2.1. The roles of fog in ecosystems
Fog is a pivotal component to control limiting resources in cloud forests through hydrologic, nutrients, and energy cycles. These three aspects significantly related to forest growth.
Hydrologic cycle
Plants abilities to uptake water from the air enhance their survival during drought seasons and affect biotic interaction. Despite physically intercepting water in the atmosphere, over 85% of species can directly uptake it (Oliveira et al. 2014). Their absorbing strategy included by foliar and by bark (Mason Earles et al. 2016). Besides, the intercepted of fog play a water supplement role as it drip from the canopy into soil (Goldsmith et al. 2013) or go down to soil via stemflow (Hutley et al. 1997). The additional water supply to soil can be utilized by nearby root systems and understory plants. On the other hand, the presence of fog may decrease vapor pressure deficit (VPD), which affect evaporation and transpiration of plants (Burgess and Dawson 2004; Oliveira et al. 2014).
The nutrients cycle
The concentrations of both harmful and nutritious solutes (e.g. SO42-, NO3-, and NH4+) in fog were estimated to have larger amount comparing to that of in precipitation (Wrzesinsky and Klemm 2000), which can affect edaphic and physiological features of plants (Gottlieb et al. 2019). However, the chemical composition of fog shows patterns depending on local land use and location, and could vary largely among fog events (Wrzesinsky and Klemm 2000), which make them difficult to estimate. For example, coastal regions tend to experience Na+ and Cl- , which come from ocean; urban and
industrial regions were recorded more sulfuric and nitric acids due to pollution;
agricultural and arid areas expose to NH4+, K+, Ca2+, and Mg2+ because of biomass burning and dust (Weathers et al. 2020). In Chilan Mountain, chemical composition of fog shared the similar pattern (mostly SO42-, NO3-, and NH4+) as other areas but with much lower magnitude than them, which indicated higher quality of fog in this area (Chang et al. 2002). Apart from inorganic nutrients, microbes, such as bacteria, fungi, and viruses, were found that they can be transported through fog between terrestrial and marine ecosystems (Evans et al. 2019).
Energy cycle
As for energy cycle, fog water may obscure the solar irradiance, and affect plant growth and photosynthetic performance (Letts and Mulligan 2005) in both positive and negative way depending on cloud thickness, position, sun direction and plant species (Gu et al.
1999; Johnson and Smith 2008). In one scenario, the water vapor may obscure the direct solar radiation, but also gain much diffuse solar radiation, which in turn lead to total solar radiation increasing (Gu et al. 1999). In another scenario, there are also cases that fog have no effects on photosynthesis of fir seedlings or reduced photosynthesis of Rhododendron by 40% (Johnson and Smith 2008).
As a result, the interaction of the forest growth and fog presence is varying, and strongly controlled by local environmental factors and plant species. Thus, it is necessary to have long term and large scale fog event monitoring projects in different ecosystems to access the big picture of how fog events affect forest growth and the whole forest ecosystems.
2.2. Fog quantification approaches
The tiny droplets among fog and its inconstant characteristic at small local scale make the fog quantification processes full of challenges. It is hard to quantify by simple field instruments and so that make the heterogeneous montane areas unpredictable without dense data collection (Gu et al. 1999). Therefore, the studies of fog are limited compare to the studies of other much easier quantified environmental factors, like precipitation, temperature, wind speed, humidity etc. Instead of precisely measuring fog water quantity, measuring fog duration or fog frequency may be a better way to understand fog features.
Fog duration approach cannot estimate exact fog water input, yet usually can observe long-term and large spatial scale fog dynamics with lower cost. Here listed two examples of fog duration quantification methods, and their potential defects. Firstly, time-lapse photography, quantified fog frequency by continuously shooting environmental photos and classifying them into cloudy or foggy by means of cloud-sensitive image characteristics algorithm (Bassiouni et al. 2017). This method can obtain up to 90%
accuracy during both day and night, but its applicable was limited because of two reasons.
First, the parameters of cloud-sensitive image characteristics set in the study cannot widely apply to other sites. The classification results successfully test in the study would fail in another due to input solar intensity or machine specification difference. Second, time lapse camera was high battery-consume and usually without waterproof, which made it unstable in the high humidity field; The other method, time series satellite images combined with in-situ data, provided a global scale perspective about cloud forest distribution and fog frequency (Obregon et al. 2014; Thies et al. 2015; Wilson and Jetz 2016). Fog duration and frequency researches, which are conducted by satellite technology, commonly face unrefined spatial resolution challenges because fog displays
highly spatial heterogeneity over short distance.
The fog duration or the fog frequency provides a crucial clue for further estimating canopy level fog water deposition and the ecohydrology of cloud forest systems (Bassiouni et al. 2017). Their potential applications include improving performance of ecophysiological model prediction and niche-based modeling of species distribution, which are important under climate change (Oliveira et al. 2014).
2.3. Summary
Fog profoundly controlled both above and below ground parts of forests by means of regulating water, nutrient (both organic and inorganic way), and energy cycles. To know more about roles of fog in cloud forest, fog duration may be the one with less costly but the most detailed approach in the wake of satellite higher temporal and spatial resolution development. Owing to the advancement of remote sensing technology, we now can identify basic pattern of tropical cloud forests around the world using satellite imagery.
Yet its resolution part have a long way to improve, especially those cloud forest in isolated island and coastal regions. Besides, pantropical regions were needed to take into account in the context of climate change.