania, could increase from 77.9 km2 to as much as 92 km2. Also, the potential cultivation area of Pistacia vera and Acca sellowiana, which are currently cultivated in Albania, would increase in the future according to all climate change scenarios analyzed in this study (Table 9). The remaining three species, Musa acuminata, Persea americana, and Ananas comosus, could potentially be cultivated, especially under the most aggressive climate change scenarios. Although these projected shifts in climate represent opportunities for introducing new crops, studies will be needed to address the interactions of climate and management practices to mitigate future climate fluctuations and uncertainty as predicted by

other studies (Tous and Ferguson 1996; Paxian et al. 2015), especially at local levels. This is critical for the introduction of species to areas that would be considered on the fringe of their range of adaptation. The areal extend of these species is based only on biological limits as dictated by the plant hardiness zones; however, other factors need to be considered. The potential cultivation area for Pistacia vera depends also on elevation, development stage, etc., (Herrera 1997). For example, kernel development is affected at elevations greater than 1 500 m, because of cool summers, while temperatures below 212.28C could kill the trees, especially young ones (Herrera 1997). Economic factors may also play a role, thus the areal extend provided in this study are only projections that may vary depending on a combination of climate and economic factors.

MARCH 2017

629

TEQJA ET AL.

FIG. 11. Percent changes in area for plant hardiness zones relative to the 1950–2000 period (0 line) and climate scenarios for the (a) 2041–60 period (rcp2650, rcp4550, rcp6050, and rcp8550) and (b) 2061–80 period (rcp2670, rcp4570, rcp6070, and rcp8570).

4. Conclusions We studied the climate impact on the changing geography of cold-season extreme temperatures and its implications for plant hardiness zones in Albania by

comparing historical climatic data from WorldClim (http://www.worldclim.org/; Hijmans et al. 2005) for the 1950–2000 period with future climate scenarios based on downscaled IPCC AR5 phase 5 of the Coupled Model Intercomparison Project (CMIP5) data. The measured

TABLE 9. The impact of climate change on the potential areas (km2) for growing new horticultural species. Climate scenario for 2041–60

Climate scenario for 2061–80

Plant species

Actual area

rcp2670

rcp4570

rcp6070

rcp8570

rcp2670

rcp4570

rcp6070

rcp8570

Pistacia vera Acca sellowiana Actinidia deliciosa Musa acuminata Persea americana Ananas comosus Mangifera indica

91.5 77.9 77.9 22.1 22.1 4.8 0.0

96.7 88.8 88.8 33.8 33.8 15.7 0.0

98.3 92.0 92.0 35.3 35.3 16.1 0.0

98.4 87.0 87.0 40.7 40.7 24.1 5.8

98.2 88.5 88.5 39.4 39.4 22.0 3.9

96.7 88.5 88.5 34.0 34.0 16.0 0.3

98.7 86.9 86.9 41.4 41.4 24.5 6.6

98.2 87.3 87.3 40.1 40.1 23.0 5.3

99.4 83.2 83.2 47.3 47.3 31.0 12.6

630


mean Tmin and absolute Tmin from climate stations were highly correlated, with a mean difference of 27.38C. The distribution of the mean differences did not show any spatial bias and allowed for the assessment of the influence of future climate scenarios on the distribution of plant hardiness zones. The IPCC AR5 climate scenarios affected the distribution and extent of plant hardiness zones. The disappearance of the most severe plant hardiness zones and the appearance of new, warmer plant hardiness zones under rcp2650/2670 IPCC AR5 climate scenarios reflect rising temperatures during the cold season. However, the uncertainty associated with the use of predicted absolute Tmin from the relationship between mean Tmin and absolute Tmin from climate stations was about 1.38C, or almost one-half of the 2.88C threshold value used to separate plant severity zones. The uncertainty may further increase because of increased climatic fluctuations. Thus, the shifts in spatial distribution of plant hardiness zones need to be viewed critically. Plant hardiness zones related to climate change present opportunities for growing new horticultural crops; however, more studies are needed to assess the interactions between climate change and management practices for these new species, especially at local levels. Acknowledgments. The authors thank Purdue University for funding the publication of this work (Grant 21030000). A portion of this research was funded by USDA Current Research Information System Project 6020-21310009-00D Sustainable Small Farm and Organic Production Systems for Livestock and Agroforestry. The authors also thank Jenny Sutherland at the USDA–Natural Resources Conservation Service National Soil Survey Center for her meticulous editing and revisions. We are also grateful for the comments and suggestions made by many colleagues at the USDA–NRCS National Soil Survey Center. We thank the reviewers in particular for their detailed comments and suggestions that substantially improved the quality of this article. REFERENCES American Horticultural Society, 1997: Plant heat zone map. Accessed 15 August 2015. [Available online at http://www.ahs.org/ gardening-resources/gardening-maps/heat-zone-map.] Brown, I., W. Towers, M. Rivington, I. Helaina, and J. Black, 2008: Influence of climate change on agricultural land-use potential: Adapting and updating the land capability system for Scotland. Climate Res., 37, 43–57, doi:10.3354/cr00753. Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 2031–2064, doi:10.1002/joc.1688.

VOLUME 56

——, M. P. Widrlechner, M. D. Halbleib, J. I. Smith, and W. P. Gibson, 2012: Development of a new USDA Plant Hardiness Zone Map for the United States. J. Appl. Meteor. Climatol., 51, 242–264, doi:10.1175/2010JAMC2536.1. Dawson, I., 1991: Plant hardiness zones for Australia. Australian National Botanic Gardens, accessed 10 May 2015. [Available online at http://www.anbg.gov.au/hort.research/zones.html.] Deser, C., R. Knutti, S. Solomon, and A. S. Phillips, 2012a: Communication of the role of natural variability in future North American climate. Nat. Climate Change, 2, 775–779, doi:10.1038/nclimate1562. ——, A. Phillips, V. Bourdette, and H. Teng, 2012b: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527–546, doi:10.1007/s00382-010-0977-x. Dobrowski, S. Z., J. T. Abatzoglou, J. A. Greenberg, and S. G. Schladow, 2009: How much influence does landscape-scale physiography have on air temperature in a mountain environment? Agric. For. Meteor., 149, 1751–1758, doi:10.1016/j.agrformet.2009.06.006. ESRI, 2010: ArcGIS Desktop, release 10. Environmental Systems Research Institute. Ferguson, A. R., 1999: New temperate fruits: Actinidia chinensis and Actinidia deliciosa. Perspectives on New Crops and New Uses, J. Janick, Ed., ASHS Press, 342–347. Gilman, E. F., 2014: Ananas comosus: Pineapple. University of Florida, Institute of Food and Agricultural Sciences Extension Publ. FPS039, accessed 15 April 2015. [Available online at http://edis.ifas.ufl.edu/fp039.] ——, and D. G. Watson, 1994: Mangifera indica: Mango. University of Florida, Institute of Food and Agricultural Sciences Fact Sheet ST-404, accessed 15 April 2015. [Available online at http://hort.ufl.edu/database/documents/pdf/tree_fact_sheets/ maninda.pdf.] ——, and ——, 2014a: Feijoa sellowiana: Feijoa. University of Florida, Institute of Food and Agricultural Sciences Extension Publ. ENH-408, accessed 15 April 2015. [Available online at http://edis.ifas.ufl.edu/st249.] ——, and ——, 2014b: Persea americana: Avocado. University of Florida, Institute of Food and Agricultural Sciences Extension Publ. ENH-594, accessed 15 April 2015. [Available online at http://edis.ifas.ufl.edu/st435.] ——, and ——, 2014c: Musa spp.: Banana. University of Florida, Institute of Food and Agricultural Sciences Extension Publ. ENH-568, accessed 15 April 2015. [Available online at http:// edis.ifas.ufl.edu/st409.] Hayashi, Y., 1990: Jumoku Ato Bukku (Tree Art Book). Abokkusha, 366 pp. Heinze, W., and D. Schreiber, 1984: Eine neue Kartierung der Winterhärtezonen für Gehölze in Mitteleuropa (A new mapping of the hardiness zones for woody plants in central Europe). Mitt. Dtsch. Dendrol. Ges., 75, 11–56. Herrera, E., 1997: Growing pistachios in New Mexico. New Mexico State University, 13 pp. [Available online at http://aces.nmsu.edu/ pubs/_circulars/CR532.pdf.] Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis, 2005: Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol., 25, 1965–1978, doi:10.1002/joc.1276. Hydro-Meteorological Institute, 1988: Climatic Atlas of Albania. Academy of Science, 360 pp. IPCC, 2013: Summary for policymakers. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1–29. Jaho, S., A. Mici, and Instituti Hidrometeorologjik, 1988: Climatic Atlas of PSR of Albania. Academy of Sciences of Albania, 360 pp.

MARCH 2017

TEQJA ET AL.

Jarvis, A., H. I. Reuter, A. Nelson, and E. Guevara, 2008: SRTM 90m Digital Elevation Database v4.1, accessed 20 July 2010. [Available online at http://www.cgiar-csi.org/data/srtm-90m-digitalelevation-database-v4-1.] Kopali, A., V. Peculi, Z. Teqja, and E. Rota, 2012: A study on agroclimatic characterization of Albanian territory. Albanian J. Agric. Sci., 11, 173–177. Lookingbill, T. R., and D. L. Urban, 2003: Spatial estimation of air temperature differences for landscape-scale studies in montane environments. Agric. For. Meteor., 114, 141–151, doi:10.1016/ S0168-1923(02)00196-X. McKenney, W. D., J. H. Pedlar, K. Lawrence, K. Campbell, and M. F. Hutchinson, 2007: Beyond traditional hardiness zones: Using climate envelopes to map plant range limits. BioScience, 57, 929–937, doi:10.1641/B571105. ——, ——, ——, P. Papadopol, K. Campbell, and M. F. Hutchinson, 2014: Change and evolution in the plant hardiness zones of Canada. BioScience, 64, 341–350, doi:10.1093/biosci/biu016. Moss, R., and Coauthors, 2008: Towards new scenarios for analysis of emissions, climate change, impacts, and response strategies. IPCC, 132 pp. [Available online at http://www.aimes.ucar.edu/ docs/IPCC.meetingreport.final.pdf.] ——, and Coauthors, 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747–756, doi:10.1038/nature08823. Ouellet, C. E., and L. C. Sherk, 1967: Woody ornamental plant zonation, II: Suitability indices of localities. Can. J. Plant Sci., 47, 339–349, doi:10.4141/cjps67-064. Paxian, A., E. Hertig, S. Seubert, G. Vogt, J. Jacobeit, and H. Paeth, 2015: Present-day and future Mediterranean precipitation extremes assessed by different statistical approaches. Climate Dyn., 44, 845–860, doi:10.1007/s00382-014-2428-6. Pienaar, K., 1996: The Ultimate Book of Trees and Shrubs for Southern African Gardeners. Southern Book Publishers, 580 pp. Pliscoff, P., F. Luebert, H. Hilger, and A. Guisan, 2014: Effects of alternative sets of climatic predictors on species distribution

631

models and associated estimates of extinction risk: A test with plants in an arid environment. Ecol. Modell., 288, 166–177, doi:10.1016/j.ecolmodel.2014.06.003. Reiger, M., 2016: Pistachio—Pistacia vera. [Available online at http://www.fruit-crops.com/pistachio-pistacia-vera/.] Resco de Dios, V., C. Fischer, and C. Colinas, 2007: Climate change effects on Mediterranean forests and preventive measures. New For., 33, 29–40, doi:10.1007/s11056-006-9011-x. Stone, R. C., and H. Meinke, 2006: Weather, climate, and farmers: An overview. Meteor. Appl., 13, 7–20, doi:10.1017/ S1350482706002519. Teqja, Z., 1994: The variability of potato yield caused by climatic factors. Agric. Mediterr., 124, 29–36. Tous, J., and L. Ferguson, 1996: Mediterranean fruits. Progress in New Crops, J. Janick, Ed., ASHS Press, 416–430. Weyant, J., C. Azar, M. Kainuma, J. Kejun, N. Nakicenovic, P. R. Shukla, E. La Rovere, and G. Yohe, 2009: Report of 2.6 versus 2.9 Watts/m2 RCPP Evaluation Panel. IPCC, 81 pp., accessed 12 February 2015. [Available online at http://www.ipcc.ch/ meetings/session30/inf6.pdf.] Widrlechner, M. P., 1997: Hardiness zones in China. Accessed 15 August 2015. [Available online at http://www.ars.usda.gov/ SP2UserFiles/Place/50301000/Graphics/Climate_china.pdf.] ——, R. E. Schutzki, V. Y. Yukhnovsky, and V. V. Sviatetsky, 2001: Collecting landscape trees and shrubs in Ukraine for the evaluation of aesthetic quality and adaptation in the north central United States. PGR Newsletter, No. 126, 12–16. [Available online at http://www.bioversityinternational.org/ fileadmin/PGR/article-issue_126-art_2-lang_en.html.] ——, C. Daly, M. Keller, and K. Kaplan, 2012: Horticultural applications of a newly revised USDA Plant Hardiness Zone Map. Horttechnology, 22, 6–19. Yalçın, O., and D. Tolunay, 2012: Distribution of the major forest tree species in Turkey within spatially interpolated plant heat and hardiness zone maps. iForest, 5, 83–92, doi:10.3832/ ifor0611-005.