The impacts of climate change on habitat suitability of Prosopis farcta (Banks & Sol.) J.F.Macbr. in Türkiye

Abstract

Background  Climate change is one of the most important environmental factors mediating the changes in the species’ distribution.

Objective  Prosopis farcta is an important weed in agricultural areas of Türkiye, which has increased its density in agricultural and non-agricultural areas. This study estimated the habitat suitability of P. farcta under current and future climatic conditions in Türkiye.

Methods  Habitat suitability of P. farcta was predicted by using 1169 occurrence records and 8 bioclimatic variables under SSP1-2.6 (mild climate change) and SSP8-5.8 (severe climate change) scenarios by using Maximum Entropy (MaxEnt) model.

Results  According to the results; i) temperature was found to be the most important climatic variable for the distribution of P. farcta, ii) under the current climatic conditions, certain parts of the eastern, southern and western regions of Türkiye were found to be potentially suitable and these areas correspond to approximately 40% of country’s total land area. iii) According to the projections, suitable habitats will expand towards the interior and northern parts of Türkiye in the future due to climate change. The model revealed that the total suitable area could increase by 22%, up to 62%. Therefore, the modeling results support the data obtained from the surveys.

Conclusions  It is predicted that P. farcta may spread to different regions of Türkiye and cause greater problems due to climate change. Monitoring the changes in P. farcta populations due to climate change and adjusting weed control methods applied may contribute to the development of more effective, climate resilient management strategies.

Prosopis Farcta; Climate Change; Maxent Model; Potential Distribution Areas; Turkey

1.Introduction

Climate changes such as increased temperature, drought, and altered precipitation regimes etc. significantly affect growth, development and distribution of plants (Önen et al., 2018; Ozaslan et al., 2016; 2017). This altered growth and development exert direct impact on the yield and quality of cultivated plants in agricultural systems. On the other hand, these changes increase in the negative effects of weeds (such as competition and allelopathy) and may make their management difficult (Myers et al., 2017; Onen, Ozcan, 2010). It has been reported that elevated temperature can affect growth cycles, development and competitiveness of cultivated plants, as well as change harvest times. Nevertheless, environmental conditions may change drastically in some areas to completely lose suitability for some crops (IPCC, 2019). Weeds and invasive alien plants can adapt to different geographical regions and ecosystems leading to significant economic, ecological and socio-economic problems. Climate changes can create opportunities to expand the distributions of weeds and invasive plant species (Onen, 2015). It is suggested that weeds can extend their habitat in natural and agricultural ecosystems by spreading to larger areas under changing climate.

Environmental conditions are among the most important factors affecting the germination, development, population growth and spread of weeds and invasive plants in different ecosystems (Farooq et al., 2017; Onen et al., 2017; Önen et al., 2018; Ozaslan et al., 2017). For this reason, climate change is one of the major causes of weed invasions (Onen, 2015; Petitpierre et al., 2012; Thuiller et al., 2005). Weeds, having become widespread and increased in density as a result of climate change, may be able to compete more strongly with cultivated plants in agricultural ecosystems and cause significant yield and quality reductions. Therefore, climate change is seen as a new threat affecting biodiversity as it aggravates the scope and effects of plant invasions (Hellmann et al., 2008; Ziska et al., 2011).

Ecological characteristics of weeds such as life cycles, population dynamics and distribution areas are severely affected by climate change (Debouk et al., 2015; Lommen et al., 2018; Rasmussen et al., 2017). However, the response of weeds to climate change is largely species-specific (Yi et al., 2019). Perennial weeds are predicted to be more affected by global change than annual species (Broennimann et al., 2006). Since perennial weeds with deep root structures are more resistant to drought stress, climate changes may positively affect their distribution and population dynamics (Harris, 1960). Therefore, determining the potential distribution areas of weeds under climate change and monitoring their populations can make important contributions towards understanding the population dynamics and developing management strategies (Onen, 2015; Taylor et al., 2012).

Prosopis farcta (Fabaceae) is one of the 44 species in the genus Prosopis. The plant has a broad distribution in South, North and Central America, Africa and Western Asia (Abd El Halim & Azer, 2015; Llanes et al., 2011). P. farcta, which is part of the Iran-Turan floristic region, has a widespread distribution in the region where Türkiye is located. P. farcta was first detected in the Southeastern Anatolia region of Türkiye and has spread to the Mediterranean and Eastern Anatolia regions. Since the plant is highly adaptable to heat and drought, its population density is constantly increasing and invading new areas. For this reason, it has been reported that the plant has spread in Adana, Batman, Mardin, Diyarbakır, Elazığ, Hatay, Mersin, Mersin, Kahramanmaraş, Muş, Siirt, Şanlıurfa and Şırnak provinces due to increasing population density (Bükün et al., 1995; Gönen, Uygur, 1998; Mennan, Işik, 2003; Orel, 1996; Sırrı, 2019).The plant is distributed in many habitats such as urban open spaces, vacant lots and roadsides as well as agricultural areas (sunflower, cotton, vegetable fields, ornamental plants, orchards), field edges, pasture areas, airports and railways.

Prosopis farcta is an important weed that not only affects yield and quality in agricultural areas but also poses problems during harvest and soil preparation. Due to its tolerance to extreme conditions, it causes significant problems in cotton, vegetable, and fruit orchards (such as pistachio) in the Southeastern Anatolia region, Türkiye. Furthermore, it also damages non-agricultural areas, parks, and gardens. The perennial nature and thorny structure enable it to rapidly establish populations and spread to new areas. The species continues to be a growing problem across various regions of Türkiye, particularly in the Southeastern Anatolia region (Sırrı, 2019). It has been reported from sunflower, vineyards, pistachio, cotton, olive, and other vegetable and fruit crops in Türkiye (Bükün et al., 1995; Uygur et al., 1996; Üremiş, 2005; Sırrı, 2019).

The occurrence frequency of P. farcta in cotton fields of Şanlıurfa province of the country has been reported as 50% (Boz et al., 1995), while it was 47% in the vegetable fields (Uygur et al., 1996). Similarly, the frequence of occurrence was 26% in pistachio plantations of Siirt province (Sırrı, 2019). Likewise, frequency of occurrence was 10% in the corn fields of Çukurova region (Orel, 1996), while it was 63% in cotton fields in 1983 and 69% in 1996 (Gönen and Uygur, 1998). Furthermore, studies conducted in different regions of Türkiye have indicated that P. farcta causes significant problems in various crops (Mennan, Işık, 2003; Tursun et al., 2003).

P. farcta may colonize and extend its distribution range in Türkiye due to its high adaptation ability to extreme environmental conditions such as elevated temperature, and drought caused by climate change. Bükün (2005) reported that frequency of occurrence of P. farcta in cotton fields was 42% and density was 0.53% in Harran plain before irrigation. However, frequency of occurrence decreased to 14% and density increased to 40% ten years after transition to irrigated cotton production in the plain. Although irrigation has influenced the population density and distribution of P. farcta, global warming, decreased water resources, and drought suggest that Prosopis species may cause larger invasions in the future in Türkiye.

Generally, weeds can survive in agricultural ecosystems because of their superior characteristics (rapid germination and growth, deep root structure, adaptation to changing environmental conditions, etc.). While many native plant species that are sensitive to climate change face threats to their populations, weeds and invasive plants can easily adapt to new conditions and expand their habitats due to their high adaptability (Farooq et al., 2015). It has been observed that the prevalence and density of P. farcta increased in agricultural and non-agricultural areas of Southern and Southeastern Anatolia despite the negative effects of climate change.

Although available evidence suggests that P. farcta could extend its distribution range and affect agricultural production in Türkiye, no studies have been conducted to infer the impact of climate change on its potential habitat suitability in the country. Therefore, the current study was aimed at predicting the potential distribution areas of P. farcta under different climate change scenarios. It was hypothesized that climate change would extend the distribution of the species in the country. The results would help to identify the areas under the invasion risk of P. farcta and inform the policymakers to take proactive decisions for halting the further spread of the species in the future.

2.Material and Methods

2.1 Habitat suitability modeling

MaxEnt modeling procedure was used to predict the current and potential distribution areas of P. farcta in Türkiye in the current study. Species distribution models are frequently used to predict the potential distribution areas of target species at global and regional scales (Yates et al., 2018). MaxEnt is the most preferred model in ecological research since it offers many advantages compared to other approaches (Dutra Silva et al., 2021). The MaxEnt model estimates the climatic/habitat suitability of the target species based on their known occurrence records (Abdelaal et al., 2019). Potential distribution areas of P. farcta were predicted under current and future (2021–2040, 2041–2060 and 2081–2100) periods under two “Shared Socioeconomic Pathways (SSPs)”, i.e., [SSP1-2.6 (mild climate change) and SSP5-8.5 (severe climate change)] via this modelling approach.

2.2 Data collection and cleaning

The occurrence records of P. farcta in the Southeastern Anatolia region (Siirt, Batman, Diyarbakır, Mardin and Şanlıurfa) were collected by exploratory surveys. Furthermore, the occurrence records reported in the literature were also collected (Bükün et al., 1995; Gönen, Uygur, 1998; Mennan, Işik, 2003; Orel, 1996; Sırrı, 2019). Furthermore, global occurrences records of P. farcta were downloaded from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/). However, the GBIF records were thoroughly cleaned. The cleaning was done at taxonomic level and occurrence records belonging to the species other than P. farcta were deleted. Furthermore, duplicated and wrong coordinates were also eliminated. The Türkiye and global data were combined, and the final dataset contained 1169 occurrence records of P. farcta.

2.3 Climate data

Various climatic, soil, topographic, and socioeconomic factors alter the distribution and potential spread of the species (Mohamed, Azer, 2015; Llanes et al., 2011). Climate data of current and future periods are available from various databases; however, the future data regarding soil, socioeconomic and topographic features are not available. Therefore, only climate data were used to infer the impact of climate changes on the potential distribution of P. farcta in the current study. Climate data for 19 bioclimatic variables (Table 1) were downloaded from the WorldClim database (https://www.world clim.org/data/index.html) at a high resolution (30 arc sec, ~1 km²) (Fick, Hijmans, 2017).

Table 1
Bioclimatic variables used in MaxEnt model, their codes and explanations

	Description	Unit		Description	Unit
bio1	Annual Mean Temperature	°C	bio11	Mean Temperature of the Coldest Quarter	(°C)
bio2	Mean Diurnal Range (mean of monthly [max temp − min temp])	°C	bio12	Annual Precipitation	mm
bio3	Isothermal Temperature (bio2/bio7) (×100)	/	bio13	Precipitation of the Wettest month	mm
bio4	Temperature Seasonality	/	bio14	Precipitation of the Driest Month	mm
bio5	Maximum Temperature of the Hottest Month	°C	bio15	Precipitation Seasonality (Coefficient of Variation)	/
bio6	Minimum Temperature of the Coldest Month	°C	bio16	Precipitation of Wettest Quarter	mm
bio7	Temperature Annual Range (bio5–bio6)	°C	bio17	Precipitation of Driest Quarter	mm
bio8	Mean Temperature of Wettest Quarter	°C	bio18	Precipitation of the Hottest Quarter	mm
bio9	Mean Temperature of the Driest Quarter	°C	bio19	Precipitation of the Coldest Quarter	mm
bio10	Mean Temperature of the Hottest Quarter	°C

2.4 Model calibration

The MaxEnt model was optimized before training and testing to improve its predictive power. The data of 19 bioclimatic variables were extracted for 1,169 occurrence records in R statistical environment. Afterwards, correlation between the extracted values was computed and climatic variables with correlation coefficient of > 0.70 were excluded. Hence, 8 bioclimatic variables were used to predict the suitability of the habitat of P. farcta under current and future climatic conditions. The default settings of MaxEnt, i.e., 10,000 background points, and 5,000 iterations with 5 replications were used to train and test the model. The model was trained on 75% of the data and tested on the remaining 25%. The contribution of bioclimatic variables was determined by Jackknife analysis.

2.5 Predictive power and habitat suitability

The predictive power of the MaxEnt model was determined by ROC or AUC (area under the receiver operating characteristics curve). Habitat suitability rasters given by MaxEnt model were imported into ArcGIS and classified for determining the habitat suitability. The Habitat suitability index (ranging from 0 to 1) was classified into four categories, i.e., 0-0.25 (unsuitable), 0.26–0.50 (slightly suitable), 0.51–0.75 (suitable) and 0.76–1.0 (highly suitable). The global rasters were then downscaled to Türkiye and the areas corresponding to each category were calculated with the raster calculation tool in ArcGIS. Furthermore, changes in the habitat suitability under future climatic conditions were compared to the habitat suitability under current climatic conditions of Türkiye.

3.Results

3.1 Predictive power of the model

The AUC is used to assess the predictive power of the model. The predictive power of the model is high when AUC value approaches 1, and decreases as the value moves away from 1. Generally, value > 0.5 indicates that the model has good predictive power (Uzun, Örücü, 2020). The AUC value of the calibrated model was 0.96–1.00, indicating that the model predicted the habitat suitability of P. farcta with high predictive power (Figure 1).

Figure 1
The receiver operating characteristic curve (AUC) indicating the predictive power of MaxEnt model under current climatic conditions

3.2 Contribution of bioclimatic variables towards model and their influence on the potential distribution of Prosopis farcta

The Jackknife test indicated that bio 9 (mean temperature of the driest quarter), bio 18 (precipitation of warmest quarter), bio 8 (mean temperature of the wettest quarter) and bio 15 (precipitation seasonality) had the highest contribution towards the model. Similarly, bio 8 (52.1%) and bio 9 (25.4%) had the highest permutation importance indicating that these variables will mediate the distribution of P. farcta under current and future climatic conditions. The permutation importance of temperature-related variables was higher than precipitation-related variables (Figure 2 and Table 2).

Figure 2
Jack knife analysis of the bioclimatic variables used in the model to predict the habitat suitability of Prosopis farcta under current climatic conditions

Table 2
Relative contribution and permutation importance of the bioclimatic variables included in the MaxEnt model to predict the habitat suitability of Prosopis farcta under current climatic conditions

Variable	Percent contribution (%)	Permutation importance (%)
bio9	47.1	25.4
bio18	23.6	0.7
bio8	13.5	52.1
bio15	6.8	4.9
bio2	5.1	3.7
bio19	3.7	0.0
bio7	0.2	1.3
bio5	0.1	11.9

• bio9: mean temperature of the driest quarter; bio18: precipitation of the hottest quarter; bio8: mean temperature of wettest quarter; bio15: precipitation seasonality; bio2: mean diurnal range; bio19: precipitation of the coldest quarter; bio7: temperature annual range; bio5: maximum temperature of the hottest month

3.3 Habitat Suitability of Prosopis farcta under current and future climate conditions

Prosopis farcta is presently recorded in the Iranian-Turanian floristic region and reported to have a wide distribution area from the Middle East to South Russia in the north, India in the east and Algeria in the west. Habitat suitability of P. farcta in Türkiye is shown in Figure 3, whereas habitat suitability under future climatic conditions is given in Figure 4, 5, and 6. Literature records indicate that P. farcta is distributed in Adana, Batman, Mardin, Diyarbakır, Elazığ, Hatay, Mersin, Kahramanmaraş, Muş, Siirt, Şanlıurfa and Şırnak provinces of Türkiye. The model predicted that Adıyaman, Adana, Antalya, Batman, Diyarbakır, Gaziantep, İzmir, Hatay, Mardin, Manisa, Osmaniye, Siirt, Şanlıurfa and Şırnak provinces have the highest suitable habitat for P. farcta. This shows that the model results correspond well with field data. Furthermore, potential distribution areas of P. farcta under future climatic conditions also corresponded well with the field data. The model predicted that the species could expand its range beyond southeastern Anatolia towards the Mediterranean, Aegean, Marmara, Eastern, Southeastern, and Inner Anatolia regions. Overall, 7%, 15%, 18% and 60% area of Türkiye was predicted as highly suitable, suitable, slightly suitable and unsuitable, respectively for P. farcta under current climatic conditions.

Figure 3
Habitat suitability of Prosopis farcta in Türkiye under current climatic conditions (1970-2000) of the country predicted by MaxEnt model

Figure 4
Habitat suitability of Prosopis farcta in 2021-2040 under SSP1-2.6 and SSP5-8.5 climate change scenarios estimated by the MaxEnt model

Figure 5
Habitat suitability of Prosopis farcta in 2041-2060 under SSP1-2.6 and SSP5-8.5 climate change scenarios estimated by the MaxEnt model

Figure 6
Habitat suitability of Prosopis farcta in 2081-2100 under SSP1-2.6 and SSP5-8.5 climate change scenarios estimated by the MaxEnt model

The model predicted that highly and moderately suitable areas will increase under future climate conditions (Figures 4, 5, 6, 7). Highly suitable areas in the western and southern regions of the country are predicted to shift towards inner Anatolia region under the mild climate change scenario.

Figure 7
The distribution of total surface area of Türkiye in different climatic suitability categories under current and future climatic conditions of the country

The model predicted a consistent increase in suitable areas (slightly suitable + moderately suitable + highly suitable) in 2021–2040, 2041–2060 and 2081–2100 compared to the current climate (Figure 8) and species will benefit more from severe climate change scenario compared to the mild climate change scenario (Figure 7). The 40% area of the country was suitable for P. farcta under current climate which would increase to 51–62% under severe climate change scenario and 49–54% under mild climate change scenario in the future (Figure 4).

4.Discussion

The results show that P. farcta will expand its distribution in Türkiye and the ecological niche of the species is not fully occupied yet. The model predicted P. farcta has a high spread potential around the occurrence records and inner parts of the country. Weed distribution is generally determined by biotic factors involving human activities (e.g., land use) and natural agents as well as abiotic (climate, soil, etc.) factors. It has been reported that population densities and distributions of many species in agro-ecosystems and natural areas can vary greatly because of global climate changes and other anthropogenic factors (Lommen et al., 2018; Onen, 2015; Önen et al., 2018). The results revealed that if there are no anthropogenic changes (weed control) and dispersal limitations due to biotic factors (pests and pathogens), P. farcta may double its distribution in Türkiye and may become more widespread. Some previous modeling studies have concluded that some weeds will extend their current distribution in Türkiye under climate change (Kekeç, Kadioğlu, 2020; Onen et al., 2023; Sırrı, 2022b; Wei et al., 2018; West et al., 2015). Nevertheless, many species that are not aggressive today may become invasive in the future due to climate changes (Onen, 2015). Nevertheless, some species would observe range contraction or even extinction risk under climate change (IPCC, 2019). Studies on the ecological characteristics of P. farcta, surveys in Türkiye and modeling indicate that it will become widespread in Türkiye and may pose significant problems in agricultural ecosystems.

Türkiye’s geographical location and rich biodiversity make it an important habitat globally (Onen, Ozcan, 2010). However, as in rest of the world, Türkiye is facing a significant biodiversity crisis due to rapidly changing land use and climatic changes (Şekercioğlu et al., 2011). It has been reported that the destruction of natural areas and changes in land use in the country during recent years have threatened many ecosystem services (Bülbül et al., 2022). In addition, it has been reported that many weed species in the flora of Türkiye have been introduced in recent years with global climate change and affected local biodiversity (Jabran et al., 2015; Önen, 2010; Onen, 2015; Onen et al., 2023; Sırrı, 2022a; Uludag et al., 2017). Extreme environmental conditions caused by climate change (temperature increase and drought) may lead to the spread of native weed species with high adaptability and competitive power such as P. farcta and some naturalized invasive plants to much larger areas (Farooq, 2018; Onen, 2015). For this reason, it is very important to determine the responses of weeds to changing climate.

The current study indicated that P. farcta may become widespread in regions with high agricultural activities, i.e., the Mediterranean, Aegean, Marmara and Central Anatolia in Türkiye. In previous studies on weed flora in agricultural ecosystems, areas favorable to P. farcta were similar to high potential areas predicted by the model in the current study (Akkuzu, 2012; Bükün, 2005; 1995; Güner et al., 2012; Mennan, Işik, 2003; Orel, 1996; Sırrı, 2019; Uremis, 2005). Because P. farcta is associated with arid and semi-arid climatic zones, has no soil selectivity, a spiny form and deep root structure, it is considered a weed that may cause problems in agricultural and non-agricultural areas by being able to adapt to new environmental conditions that will arise in the future due to climate change more rapidly than most other weed species (Bükün et al., 1995; Orel, 1996; Uremis, 2005). P. farcta is like other species belonging to the genus Prosopis which are invasive globally and cause serious economic, ecological and health problems in the ecosystems where they are invasive (Hussain et al., 2021; Kaur et al., 2012). Although P. farcta is controlled by certain herbicides under the current climate (Akkuzu, 2012), there are serious concerns that climate change will lead to evolution of herbicide resistance in weeds and the decrease the effectiveness of available herbicides (Varanasi et al., 2016; Ziska, 2020). Therefore, weed management strategies will need to be planned by considering the climate-induced changes (Onen, Ozcan, 2010; Waage, Reaser, 2001). In addition, new weed-crop interactions may be emerge in these agricultural ecosystems in the future calling for the development of climate-resilient weed management strategies.

5.Conclusions

The current study revealed the climatic change would exert a significant effect on the distribution of P. farcta in Türkiye. Mean temperature of the driest and the wettest quarter will exert the highest effect on the distribution of the species in the country. The model predicted that species could extend its distribution range by 51–62% under severe climate change and 49–54% under mild climate change scenario in the future. The species is predicted to become widespread in regions with high agricultural activities, i.e., Mediterranean, Aegean, Marmara and Central Anatolia in Türkiye. Therefore, novel weed-crop interactions may emerge in these region which would demand increasingly sophisticated weed management strategies in response. The results of the study can be used to design proactive measures for limiting the spread of P. farcta.

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• Funding:
  This research received no external funding.

Edited by

• Editor in Chief:
  Anderson Luis Nunes

• Associate Editor:
  Luis Antonio de Avila

Publication Dates

• Publication in this collection
  13 Jan 2025
• Date of issue
  2024

History

• Received
  4 Dec 2023
• Accepted
  18 Oct 2024