Spatial and Temporal Effects of Wind Speed on Global Vegetation Growth

doi:10.21203/rs.3.rs-1947975/v1

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Spatial and Temporal Effects of Wind Speed on Global Vegetation Growth

https://doi.org/10.21203/rs.3.rs-1947975/v1

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Wind effects on vegetation growth have not been examined at the global scale. In this work, the spatial and temporal effects of wind speed on global Solar-induced Chlorophyll Fluorescence (SIF) and Gross Primary Production (GPP) were analyzed. The results show: (1) Spatially, wind has almost the same area percentage (about 12%) of positive influence on SIF (GPP) no matter SIF (GPP) changes or not. Wind itself explains the negative impact more than it does the positive impact. (2) Temporally, there is significant increase in the area percentage (0%~3.9% each year) and magnitude of positive wind effects on SIF and GPP globally for most of vegetation types from 2001 to 2018. (3) Based on SIF and GPP, different types of vegetations enjoy different level of optimal wind speeds. This work provides an overall picture of wind speed effects on vegetation growth globally, and are important for understanding sustainability while climate changes.

Global Vegetation

Wind

Plant Growth

Solar-indued Chlorophyll Fluorescence

Gross Primary Production

Plants play a critical role in global carbon assimilation and food supply (Pan et al. 2011; Pessarrodona et al. 2018). As an important meteorological factor, wind influences directly on plant growth or interacts with other environmental factors such as humidity, temperature, and solar radiation (Good et al. 2021; Hihara et al. 2015; Saatchi et al. 2021; Traxl et al. 2021) further affects plant growth in a complicated manner. In general, light wind increases the gas exchange rate of leaf stomata and improves photosynthesis and assimilation capacity (Wu et al. 2021), while strong wind induces the opposite effect. With unpredictable climate changes, limited agriculture land, and increased population, studying the overall effects of wind speeds on vegetation growth at the global scale is very important to sustainability evaluation. However, investigations of wind effects on plants have been limited to laboratory or small scales. There is a lack of a holist picture of wind effects on plant growth globally.

Satellite-borne solar-induced chlorophyll fluorescence (SIF) imagery has become a novel proxy to monitor photosynthetic activity in various spatial scales and it has been widely used for assessment of plant stresses and other physiological characteristics (Gerlein-Safdi et al. 2020; Chang et al. 2021). Gross primary production (GPP) by terrestrial vegetation has been associated with the largest CO₂ flux and can be used to track the global carbon source-sink relationship (Sun et al. 2017). The effects of meteorological factors on SIF and GPP are complicated. In the literature, there are lot of studies on analyzing the relationship between SIF/GPP and temperature, precipitation, or radiation at different scales (Chen et al. 2021; Liu et al. 2018; Xu et al. 2021). SIF and GPP may be used as measures to study vegetation growth (Magney et al. 2019; Guanter et al. 2014)

To provide a holist picture of wind speed effects on plants, SIF and GPP are used to evaluate wind effects on the growth of global vegetations in this work based on the Global Land Data Assimilation System (GLDAS)data, SIF and GPP data, and the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6 data product. Optimal wind speed was also studied for the growth of each type of vegetation at the global scale.

GLDAS data.

The Global Land Data Assimilation System (GLDAS) model is jointly established by the National Aeronautics and Space Administration (NASA), Goddard Space Flight Center (GSFC), the National Oceanic and Atmospheric Administration (NOAA) and National Centers for Environmental Prediction (NCEP) of the United States (Rodell et al. 2004). The GLDAS datasets which were retrieved from https://avdc.gsfc.nasa.gov/ provide the 10-meter wind speed (WS) for the entire time span with a spatial resolution of 0.25 degrees and a temporal resolution of one month. For this study, we selected the wind speed data from 2001 to 2018.

GOSIF Data and GPP data.

SIF brings major advancements in measuring the terrestrial photosynthesis. GOSIF is a more refined global SIF dataset based on OCO-2 with high spatial and temporal resolution, which is important for detecting plant growth. GOSIF GPP used the global, OCO-2-based SIF product (GOSIF) and linear relationships between SIF and GPP to map GPP globally (http://globalecology.unh.edu/data/GOSIF.html) (Li et al 2019).In this study, we used GOSIF and GOSIF GPP data (temporal resolution of months and spatial resolution of 0.05 degrees) from 2001 to 2018 as indicators of plant growth to investigate the temporal and spatial effects of wind speed on SIF and GPP.

Data products of MODIS.

The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6 data product provides a spatially aggregated and reprojected version of the tiled MCD12Q1 Version 6 data product (https://lpdaac.usgs.gov/). We used the 2014 International Geosphere-Biosphere Programme (IGBP) dataset with a spatial resolution of 0.05°, which includes ten land cover types (Evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, closed shrublands, open shrublands, woody savannas, savannas, grasslands) but excludes areas with little vegetation such as snow, ice, water bodies, wetlands, deserts, and areas with high anthropogenic disturbance such as farmland and towns (Sulla-Menashe et al 2019).

Trend analysis.

In order to analyze the trends of global SIF and GPP Eq. (1) was used for calculation:

$$\text{slope }=\frac{\text{n}\times \sum _{\text{i}=1}^{\text{n}} {\text{x}}_{\text{i}}{\text{t}}_{\text{i}}-\left(\sum _{\text{i}=1}^{\text{n}} {\text{x}}_{\text{i}}\right)\left(\sum _{\text{i}=1}^{\text{n}} {\text{t}}_{\text{i}}\right)}{\text{n}\times \sum _{\text{i}=1}^{\text{n}} {\text{t}}_{\text{i}}^{2}-{\left(\sum _{\text{i}=1}^{\text{n}} {\text{t}}_{\text{i}}\right)}^{2}} \left(1\right)$$

where slope is the change in SIF or GPP per year, ${x}_{i}$ is the value of SIF or GPP, $n$ is the total number of years under investigation ($n$ =18), and ${t}_{i}$ denotes the year from 2001 to 2018 (1–18). A positive (negative) slope value represents increase (decrease) in SIF or GPP. A slope value with a p < 0.05 was considered significantly different from zero.

slope and p were used to classify the global SIF and GPP areas into three categories. If p > 0.05, an area was considered an area without significant change (AWSC); if slope > 0, and p < 0.05, it was classified as an area with significant increase (AWSI); if slope < 0, and p < 0.05, it was classified as an area with significant decrease (AWSD). Finally, the area percentage of each of the three categories was calculated in terms of the number of pixels in the category.

Linear regression models.

Linear regression models of SIF, GPP vs. wind speed were developed according to Eq. 2.

$$Y=K\times WS+A+\epsilon \left(2\right)$$

where Y denotes the monthly SIF or GPP, and WS denotes the monthly average wind speed, K is the standardized regression coefficient, A is a constant, and ε is an error term.

Classification of global wind speed.

Natural breaks were found to categorize wind speed. The classification intervals were identified so that similar values could be appropriately grouped and differences between classes could be maximized. We analyzed the frequency variations of wind speed over the years and found that the overall distribution was approximately normal. The Jenks method was thus used to divide wind speed into 10 intervals to maximize the differences between them.

Determination of significant wind speed intervals for vegetations.

A linear regression equation of SIF or GPP vs. wind speed was developed for each of the 10 wind speed intervals, each of the 10 vegetation types, and each location or pixel on the map. A p-value < 0.5 for regression was considered indicative of significant dependence of SIF or GPP on wind speed. The number of significant pixels for the j^th wind speed interval of the i^th vegetation type was denoted as n_SIF/GPP(i,j), where i is from 1 to 10, representing evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, closed shrublands, open shrublands, woody savannas, savannas, and grasslands, respectively; and j is from 1 to 10, representing wind speed intervals (0.59 to 1.51), (1.51 to 2.39), (2.39 to 3.01), (3.01 to 3.56), (3.56 to 4.10), (4.10 to 4.68), (4.68 to 5.35), (5.35 to 6.06), (6.06 to 7.19), and (7.19 to 11.25) (m/s), respectively. N_SIF/GPP(i) is the sum of the number of significant pixels for the 10 wind speed intervals for the i^th vegetation types. M_SIF/GPP$\left(i,j\right)$ represents the ratio between n_SIF/GPP(i,j) and N_SIF/GPP(i).

$${N}_{\frac{SIF}{GPP}}\left(i\right)={\sum }_{j=1}^{10}{n}_{\frac{SIF}{GPP}}\left(i,j\right) \left(3\right)$$

$${M}_{\frac{SIF}{GPP}}\left(i,j\right)=\frac{{n}_{\frac{SIF}{GPP}}\left(i,j\right)}{{N}_{\frac{SIF}{GPP}}\left(i\right)} \left(4\right)$$

If M_SIF/GPP (i, j) < 5%, we excluded the j^th wind speed interval of the i^th vegetation; if M_SIF/GPP (i, j) > 5%, we calculated the average standardized regression coefficient K and the pixel percentage with K > 0.

Spatial effects of wind speed on SIF and GPP.

The effects of wind speed on SIF and GPP were studied by using the standardized regression coefficient K and coefficient of determination R² of a linear model with wind speed as the independent variable and SIF/GPP as the dependent variable (See Methods). Figure 1 (A, B) present the K spatial distributions for wind speed effects on SIF/GPP, respectively (The R² distribution can be found in Figure S1 in Supplementary Information). A K larger (less) than zero indicates that wind speed has positive (negative) effect on SIF or GPP. A higher R² indicates that wind speed can be used to predict more variations in SIF or GPP. Areas with an insignificant K (p > 0.05) were excluded. The results show that 61.2% of the areas for SIF and 44.6% of the areas for GPP were significantly affected by wind. The spatial distribution pattern of K (Fig. 1A, B) and R² (Figure S1 A, B) are almost the same for SIF and GPP except for the region from 60°N to 90°N, where the effects of wind speed on GPP are insignificant but the effects on SIF are significant and positive. In the Amazon Rain Forest, Eurasia, and some coastal regions (Australia, South Africa, Oman, and India), the K value is greater than that in other regions for SIF and GPP (Fig. 1A, B). Regions of eastern United States (US), Brazilian Plateau, and some areas in Africa (Lunda Plateau, Katanga Plateau, and approximately the 10°N region) have lager R² values than those in other regions for both SIF and GPP (Figure S1 A, B).

The areas with stronger positive (K being close to 1) wind speed effect on SIF and GPP are mainly located in the northern part of South America (Fig. 1A, B), where most of the vegetation types are broadleaf evergreen forests. The areas with stronger negative (K being close to -1) wind speed effect on SIF and GPP are mainly located in central Africa (Fig. 1A, B), where most of the vegetation types are grasslands. For the rest of areas, distributions of wind speed effect on SIF and GPP are scattered without an obvious pattern.

By analyzing the spatial distribution of wind speed effects on SIF and GPP, we found that the influence of wind speed on vegetation may be related to vegetation type, but different types of vegetation may not be present in all areas. Therefore, we further studied the influence of wind speed on different vegetations. Based on the MODIS data (MCD12C1), ten types of major vegetation were studied (evergreen needleleaf forests, evergreen broadleaf forest, deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, closed shrublands, open shrublands, woody savannas, savannas, and grasslands) (Fig. 2A). Figure 2B shows the annual wind speed mean values (from 2001 to 2018) for the ten types of vegetation. The mean annual wind speeds in descending order for these vegetation types are: open shrublands, closed shrublands, grasslands, evergreen needleleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, deciduous needleleaf forests, and evergreen broadleaf forests. Figure 2C shows the standardized regression coefficients K (regression models for SIF or GPP vs. wind speed) for the ten vegetation types. The averages of K for SIF in descending order for these vegetation types are: open shrublands, evergreen broadleaf forests, deciduous needleleaf forests, grasslands, closed shrublands, woody savannas, evergreen needleleaf forests, savannas, mixed forests, and deciduous broadleaf forests. The averages of K for GPP in descending order of magnitude for these vegetation types are: evergreen broadleaf forests, open shrublands, closed shrublands, deciduous needleleaf forests, grasslands, woody savannas, evergreen needleleaf forests, mixed forests, savannas, and deciduous broadleaf forests. Although, the orders for SIF and GPP are not exactly the same, they are very similar considering the large span of vegetation distribution. In general. Of these ten vegetation types, the proportions of wind speed that had a positive effect on SIF and GPP are all less than 50%; however, the proportion of wind speed that had a positive effect on SIF and GPP in evergreen broadleaf forests (SIF in open Shrublands) was the closest to 50%. SIF and GPP for most other vegetation types are under negative influence of wind speed (Fig. 2D), indicating winds being too strong for their growth need directly or indirectly. The specific spatial distribution of wind speed effects on SIF and GPP for different vegetations can be seen in Figure S4 and Figure S5, respectively. In north-central South America, wind speed positively influences the SIF and GPP of the local evergreen broadleaf forest overall. For most of the evergreen needleleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, and open shrublands, the lower the wind speed, the higher the SIF and the GPP. In most grassland areas near the equator, wind speed has a strong negative effect on SIF and GPP.

GPP or SIF might change as a result of various reasons over 18 years. For further analysis of wind speed effects, it is meaningful to evaluate wind effect in the areas with increasing, decreasing, or no change in SIF/GPP over the 18 years individually. For GPP, the global areas are separated into three categories: areas with significant increase in GPP (AWSI), areas with significant decrease in GPP (AWSD), and areas without significant change in GPP (AWSC) (Figure S2, see Supplementary Information) according to GPP trend of variation (Eq. 1). The AWSI, the AWSD, and the AWSC account 28%, 3%, and 69% (p < 0.05) of the total areas, respectively. The color in Figs. 3A, 3B, and 3C represents the standardized regression coefficient K between wind speed and GPP. Area percentage for K < 0 accounts for 88.2%, 88.3%, and 89.0% in AWSI, AWSD and AWSC areas, respectively; area percentage for K > 0 accounts 11.8%, 11.7%, and 11.0% in AWSI, AWSD, and AWSC, respectively (Fig. 3D). Surprisingly, the area proportion with K > 0 or K < 0 is almost the same in AWSI, AWSD, and AWSC for GPP. Following the same procedures, the results for SIF can be computed, which also show that the area proportion with K > 0 (about 12%) or K < 0 (about 88%) is almost the same in AWSI, AWSD, and AWSC for SIF (Figure S3).

Temporal effects of wind speed on SIF and GPP.

In order to analyze the temporal effect of wind speeds on vegetation, we took the SIF and GPP data of 18 years (from 2001 to 2018) and divided them into 13 consecutive 6-year sliding windows in 1-year increment: the first period is from 2001 to 2006, and the second period is from 2002 to 2007, and so on (See Methods). In each of the six years, a regression equation between wind speed and SIF or GPP was established and the proportion of the areas with K > 0 was recorded. The percentage of the areas with K > 0 increases by nearly 2.8 times from 8.77–24.66% for SIF, while by about 2.5 times from 6.26–15.79% for GPP, which shows a successively increasing trend over time for SIF and GPP (p < 0.001) (Fig. 4A). The average of standardized regression coefficients K reduced by 60% from − 0.45 to -0.27 for SIF, and by 72% from − 0.50 to -0.36 for GPP, which also shows a successively increasing trend over time for SIF and GPP (p < 0.001) (Fig. 4B).

Similar to studying the spatial effect, we studied the change in wind speed effects on SIF and GPP of different vegetations from 2001 to 2018. The percentage of the areas with K > 0 increased by1.0% (1.0%), 1.2% (1.2%), 2.8% (2.6%), 0.3% (0.3%), 0.3% (0.3%), 3.9% (0.6%), 1.7% (1.6%), 1.7% (0.9%), and 1.7% (0.8%) in per 6 consecutive years for SIF (GPP) in evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, open shrublands, woody savannas, savannas, and grasslands, respectively, which shows a successively increasing trend with time (p < 0.001); however, for the SIF and GPP of closed shrublands, the percentage of the areas with K > 0 did not change significantly (p > 0.5) (Fig. 5A and B); the average of standardized regression coefficient K increased by 0.02 (0.02), 0.01 (0.01), 0.03 (0.03), 0.01 (0.01), 0.01 (0.01), 0.01 (0.01), 0.03 (0.01), 0.02 (0.02), 0.02 (0.01), and 0.02 (0.01) per 6 consecutive years for SIF (GPP) in evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, closed shrublands, open shrublands, woody savannas, savannas, and grasslands, respectively, which again shows a successively increasing trend with time (p < 0.05) (Fig. 5C and D).

Optimal wind speed for the growth of each type of vegetation.

Optimal wind speed for the growth of each type of vegetation was analyzed based on GIP and SIF. To do so, the 18 years of wind speed data were synthesized into annual mean wind speed (Fig. 6A) and then the annual mean wind speed data were further classified into 10 intervals by using the Jenks Natural Breaks Classification to maximize the difference between two neighboring intervals (See Methods), which tends to increase from low to high latitudes and from low to high altitudes. The 10 intervals are (0.59 to 1.51), (1.51 to 2.39), (2.39 to 3.01), (3.01 to 3.56), (3.56 to 4.10), (4.10 to 4.68), (4.68 to 5.35), (5.35 to 6.06), (6.06 to 7.19), and (7.19 to 11.25) m/s. Figure 6B shows the global mean wind speed distribution, which is close to a normal distribution.

For each type of vegetation, a map pixel was classified into one of the 10 wind speed intervals first. If a wind speed interval contains fewer than 5% of the total number of pixels, the interval would be ignored (See Methods, Figure S6 and Figure S7). Linear regression was performed between the monthly SIF (GPP) and monthly average wind speed for each of the pixel. The average K and the pixel percentage with K > 0 (p < 0.05) were computed for each of the 10 wind speed intervals for each of the vegetation types, and the results are shown in Fig. 6 and Figure S8, respectively.

As seen in Figs. 6C, D, E, F, G, H, I, J, K, and L, the effects of wind speed on SIF and GPP are similar in terms of average K among the different vegetation types. Evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, and mixed forests belong to the forest system and their distributions are more concentrated in certain latitudinal ranges (Fig. 2A). It is relatively clear that the SIF and GPP of these five forest vegetation types have decreasing wind effect (reducing K) with increasing wind speed (Fig. 6C, D, E, F, and G). K is close to 0 in the (0.59, 1.51) m/s interval for evergreen broadleaf forests and in the (1.51, 2.39) m/s interval for deciduous needleleaf forests. The proportion of areas with positive impact of wind speed on SIF and GPP in evergreen needleleaf forests, deciduous broadleaf forests, and mixed forests is less than 10% (Figures S8 A, B, C, D, and E), but the percentage of areas with positive wind speed effects is close to 50% in the (0.59, 1.51) m/s interval for evergreen broadleaf forests and in the (1.51, 2.39) m/s interval for deciduous needleleaf forests, indicating that globally, based on SIF and GPP, evergreen broadleaf forests and deciduous needleleaf forests prefer higher mean annual wind speeds than the other three types of forest vegetation. Closed shrublands, open shrublands, woody savannas, savannas, and grasslands are globally dispersed in such a large latitudinal range that the optimal wind speed varies for them because of variations in other environmental conditions. According to Fig. 6A, wind speed increases from low to high latitudes and from low to high altitudes. Optimal K values for the growth of savannas and grasslands also increase with latitude or altitude (Fig. 6K and L). The effect of wind speed on SIF of open shrublands has a clear trend of changing from positive to negative with increasing latitude or altitude, while the trend of wind speed effect on GPP of open shrublands over latitude or altitude is not so obvious (Fig. 6I). The changes in K values for closed shrublands and woody savannas are not significant with increasing latitude or altitude (Fig. 6H, J).

Wind interacts with other environmental and climate factors, affects plant physiology, and influences plant growth (Thomey et al. 2019; Mogollón et al. 2018; Sperry et al. 2019). Most previous studies focused on the effects of temperature and precipitation on global plant growth (Pinke et al. 2017; Cox et al. 2020; Roy et al. 2016). Since wind as a major part of a changing climate continues to affect the structure and function of the vegetation ecosystems, vegetation ecologists and vegetation resource managers are interested in a better understanding of the overall effects of wind on different vegetations.

Spatial effects of wind speed on different type of vegetations.

Based on the spatial analysis of wind speed effects on global SIF and GPP, we found that such effects are related to vegetation types. The results show that wind speed have a significantly larger impact on SIF and GPP of broadleaf evergreen forests in the forest system and open shrublands in the shrub type. Broadleaf evergreen forests in northern South America are tall and dense with high moisture. Air circulation and more light capture might be critical to broadleaf evergreen forests. A higher wind speed can increase air flow rate between dense plants and also changes leaf morphology to adapt to the directions of light radiation (Feng et al. 2019; Yan et al. 2016), thus improves photosynthesis (Bagley et al. 2015; Anten et al. 2010) of broadleaf evergreen forests. This may explain why wind has a strong positive effect on broadleaf evergreen trees. For all other areas of vegetations, a positive effect of wind speed is less common. This phenomenon is most evident in the central African grasslands with shorter vegetations and thinner rhizomes, where the mean annual wind speed is greater than in other areas. Grass is not as high and dense as broadleaf evergreen forests and open shrublands. Air circulation and light capture may not be limiting factors for plant growth. In fact, high wind speed may reduce soil moisture, cause physical damages, and weaken plant photosynthetic activities (Werger et al. 2020; Louf et al. 2018; Scholl et al. 2021; Peterson 2020). This may explain why wind speed has strong negative effects on SIF and GPP in central Africa. For the other areas, the distributions of wind speed effect on SIF and GPP are scattered without obvious patterns.

Figure 1A and Figure S1A (for SIF) or Fig. 1B and Figure S1B (for GPP) show that, although the coefficient of determination R² and standardized regression coefficient K are positively correlated when K > 0 and negatively correlated when K < 0, the relationship between R² and K is not symmetric about the y axis (Fig. 7). It is noticeable that the K-R² curve is more vertical (higher R²) and gets closer to the perfect point of (-1, 1) when K < 0 than it is or does to (1, 1) when K > 0 for both SIF and GPP. This means that when winds have a negative effect (K < 0), SIF and GPP can be more accurately predicted from wind speed. This may suggest that wind needs to be combined with other environmental factors such temperature and humidity to impart a positive effect but wind itself is enough to limit plant growth and show a negative impact. Due to wind has negative impact on vegetations in general and itself is enough for incurring negative impact, the results that the area percentages of (positive or negative) wind effects on SIF(GPP) are very consistent in the areas with or without SIF (GPP) changes.

Temporal effects of wind speed on different types of vegetation.

The proportion of areas positively affected by wind speed and the average of standardized regression coefficients K on global SIF and GPP increased significantly vs. years (Fig. 4). For each of the ten vegetation types, the proportion of areas positively affected by wind speed and average of standardized regression coefficients K for a majority of vegetations increased significantly from 2001 to 2018 (Fig. 5). Table 1 shows the major areas of increasing positive wind effect by different vegetations based on SIF and GPP from 2001 to 2018. This implies that changes in the interactions between wind and plants are beneficial to plant growth in general and global sustainability in the past 17 years while climate changes. It also implies that wind effect should be considered in the analysis of global vegetation growth.

Table 1

Major areas of increasing standardized regression coefficients K for the regression model of SIF (GPP) vs. wind speed for different vegetation types from 2001 to 2018
Vegetation types	Main areas of increasing positive impact
Evergreen Needleleaf Forests	Western Canada
Evergreen Broadleaf Forests	Eastern and Northern Peru, Indonesia
Deciduous Needleleaf Forests	Central East Russia
Deciduous Broadleaf Forests	Central China, Central East India
Mixed Forests	Central China, West-central Canada, Southeastern Russia
Open Shrublands	Areas above 55°N latitude
Woody Savannas	Western Canada, Central China, East Central Russia
Savannas	Northwestern Canada, Eastern coast of Australia, Central India
Grasslands	Western USA, Central Argentina, Southern Kazakhstan

Optimal wind speeds for different types of vegetation.

All the five types of forests (Evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, and mixed forests) cover a small and fixed latitudinal range, and they have similar other conditions such as sun light incident angle and temperature. The results show that among the five types of forests, evergreen broadleaf forests and deciduous needleleaf forests prefer a higher mean annual wind speed. This is mainly related to specific characteristics (e. g. tree species, tree height and diameter, crown area, root depth and width, stand density, soil type and topography) (Gardiner et al. 2016; Peltola et al. 2006). The other five vegetation types (closed shrublands, open shrublands, woody savannas, savannas, and grasslands) are so widely distributed that a general annual mean wind speed does not seem to exist though a negative effect of wind speed on closed shrublands (savannas and grasslands) increases (decreases) was observed with increasing latitude or altitude. This may be related to changes in sunlight, temperature, and rain falls mediating the effects of wind speed variations over latitude or altitude (Chiang et al. 2019; Johansen et al. 2017).

This study provides a holistic assessment of the spatial and temporal impacts of wind speed on SIF and GPP and reveals a suitable wind speed for each of the ten vegetation types on a global scale. While this research fills the gap of wind influence on plants, there are still some limitations, (1) the results are limited by the temporal and spatial resolutions of the data from satellites, and (2) this work evaluates only the overall wind effects without considering the interaction effects with other environmental and plant physiological factors. In future work, refined algorithms and data from multiple satellites can be used to improve the resolution and study the effects of winds on plants in a more comprehensive manner.

The spatial and temporal effects of wind speed on global plant growth were explored based on SIF and GPP. It was found that the spatial impact of wind speed on global plant growth is mostly negative; the distribution of impact mainly depends on the local vegetation type; wind itself explains the negative impact more than it does the positive impact. It was also found that the negative impact of wind speed on plant growth has reduced over the 18-year period (from 2001 to 2018). The results show that evergreen broadleaf forests and deciduous needleleaf forests prefer higher optimal mean annual wind speeds than evergreen needleleaf forests, and wind speed effects on savannas and grasslands change with latitude or altitude. This work helps understand plant growth as affected by winds, and provides knowledge and data for evaluating forest management strategy and protecting natural woodlands and vegetations globally.

Acknowledgements.

This project is partially supported by the National Natural Science Foundation of China (No: 51961125102, 31771680), and the 111 Project (B12018).

Author contributions.

Zhiwei Li performed the analyses and wrote the first version of the manuscript. Lijiang Fu, Zhiwei Li, Wei Guo, Li Song, and Hao Tang assisted with data collection and data processing. Li Song and Jinglu Tan contributed to discussions of the results and revision of the manuscript. Zhenyu Xu contributed to result analysis. Ya Guo was responsible for designing the study, interpretating the results, seeking funding, and revising the manuscript. All authors read and approved the final manuscript.

Competing interests.

The authors declare that there is no competing interest.

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Spatial and Temporal Effects of Wind Speed on Global Vegetation Growth

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