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Tropical Montane Cloud Forests
5.05
Tropical Montane Cloud Forests
DK Ray, Institute on the Environment, University of Minnesota, Saint Paul, MN, USA. Ó 2013 Elsevier Inc. All rights reserved.
5.05.1 5.05.2 5.05.2.1 5.05.2.2 5.05.3 References
Tropical Montane Cloud Forests and Their Relationship to Water Input Stability of Tropical Montane Cloud Forests Impacts of Global Changes Impacts of Regional Changes Bottom–Up Solutions
5.05.1 Tropical Montane Cloud Forests and Their Relationship to Water Input Tropical montane cloud forests (TMCFs) are unique tropical ecosystems that occur as narrow altitudinal bands between 800 and 3500 m on mountains in the continents of Africa, America, and Asia and in equatorial Oceania (Bubb et al. 2004; Kapelle 2004; Bruijnzeel et al. 2011) (Figure 1, also see Figure 1 of Bruijnzeel et al. 2011). The defining characteristic of TMCFs is persistent cloud cover/fog at the vegetation or ground level that ensures that the tree crowns are regularly in contact with cloud water. The cloud water in turn contains drops of an entire spectrum of sizes from suspended droplets to falling rain-sized droplets. Cloud water directly condenses on the vegetation surface in TMFCs as precipitation. This phenomenon is variously referred to in the literature as ‘horizontal precipitation,’ ‘direct interception,’ or ‘cloud water stripping.’ The cloud water that is deposited regularly on the vegetation surface is primarily from orographically lifted air masses that cool to form the clouds (Figure 2(a)), although orographically lifted advection fog, sea fog, steam fog, or radiation fog could also provide a source for horizontal precipitation (Scholl et al. 2010). The singular distinguishing feature of regular immersion in clouds and cloud water input to TMCFs has important implications to vegetation characteristics, vegetation productivity, nutrient uptake, soil and litter composition, and a positive water balance (Table 1). This process is especially important for dependable streamflows in areas downstream that have little dry-season precipitation but where cloud forests are present upstream (Bubb et al. 2004). This unique feature of TMCFs also results in higher species richness and higher biological endemism within the TMCF (Foster 2001; Bruijnzeel et al. 2011). Three types of TMCFs are now recognized: lower montane cloud forest, upper montane cloud forest, and subalpine cloud forest (Bruijnzeel et al. 2011). The transition from lower to upper montane cloud forest coincides with the level where cloud condensation becomes more persistent, whereas that from upper to subalpine TMCF occurs at the altitude where the average maximum temperature falls below 10 C (Bruijnzeel et al. 2011). The current estimates of global spatial extent of TMCFs vary widely from 21 5000 km2 to 2.21 million km2 (Bruijnzeel et al. 2011), partly due to the difficulty of identifying which tropical forests are regularly cloud immersed forests, although remote sensing techniques provide promising new approaches (Nair et al. 2008; Welch et al. 2008; Lawton et al. 2010; Ray et al. 2011). Bubb et al. (2004) estimated that
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2.5% of all tropical forests are cloud forests, with the lowest percentage in the Americas (1.2%) followed by Africa (1.4%) and Asia having the highest percentage (6.6%). Owing to adaptation to regular cloud water interception, TMCF species are vulnerable to droughtlike conditions. Epiphytes, which grow on other plants in the TMCF and derive their moisture from the air, are a defining characteristic of cloud forests, where a quarter of all plants may be composed of various species of epiphytes (Foster 2001). Epiphytes are especially vulnerable to changes in regular cloud water immersion since they can only access intercepted water through organs dedicated to uptake of water deposited on them. Short intense pulses of rain events are thus relatively of little use to epiphytes. However, in many TMCFs, a significant proportion of the water input could be from continuous drizzle that keeps the organs for water uptake, such as roots, wet. Persistent fogginess also has led to specialist anuran and avian species that are adapted to specific altitudes corresponding to cloud frequency of immersion.
5.05.2
Stability of Tropical Montane Cloud Forests
The pressure to feed a growing and wealthier global population and grow biofuel crops has resulted in a net increase of the world’s croplands and pastures by 154 million hectares between 1985 and 2005, which included significant expansions in the tropics and losses in the higher latitudes (Foley et al. 2011). It is estimated that 80% of new tropical croplands are from the conversion of forests (Gibbs et al. 2010). TMCFs are clearly threatened from these direct conversions into agricultural and pastoral lands and other developmental activities such as road constructions, building of dams and reservoirs, and mining activity (Ray et al. 2011). However, even when TMCFs are protected, they are affected indirectly by global, regional, and local processes that change the very critical ‘regular immersion in clouds’ process via changing the upper and lower bounds of clouds, cloud thickness, cloud cover and its frequency, cloud water content, and temperature changes.
5.05.2.1
Impacts of Global Changes
Changes in global scale processes impact TMCFs because the global scale atmospheric circulation of the Hadley cells determines the altitude of the trade wind inversion layer that in turn marks the upper bound of cloud top heights in the tropics.
http://dx.doi.org/10.1016/B978-0-12-384703-4.00519-0
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Figure 1 Global potential (red) and known (green) TMCF distribution. Reproduced from Bubb, P., I. May, L. Miles, and J. Sayer, 2004: Cloud Forest Agenda, UNEP-WCMC, Cambridge, UK. [Available online at http://www.unep-wcmc.org/biodiversity-series-20_105.html].
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Figure 2 Effects of lowland land-use change on TMCF. (a) Current conditions. (b) Lowland deforestation reduces evapotranspiration and pushes cloud base heights upward. (c) Lowland deforestation increases evaporation from wetlands and lowers cloud base heights.
The trade wind inversion layer in turn is due to the descending branch of the Hadley cells and compression and warming of the air (Bruijnzeel et al. 2011). The altitude at which the orographically forced trade winds begin condensing clouds on the other hand determines the cloud base heights and thus the lower bound of the TMCF. The movement of the Intertropical Convergence Zone (ITCZ) also determines the timing and length of the wet season over the TMCF.
Table 1
During the last glacial maximum, the drastically altered global conditions of temperature and precipitation influenced the spatial and altitudinal distribution of TMCFs. Lake pollen records from Andean TMCFs during the ice ages and modeling results show downslope expansion of TMCF species during the conditions of reduced temperature and wetter conditions that existed and consequently lowered cloud bases (Still et al. 1999; Urrego et al. 2005). Lowering of cloud bases allowed the TMCF
Principle hydrometeorological and vegetation characteristics of TMCFs
Climate
Vegetation
Productivity
Water balance
High cloud frequency High relative humidity Low irradiance
High endemism High proportion of epiphytes Stunted trees with small thick and hard leaves
Low photosynthesis rates Low net primary productivity
Additional water input from horizontal precipitation Low transpiration and physical evaporation Streamflow/incident rainfall very high
Adapted from Foster, P., 2001: The potential negative impacts of global climate change on tropical montane cloud forests. Earth-Sci. Rev., 55, 73–106.
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species to colonize the lower elevations. Other studies, however, from Andean TMCFs suggest that during the ice ages aridity increased and fragmented the TMCF (e.g., Mourguiart and Ledru 2003). Clearly, global climate has a profound impact on TMCF spatial and altitudinal distributions. Looking forward, atmospheric carbon dioxide has continued to increase, reaching a globally averaged concentration of 387.2 ppm at the end of 2009 (Friedlingstein et al. 2010). The technological advance required to stabilize greenhouse gas emissions, however, could be unachievable (Pielke et al. 2008), and emissions of carbon dioxide and other greenhouse gases are expected to continue; global climatic changes are occurring via altering the radiative balance of the earth’s ocean–atmosphere system. Future globally averaged warmer conditions at the surface could result from these elevated greenhouse gases, which would increase evaporation from the water bodies that supply moisture leading to more cloudiness. However, at the same time, the condensation level for clouds could increase because of the warmer air thereby reducing cloud cover and the frequency with which cloud immersion occurs at the vegetation level (Still et al. 1999). It is not clear which process would dominate: more humidity and lowered cloud bases or more humidity but higher cloud bases due to warmer air masses at each TMCF site. Nor is there certainty of global climate changes being felt at each TMCF site. However, if the current regime of cloudiness and temperature changes for any reason, it could lead to the shifting of the current species composition, favoring those that are more adapted to the new conditions and extinction of others. Lack of cloudiness, for example, has been implicated in the outbreak of fungal diseases and species extinction of endemic TMCF fauna (e.g., Pounds et al. 2006). Unfortunately, future cloud immersion is nearly impossible to predict at the individual TMCF locations, and studies that predict future conditions can only indirectly estimate the cloud immersion process via changes in other variables such as relative humidity (Still et al. 1999). This is because of multiple reasons: global climate models have coarse resolutions with each climate model grid cell representing areas of more than 1 of latitude– longitude, whereas TMCFs generally occur only as narrow altitudinal belts of less than a degree of latitude–longitude. Moreover, not only do TMCF belts occur as subgrid-scale regions within global climate model grid cells but the global climate models also parameterize subgrid-scale phenomena such as smaller scale clouds that are primarily the ones that bathe TMCF (Lawton et al. 2001). This means that even if global climate models could accurately predict the future climate, future cloud conditions at the vegetation level of TMCFs cannot be directly determined from global climate model simulations. While regional-scale dynamic downscaling from global model forecasts could provide future cloud frequency at the TMCF resolution, it is now quite clear that regional-scale downscaling is unable, at present, to add any value beyond what is already available via historical analyses, recent paleohistory, and worst case sequence of observed weather (Pielke and Wilby 2012). Under such conditions, a bottom–up assessment of historical regional processes that impact TMCF stability is more useful in understanding the indirect hydrometeorological threats to TMCFs. In order to make this assessment, what do past observations and modeling at TMCF sites tell us about their hydrometeorological vulnerability?
5.05.2.2
Impacts of Regional Changes
Modeling and observational studies show that regional landuse change could either raise or lower the altitudes of cloud formation, i.e., cloud base heights, but the magnitude of the impact is highly site specific. Generally, if the air column gets increasingly warmer and drier while traversing the landscape from the moisture sources before being forced to rise orographically, the altitude at which clouds form (cloud base height) also gets raised (Figure 2(b)). Not only the cloud base heights but also the thickness of clouds, cloud water content, and cloud immersion frequency could get reduced, thereby changing the hydrometeorological environment of the TMCF and subsequently its ecology, irreversibly. The amount by which the clouds are impacted differs based on the amount of upwind deforestation, new land use, and sea surface temperature changes. Inland cloud forests like those of southern Mexico and Costa Rica may be profoundly influenced by regional deforestation (Lawton et al. 2001). Multiple numerical modeling simulations from the Monteverde TMCF of Costa Rica (Nair et al. 2003; Ray et al. 2006) show how deforestation reduces cloud cover at the TMCF vegetation level. Originally, tropical moist and wet forests covered most of the Costa Rican lowlands. However, by 1992, deforestation had reduced the forest cover in the lowlands to only 18% of the original forest cover (Veldkamp et al. 1992; Ray et al. 2006). Deforestation converted the forest trees that have a deeper root system into the shallower rooted agriculture (pasture grasses and seasonal agricultural crops have shallower roots, although tree plantation agriculture could have deeper roots). Field observations suggest that trees have access to water stored in deeper soil layers unlike cropland species (Ray et al. 2006). Studies from eastern Amazonia have also shown that up to 75% of all the water extracted during the dry season originates from soil layers below 2 m, which only trees can access (Nepstad et al. 1994). Thus, as the dry season progresses in Costa Rica’s lowlands, evapotranspiration reduces, but because of conversion to cropland it now reduces drastically. This is affecting the cloud cover. Nair et al. (2003) noted that patterns of cloud formation over the forested lowland regions in Costa Rica exhibited linear organization, but over the deforested areas the convection was unorganized. According to them, organized convection over forested lowland areas was indicative of reduced sensible heat fluxes over forests compared to deforested areas. Souza et al. (2000) via measurements of dry-season surface energy fluxes over paired forested–deforested sites in Amazonia showed a pattern similar to that found by Nair et al. (2003) for Costa Rica. The mechanism is explained in the extensively validated modeling study of Ray et al. (2006). The trade winds that blow over the now extensively deforested Atlantic lowlands of Costa Rica during the dry season encounter drier and warmer air due to the reduced evapotranspiration. Via turbulent mixing with this drier and warmer air the trade winds get drier and warmer (Figure 2(b)). It takes the trade winds around 5–10 h to traverse the extensively deforested Atlantic lowland areas of Costa Rica when this mixing process occurs (Lawton et al. 2001). If the lowlands were forested instead, it is very likely that transpiration would be higher because of trees accessing deeper soil moisture and thus the trade winds would have mixed with
Tropical Montane Cloud Forests
cooler and moister air. This in turn would dry and warm the trade winds comparatively less than what happens currently (Figure 2(b)). When these modified air masses from the lowlands now rise up orographically, the altitude at which water vapor begins to condense into cloud droplets is higher; in other words, the cloud base heights have been raised because of the extensive deforestation of the lowlands. Ray et al. (2006) estimate that this impact is greatest in the afternoon hours and has already reduced the areal coverage of cloud immersion by 5–13% because of the w75-m rise in cloud base heights. They estimate that further lowland deforestation could reduce the spatial extent of cloud immersion by another 15% via another w125-m rise in cloud base heights. There are similar bottom–up evidences from other sites as well. Barradas et al. (2010) show that the widespread conversion of cloud-affected montane forests to agriculture and pastures in central Veracruz, eastern Mexico, is affecting the climate. There is an observable altitudinal shift in dry-season precipitation (notably in February), and overall fog frequency has also reduced. Ray et al. (2011) have also shown that if the lowland areas of the Western Ghats in southwest India are further deforested then the cloud base heights would increase in the dry season. This is similar to the mechanism reported from Costa Rica. However, unlike in Costa Rica where precipitation is also expected to reduce with further deforestation (Ray et al. 2010), over the Western Ghats precipitation could contrarily increase in the dry season, although cloud immersion reduces. On the other hand, coastal forests like those of some Caribbean islands (e.g., the Luquillo forest of Puerto Rico) may have too little upwind lowland to experience any lowland deforestation impacts (Lawton et al. 2001). While this may be true for many other coastal TMCFs, Van der Molen et al. (2006, 2010) have suggested a different mechanism and found decreases in cloud base heights and precipitation with deforestation (Figure 2(c)). On the other hand, using historical model simulations, Comarazamy and González (2011) have implicated climatic change as impacting the cloud base heights and precipitation in Puerto Rico’s TMCFs. While the studies of Van der Molen et al. (2006, 2010) and Comarazamy and González (2011) may disagree on the exact mechanism that lowered cloud base heights and reduced precipitation, they were both also partly conducted because water supply in Puerto Rico decreased in the 1990s and led to water rationing, affecting millions (Van der Molen et al. 2010). According to Van der Molen et al. (2006, 2010), deforestation reduced the sensible heat flux because the pastures of Puerto Rico that replaced the forests were well-watered long grasses. Thus, under clear and calm conditions, when the sea breeze dominates the trade wind flow, reduction in sensible heat flux from deforestation reduced the strength of the sea breeze, resulting in lower cloud base heights, but the reduced uplift also lowered the precipitation. Comarazamy and González (2011), however, found a global warming signal driving the reductions in precipitation in Puerto Rico. According to them, between 1955 and 2000 the lower atmospheric temperature has increased between 1.0 and 1.8 C over the Caribbean region but much higher toward the North Atlantic. This has created a pressure difference between the two regions that has intensified and also changed the flow direction of the trade winds in such a way that the axis of the orographic uplift has
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itself shifted. The new flow direction has reduced orographic uplift over the TMCF sites and reduced precipitation, although higher humidity has increased cloud thickness, thus appearing as lowering of the cloud bases. Clearly, there is a lot of variation in the exact mechanism even at a site, and at different times different mechanisms may dominate, but similar bottom–up investigations are urgently required elsewhere to determine the most dominant process and the conditions under which they would dominate. One of the key determining factors for the cloud base height and its prediction at each TMCF site is measuring the Bowen ratio – the ratio of sensible heat flux to latent heat flux. As evapotranspiration reduces, radiative balance is achieved via increased sensible heat flux, and the Bowen ratio increases. A higher sensible heat flux triggers stronger updrafts and higher cloud base heights. Thus, cloud base heights are lowered with lower Bowen ratios (Foster 2001). Historical global climate signals such as those of Comarazamy and González (2011) provide additional layers of information. For example, under calmer conditions, local processes could dominate, and clearly, the nature of the lowland land use would then impact the TMCF. In other places such as Costa Rica, even when the trade winds dominate, the nature of lowland land use would impact the TMCF, as the trade winds mix for long periods with the air column over the lowlands during which they can be significantly modified prior to orographic uplift. Unfortunately, while it is quite clear that similar bottom–up observations and modeling exercises are required, the continent with the largest proportion of TMCF, i.e., Asia, is the least studied and the Americas with the least proportion of tropical forests as TMCFs are the most studied. In general, at each TMCF site, the hydrometeorological conditions are being simultaneously impacted from global, regional, and local factors, but one or more of these generally dominate. Moreover, the occurrence of montane cloud forests and the thickness of the cloud forest belt itself are governed by the altitude of persistent cloud condensation, which needs to be urgently determined to establish a baseline against which changes could be measured. What is, however, clear is that with increasing distance from the ocean, air tends to be drier and therefore requires lower temperatures and higher elevations to reach its condensation point. Consequently, the associated cloud base and TMCF vegetation will occur at higher elevations farther from the oceans. Likewise, at given moisture content, the condensation point is reached more rapidly by cooler air than by warmer air. Thus, at greater distances from the equator, the average temperature, and therefore the altitude at which condensation and TMCFs occur, will be lower. If land-use change reduces evapotranspiration into the air masses as they travel from the oceans to the mountains then the cloud bases will rise and cloud forests at lower elevations will become unstable and vice versa. If global climate changes the direction of flow, then cloud formation will change and TMCFs will become unstable.
5.05.3
Bottom–Up Solutions
Currently, the most serious challenge being faced by TMCFs is from economic activities that are directly destroying
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TMCFs – construction of roads and other infrastructure, timber and mineral extraction, telecommunication facilities, tourist development (including golf courses), hydropower production, illegal plant collection, excessive and inappropriate tourism, and steady encroachment of agriculture (Kumaran et al. 2010; Meyer 2010; Owiunji and Plumptre 2010; Ray et al. 2011). The loss of cloud forests can even induce fires and destroy other areas of the cloud forest. Other destructive activities include grazing or trampling by feral ungulates and invasion by alien plant species (Meyer 2010). Some areas that lost TMCFs are now being allowed to recover because of changed political, economic, and social drivers (Aide et al. 2010). TMCF recoveries are, however, hampered from competition with invasive grasses and ferns and poor seed dispersal (Aide et al. 2010). One of the most costeffective ways to accelerate the recovery of TMCFs is to promote the establishment of shrubs, which helps to shade out invasive grasses and ferns and create more appropriate conditions for seedling growth. This strategy reduces the competition faced by the original TMCF plant species, but clearly, not all TMCF species will recover successfully. Thus, planting will also be required to recover a species composition similar to the intact forest because most forest species are rarely dispersed far from forest stands (Aide et al. 2010). Where modeling studies have already shown that conversion of lowlands to shallow-rooted agriculture impacts TMCF cloud immersion, deforestation should be immediately stopped to prevent any further reduction in the frequency with which cloud immersion occurs. In areas where no assessment has yet been done, modelers should make extra efforts because the rates of lowland deforestation is very high in the tropics and is expected to continue in the near future. Historical simulations such as those of Comarazamy and González (2011) should be done to determine climate impacts. A global effort with focused local action in which researchers, governmental agencies, local authorities, NGOs, private enterprises, and civil society all work together is urgently required (Bruijnzeel et al. 2010). Finally, relevance to the conservation of biodiversity is the most common justification for conducting research not just about TMCFs but also in all biology-related tropical research. A study by Pitman et al. (2007) shows that there is a distinct disconnect between ‘foreign researchers’ and the perceived beneficiaries of the research. Foreign authors tend to focus on basic science that the on-the-ground beneficiary cannot understand; publish their results in very expensive academic journals in which they cite each other but that the beneficiary invariably cannot access; publish in English, which most of the beneficiaries cannot read, and have few explicit conservation recommendations. On the other hand, ‘gray literature,’ written mostly by on-the-ground practitioners of conservation in the tropics is related to management, not digitally available, nonpeer reviewed, and in a non-English language that ‘foreign researchers’ cannot access. For the bottom–up process to really succeed for TMCF conservation “scientists in the tropics, regardless of their nationality” need “to examine and reexamine the practical connections between the work they do and the work done by those who manage tropical lands. For scientists whose funding derives from grant proposals that
invoke the critical environmental situation in the tropics, this is practically an ethical obligation” (Pitman et al. 2007).
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Pielke, R. A., Jr.Jr., T. Wigley, and C. Green, 2008: Dangerous assumptions. Nature, 452, 531–532. Pielke, R. A., and R. L. Wilby, 2012: Regional climate downscaling: what’s the point? EOS, 93 (5), 52–53. Pitman, N. C. A., M. D. C. L. Azaldegui, K. Salas, G. T. Vigo, and D. A. Lutz, 2007: Written accounts of an Amazonian landscape over the last 450 years. Conservation Biology, 21, 253–262, http://dx.doi.org/10.1111/j.1523-1739.2006.00576.x. Pounds, J. A., and Coauthors, 2006: Widespread amphibian extinctions from epidemic disease driven by global warming. Nature, 439, 161–167. Ray, D. K., U. S. Nair, R. O. Lawton, R. M. Welch, and R. A. PielkeSr., 2006: Impact of land use on Costa Rican tropical montane cloud forests: sensitivity of orographic cloud formation to deforestation in the plains. J. Geophys. Res, 111, D02108, http://dx.doi.org/10.1029/2005JD006096. Ray, D. K., R. A. Pielke Sr.Sr., U. S. Nair, and D. Niyogi, 2010: Roles of atmospheric and land surface data in dynamic regional downscaling. J. Geophys. Res., 115, D05102, http://dx.doi.org/10.1029/2009JD012218. Ray, D. K., V. S. Manoharan, and R. M. Welch, 2011: Cloud cover conditions and stability of the Western Ghats montane wet forests. J. Geophys. Res., 116, D12104, http://dx.doi.org/10.1029/2010JD015245. Scholl, M., W. Eugster, and R. Burkard, 2010: Understanding the role of fog in forest hydrology: stable isotopes as tools for determining input and partitioning of cloud water in montane forests. Tropical Montane Cloud Forests. L. A. Bruijnzeel, F. N. Scatena, and L. S. Hamilton, Eds., Cambridge University Press, 228–241.
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Souza, E. P., N. O. Renno, and M. A. F. Silva Dias, 2000: Convective circulations induced by surface heterogeneities. J. Atmos. Sci., 57, 2915–2922. Still, C. J., P. N. Foster, and S. H. Schneider, 1999: Simulating the effects of climate change on tropical montane cloud forests. Nature, 398, 608–610. Urrego, D. H., M. R. Silman, and M. B. Bush, 2005: The last glacial maximum: stability and change in a western Amazonian cloud forest. J. Quat. Sci., 20, 693–701, http://dx.doi.org/10.1002/jqs.976. Van der Molen, M. K., A. J. Dolman, M. J. Waterloo, and L. A. Bruijnzeel, 2006: Climate is affected more by maritime than by continental land use change: a multiple scale analysis. Global Planet. Change, 54, 128–149, http://dx.doi.org/ 10.1016/j.gloplacha.2006.05.005. Van der Molen, M. K., H. F. Vugts, L. A. Bruijnzed, F. N. Scatena, R. A. Pielke Sr., and L. J. M. Kroom, 2010: Meso-scale climate change due to lowland deforestation in the maritime tropics. Tropical Montane Cloud Forests. L. A. Bruijnzeel, F. N. Scatena, and L. S. Hamilton, Eds., Cambridge University Press, 527–537. Veldkamp, E., A. M. Weitz, I. G. Staritsky, and E. J. Huising, 1992: Deforestation trends in Atlantic zone of Costa Rica: a case study. Land Degrad. Rehabil., 3, 71–84. Welch, R. M., S. Asefi, J. Zeng, U. S. Nair, Q. Han, R. O. Lawton, D. K. Ray, and V. S. Manoharan, 2008: Biogeography of tropical montane cloud forests. I. Remote sensing of cloud base heights. J. Appl. Meteorol. Climatol., 47, 960–975.
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DK Ray, Institute on the Environment, University of Minnesota, Saint Paul, MN, USA. Ó 2013 Elsevier Inc. All rights reserved.
5.05.1 5.05.2 5.05.2.1 5.05.2.2 5.05.3 References
Tropical Montane Cloud Forests and Their Relationship to Water Input Stability of Tropical Montane Cloud Forests Impacts of Global Changes Impacts of Regional Changes Bottom–Up Solutions
5.05.1 Tropical Montane Cloud Forests and Their Relationship to Water Input Tropical montane cloud forests (TMCFs) are unique tropical ecosystems that occur as narrow altitudinal bands between 800 and 3500 m on mountains in the continents of Africa, America, and Asia and in equatorial Oceania (Bubb et al. 2004; Kapelle 2004; Bruijnzeel et al. 2011) (Figure 1, also see Figure 1 of Bruijnzeel et al. 2011). The defining characteristic of TMCFs is persistent cloud cover/fog at the vegetation or ground level that ensures that the tree crowns are regularly in contact with cloud water. The cloud water in turn contains drops of an entire spectrum of sizes from suspended droplets to falling rain-sized droplets. Cloud water directly condenses on the vegetation surface in TMFCs as precipitation. This phenomenon is variously referred to in the literature as ‘horizontal precipitation,’ ‘direct interception,’ or ‘cloud water stripping.’ The cloud water that is deposited regularly on the vegetation surface is primarily from orographically lifted air masses that cool to form the clouds (Figure 2(a)), although orographically lifted advection fog, sea fog, steam fog, or radiation fog could also provide a source for horizontal precipitation (Scholl et al. 2010). The singular distinguishing feature of regular immersion in clouds and cloud water input to TMCFs has important implications to vegetation characteristics, vegetation productivity, nutrient uptake, soil and litter composition, and a positive water balance (Table 1). This process is especially important for dependable streamflows in areas downstream that have little dry-season precipitation but where cloud forests are present upstream (Bubb et al. 2004). This unique feature of TMCFs also results in higher species richness and higher biological endemism within the TMCF (Foster 2001; Bruijnzeel et al. 2011). Three types of TMCFs are now recognized: lower montane cloud forest, upper montane cloud forest, and subalpine cloud forest (Bruijnzeel et al. 2011). The transition from lower to upper montane cloud forest coincides with the level where cloud condensation becomes more persistent, whereas that from upper to subalpine TMCF occurs at the altitude where the average maximum temperature falls below 10 C (Bruijnzeel et al. 2011). The current estimates of global spatial extent of TMCFs vary widely from 21 5000 km2 to 2.21 million km2 (Bruijnzeel et al. 2011), partly due to the difficulty of identifying which tropical forests are regularly cloud immersed forests, although remote sensing techniques provide promising new approaches (Nair et al. 2008; Welch et al. 2008; Lawton et al. 2010; Ray et al. 2011). Bubb et al. (2004) estimated that
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2.5% of all tropical forests are cloud forests, with the lowest percentage in the Americas (1.2%) followed by Africa (1.4%) and Asia having the highest percentage (6.6%). Owing to adaptation to regular cloud water interception, TMCF species are vulnerable to droughtlike conditions. Epiphytes, which grow on other plants in the TMCF and derive their moisture from the air, are a defining characteristic of cloud forests, where a quarter of all plants may be composed of various species of epiphytes (Foster 2001). Epiphytes are especially vulnerable to changes in regular cloud water immersion since they can only access intercepted water through organs dedicated to uptake of water deposited on them. Short intense pulses of rain events are thus relatively of little use to epiphytes. However, in many TMCFs, a significant proportion of the water input could be from continuous drizzle that keeps the organs for water uptake, such as roots, wet. Persistent fogginess also has led to specialist anuran and avian species that are adapted to specific altitudes corresponding to cloud frequency of immersion.
5.05.2
Stability of Tropical Montane Cloud Forests
The pressure to feed a growing and wealthier global population and grow biofuel crops has resulted in a net increase of the world’s croplands and pastures by 154 million hectares between 1985 and 2005, which included significant expansions in the tropics and losses in the higher latitudes (Foley et al. 2011). It is estimated that 80% of new tropical croplands are from the conversion of forests (Gibbs et al. 2010). TMCFs are clearly threatened from these direct conversions into agricultural and pastoral lands and other developmental activities such as road constructions, building of dams and reservoirs, and mining activity (Ray et al. 2011). However, even when TMCFs are protected, they are affected indirectly by global, regional, and local processes that change the very critical ‘regular immersion in clouds’ process via changing the upper and lower bounds of clouds, cloud thickness, cloud cover and its frequency, cloud water content, and temperature changes.
5.05.2.1
Impacts of Global Changes
Changes in global scale processes impact TMCFs because the global scale atmospheric circulation of the Hadley cells determines the altitude of the trade wind inversion layer that in turn marks the upper bound of cloud top heights in the tropics.
http://dx.doi.org/10.1016/B978-0-12-384703-4.00519-0
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Figure 1 Global potential (red) and known (green) TMCF distribution. Reproduced from Bubb, P., I. May, L. Miles, and J. Sayer, 2004: Cloud Forest Agenda, UNEP-WCMC, Cambridge, UK. [Available online at http://www.unep-wcmc.org/biodiversity-series-20_105.html].
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Figure 2 Effects of lowland land-use change on TMCF. (a) Current conditions. (b) Lowland deforestation reduces evapotranspiration and pushes cloud base heights upward. (c) Lowland deforestation increases evaporation from wetlands and lowers cloud base heights.
The trade wind inversion layer in turn is due to the descending branch of the Hadley cells and compression and warming of the air (Bruijnzeel et al. 2011). The altitude at which the orographically forced trade winds begin condensing clouds on the other hand determines the cloud base heights and thus the lower bound of the TMCF. The movement of the Intertropical Convergence Zone (ITCZ) also determines the timing and length of the wet season over the TMCF.
Table 1
During the last glacial maximum, the drastically altered global conditions of temperature and precipitation influenced the spatial and altitudinal distribution of TMCFs. Lake pollen records from Andean TMCFs during the ice ages and modeling results show downslope expansion of TMCF species during the conditions of reduced temperature and wetter conditions that existed and consequently lowered cloud bases (Still et al. 1999; Urrego et al. 2005). Lowering of cloud bases allowed the TMCF
Principle hydrometeorological and vegetation characteristics of TMCFs
Climate
Vegetation
Productivity
Water balance
High cloud frequency High relative humidity Low irradiance
High endemism High proportion of epiphytes Stunted trees with small thick and hard leaves
Low photosynthesis rates Low net primary productivity
Additional water input from horizontal precipitation Low transpiration and physical evaporation Streamflow/incident rainfall very high
Adapted from Foster, P., 2001: The potential negative impacts of global climate change on tropical montane cloud forests. Earth-Sci. Rev., 55, 73–106.
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species to colonize the lower elevations. Other studies, however, from Andean TMCFs suggest that during the ice ages aridity increased and fragmented the TMCF (e.g., Mourguiart and Ledru 2003). Clearly, global climate has a profound impact on TMCF spatial and altitudinal distributions. Looking forward, atmospheric carbon dioxide has continued to increase, reaching a globally averaged concentration of 387.2 ppm at the end of 2009 (Friedlingstein et al. 2010). The technological advance required to stabilize greenhouse gas emissions, however, could be unachievable (Pielke et al. 2008), and emissions of carbon dioxide and other greenhouse gases are expected to continue; global climatic changes are occurring via altering the radiative balance of the earth’s ocean–atmosphere system. Future globally averaged warmer conditions at the surface could result from these elevated greenhouse gases, which would increase evaporation from the water bodies that supply moisture leading to more cloudiness. However, at the same time, the condensation level for clouds could increase because of the warmer air thereby reducing cloud cover and the frequency with which cloud immersion occurs at the vegetation level (Still et al. 1999). It is not clear which process would dominate: more humidity and lowered cloud bases or more humidity but higher cloud bases due to warmer air masses at each TMCF site. Nor is there certainty of global climate changes being felt at each TMCF site. However, if the current regime of cloudiness and temperature changes for any reason, it could lead to the shifting of the current species composition, favoring those that are more adapted to the new conditions and extinction of others. Lack of cloudiness, for example, has been implicated in the outbreak of fungal diseases and species extinction of endemic TMCF fauna (e.g., Pounds et al. 2006). Unfortunately, future cloud immersion is nearly impossible to predict at the individual TMCF locations, and studies that predict future conditions can only indirectly estimate the cloud immersion process via changes in other variables such as relative humidity (Still et al. 1999). This is because of multiple reasons: global climate models have coarse resolutions with each climate model grid cell representing areas of more than 1 of latitude– longitude, whereas TMCFs generally occur only as narrow altitudinal belts of less than a degree of latitude–longitude. Moreover, not only do TMCF belts occur as subgrid-scale regions within global climate model grid cells but the global climate models also parameterize subgrid-scale phenomena such as smaller scale clouds that are primarily the ones that bathe TMCF (Lawton et al. 2001). This means that even if global climate models could accurately predict the future climate, future cloud conditions at the vegetation level of TMCFs cannot be directly determined from global climate model simulations. While regional-scale dynamic downscaling from global model forecasts could provide future cloud frequency at the TMCF resolution, it is now quite clear that regional-scale downscaling is unable, at present, to add any value beyond what is already available via historical analyses, recent paleohistory, and worst case sequence of observed weather (Pielke and Wilby 2012). Under such conditions, a bottom–up assessment of historical regional processes that impact TMCF stability is more useful in understanding the indirect hydrometeorological threats to TMCFs. In order to make this assessment, what do past observations and modeling at TMCF sites tell us about their hydrometeorological vulnerability?
5.05.2.2
Impacts of Regional Changes
Modeling and observational studies show that regional landuse change could either raise or lower the altitudes of cloud formation, i.e., cloud base heights, but the magnitude of the impact is highly site specific. Generally, if the air column gets increasingly warmer and drier while traversing the landscape from the moisture sources before being forced to rise orographically, the altitude at which clouds form (cloud base height) also gets raised (Figure 2(b)). Not only the cloud base heights but also the thickness of clouds, cloud water content, and cloud immersion frequency could get reduced, thereby changing the hydrometeorological environment of the TMCF and subsequently its ecology, irreversibly. The amount by which the clouds are impacted differs based on the amount of upwind deforestation, new land use, and sea surface temperature changes. Inland cloud forests like those of southern Mexico and Costa Rica may be profoundly influenced by regional deforestation (Lawton et al. 2001). Multiple numerical modeling simulations from the Monteverde TMCF of Costa Rica (Nair et al. 2003; Ray et al. 2006) show how deforestation reduces cloud cover at the TMCF vegetation level. Originally, tropical moist and wet forests covered most of the Costa Rican lowlands. However, by 1992, deforestation had reduced the forest cover in the lowlands to only 18% of the original forest cover (Veldkamp et al. 1992; Ray et al. 2006). Deforestation converted the forest trees that have a deeper root system into the shallower rooted agriculture (pasture grasses and seasonal agricultural crops have shallower roots, although tree plantation agriculture could have deeper roots). Field observations suggest that trees have access to water stored in deeper soil layers unlike cropland species (Ray et al. 2006). Studies from eastern Amazonia have also shown that up to 75% of all the water extracted during the dry season originates from soil layers below 2 m, which only trees can access (Nepstad et al. 1994). Thus, as the dry season progresses in Costa Rica’s lowlands, evapotranspiration reduces, but because of conversion to cropland it now reduces drastically. This is affecting the cloud cover. Nair et al. (2003) noted that patterns of cloud formation over the forested lowland regions in Costa Rica exhibited linear organization, but over the deforested areas the convection was unorganized. According to them, organized convection over forested lowland areas was indicative of reduced sensible heat fluxes over forests compared to deforested areas. Souza et al. (2000) via measurements of dry-season surface energy fluxes over paired forested–deforested sites in Amazonia showed a pattern similar to that found by Nair et al. (2003) for Costa Rica. The mechanism is explained in the extensively validated modeling study of Ray et al. (2006). The trade winds that blow over the now extensively deforested Atlantic lowlands of Costa Rica during the dry season encounter drier and warmer air due to the reduced evapotranspiration. Via turbulent mixing with this drier and warmer air the trade winds get drier and warmer (Figure 2(b)). It takes the trade winds around 5–10 h to traverse the extensively deforested Atlantic lowland areas of Costa Rica when this mixing process occurs (Lawton et al. 2001). If the lowlands were forested instead, it is very likely that transpiration would be higher because of trees accessing deeper soil moisture and thus the trade winds would have mixed with
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cooler and moister air. This in turn would dry and warm the trade winds comparatively less than what happens currently (Figure 2(b)). When these modified air masses from the lowlands now rise up orographically, the altitude at which water vapor begins to condense into cloud droplets is higher; in other words, the cloud base heights have been raised because of the extensive deforestation of the lowlands. Ray et al. (2006) estimate that this impact is greatest in the afternoon hours and has already reduced the areal coverage of cloud immersion by 5–13% because of the w75-m rise in cloud base heights. They estimate that further lowland deforestation could reduce the spatial extent of cloud immersion by another 15% via another w125-m rise in cloud base heights. There are similar bottom–up evidences from other sites as well. Barradas et al. (2010) show that the widespread conversion of cloud-affected montane forests to agriculture and pastures in central Veracruz, eastern Mexico, is affecting the climate. There is an observable altitudinal shift in dry-season precipitation (notably in February), and overall fog frequency has also reduced. Ray et al. (2011) have also shown that if the lowland areas of the Western Ghats in southwest India are further deforested then the cloud base heights would increase in the dry season. This is similar to the mechanism reported from Costa Rica. However, unlike in Costa Rica where precipitation is also expected to reduce with further deforestation (Ray et al. 2010), over the Western Ghats precipitation could contrarily increase in the dry season, although cloud immersion reduces. On the other hand, coastal forests like those of some Caribbean islands (e.g., the Luquillo forest of Puerto Rico) may have too little upwind lowland to experience any lowland deforestation impacts (Lawton et al. 2001). While this may be true for many other coastal TMCFs, Van der Molen et al. (2006, 2010) have suggested a different mechanism and found decreases in cloud base heights and precipitation with deforestation (Figure 2(c)). On the other hand, using historical model simulations, Comarazamy and González (2011) have implicated climatic change as impacting the cloud base heights and precipitation in Puerto Rico’s TMCFs. While the studies of Van der Molen et al. (2006, 2010) and Comarazamy and González (2011) may disagree on the exact mechanism that lowered cloud base heights and reduced precipitation, they were both also partly conducted because water supply in Puerto Rico decreased in the 1990s and led to water rationing, affecting millions (Van der Molen et al. 2010). According to Van der Molen et al. (2006, 2010), deforestation reduced the sensible heat flux because the pastures of Puerto Rico that replaced the forests were well-watered long grasses. Thus, under clear and calm conditions, when the sea breeze dominates the trade wind flow, reduction in sensible heat flux from deforestation reduced the strength of the sea breeze, resulting in lower cloud base heights, but the reduced uplift also lowered the precipitation. Comarazamy and González (2011), however, found a global warming signal driving the reductions in precipitation in Puerto Rico. According to them, between 1955 and 2000 the lower atmospheric temperature has increased between 1.0 and 1.8 C over the Caribbean region but much higher toward the North Atlantic. This has created a pressure difference between the two regions that has intensified and also changed the flow direction of the trade winds in such a way that the axis of the orographic uplift has
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itself shifted. The new flow direction has reduced orographic uplift over the TMCF sites and reduced precipitation, although higher humidity has increased cloud thickness, thus appearing as lowering of the cloud bases. Clearly, there is a lot of variation in the exact mechanism even at a site, and at different times different mechanisms may dominate, but similar bottom–up investigations are urgently required elsewhere to determine the most dominant process and the conditions under which they would dominate. One of the key determining factors for the cloud base height and its prediction at each TMCF site is measuring the Bowen ratio – the ratio of sensible heat flux to latent heat flux. As evapotranspiration reduces, radiative balance is achieved via increased sensible heat flux, and the Bowen ratio increases. A higher sensible heat flux triggers stronger updrafts and higher cloud base heights. Thus, cloud base heights are lowered with lower Bowen ratios (Foster 2001). Historical global climate signals such as those of Comarazamy and González (2011) provide additional layers of information. For example, under calmer conditions, local processes could dominate, and clearly, the nature of the lowland land use would then impact the TMCF. In other places such as Costa Rica, even when the trade winds dominate, the nature of lowland land use would impact the TMCF, as the trade winds mix for long periods with the air column over the lowlands during which they can be significantly modified prior to orographic uplift. Unfortunately, while it is quite clear that similar bottom–up observations and modeling exercises are required, the continent with the largest proportion of TMCF, i.e., Asia, is the least studied and the Americas with the least proportion of tropical forests as TMCFs are the most studied. In general, at each TMCF site, the hydrometeorological conditions are being simultaneously impacted from global, regional, and local factors, but one or more of these generally dominate. Moreover, the occurrence of montane cloud forests and the thickness of the cloud forest belt itself are governed by the altitude of persistent cloud condensation, which needs to be urgently determined to establish a baseline against which changes could be measured. What is, however, clear is that with increasing distance from the ocean, air tends to be drier and therefore requires lower temperatures and higher elevations to reach its condensation point. Consequently, the associated cloud base and TMCF vegetation will occur at higher elevations farther from the oceans. Likewise, at given moisture content, the condensation point is reached more rapidly by cooler air than by warmer air. Thus, at greater distances from the equator, the average temperature, and therefore the altitude at which condensation and TMCFs occur, will be lower. If land-use change reduces evapotranspiration into the air masses as they travel from the oceans to the mountains then the cloud bases will rise and cloud forests at lower elevations will become unstable and vice versa. If global climate changes the direction of flow, then cloud formation will change and TMCFs will become unstable.
5.05.3
Bottom–Up Solutions
Currently, the most serious challenge being faced by TMCFs is from economic activities that are directly destroying
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TMCFs – construction of roads and other infrastructure, timber and mineral extraction, telecommunication facilities, tourist development (including golf courses), hydropower production, illegal plant collection, excessive and inappropriate tourism, and steady encroachment of agriculture (Kumaran et al. 2010; Meyer 2010; Owiunji and Plumptre 2010; Ray et al. 2011). The loss of cloud forests can even induce fires and destroy other areas of the cloud forest. Other destructive activities include grazing or trampling by feral ungulates and invasion by alien plant species (Meyer 2010). Some areas that lost TMCFs are now being allowed to recover because of changed political, economic, and social drivers (Aide et al. 2010). TMCF recoveries are, however, hampered from competition with invasive grasses and ferns and poor seed dispersal (Aide et al. 2010). One of the most costeffective ways to accelerate the recovery of TMCFs is to promote the establishment of shrubs, which helps to shade out invasive grasses and ferns and create more appropriate conditions for seedling growth. This strategy reduces the competition faced by the original TMCF plant species, but clearly, not all TMCF species will recover successfully. Thus, planting will also be required to recover a species composition similar to the intact forest because most forest species are rarely dispersed far from forest stands (Aide et al. 2010). Where modeling studies have already shown that conversion of lowlands to shallow-rooted agriculture impacts TMCF cloud immersion, deforestation should be immediately stopped to prevent any further reduction in the frequency with which cloud immersion occurs. In areas where no assessment has yet been done, modelers should make extra efforts because the rates of lowland deforestation is very high in the tropics and is expected to continue in the near future. Historical simulations such as those of Comarazamy and González (2011) should be done to determine climate impacts. A global effort with focused local action in which researchers, governmental agencies, local authorities, NGOs, private enterprises, and civil society all work together is urgently required (Bruijnzeel et al. 2010). Finally, relevance to the conservation of biodiversity is the most common justification for conducting research not just about TMCFs but also in all biology-related tropical research. A study by Pitman et al. (2007) shows that there is a distinct disconnect between ‘foreign researchers’ and the perceived beneficiaries of the research. Foreign authors tend to focus on basic science that the on-the-ground beneficiary cannot understand; publish their results in very expensive academic journals in which they cite each other but that the beneficiary invariably cannot access; publish in English, which most of the beneficiaries cannot read, and have few explicit conservation recommendations. On the other hand, ‘gray literature,’ written mostly by on-the-ground practitioners of conservation in the tropics is related to management, not digitally available, nonpeer reviewed, and in a non-English language that ‘foreign researchers’ cannot access. For the bottom–up process to really succeed for TMCF conservation “scientists in the tropics, regardless of their nationality” need “to examine and reexamine the practical connections between the work they do and the work done by those who manage tropical lands. For scientists whose funding derives from grant proposals that
invoke the critical environmental situation in the tropics, this is practically an ethical obligation” (Pitman et al. 2007).
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Souza, E. P., N. O. Renno, and M. A. F. Silva Dias, 2000: Convective circulations induced by surface heterogeneities. J. Atmos. Sci., 57, 2915–2922. Still, C. J., P. N. Foster, and S. H. Schneider, 1999: Simulating the effects of climate change on tropical montane cloud forests. Nature, 398, 608–610. Urrego, D. H., M. R. Silman, and M. B. Bush, 2005: The last glacial maximum: stability and change in a western Amazonian cloud forest. J. Quat. Sci., 20, 693–701, http://dx.doi.org/10.1002/jqs.976. Van der Molen, M. K., A. J. Dolman, M. J. Waterloo, and L. A. Bruijnzeel, 2006: Climate is affected more by maritime than by continental land use change: a multiple scale analysis. Global Planet. Change, 54, 128–149, http://dx.doi.org/ 10.1016/j.gloplacha.2006.05.005. Van der Molen, M. K., H. F. Vugts, L. A. Bruijnzed, F. N. Scatena, R. A. Pielke Sr., and L. J. M. Kroom, 2010: Meso-scale climate change due to lowland deforestation in the maritime tropics. Tropical Montane Cloud Forests. L. A. Bruijnzeel, F. N. Scatena, and L. S. Hamilton, Eds., Cambridge University Press, 527–537. Veldkamp, E., A. M. Weitz, I. G. Staritsky, and E. J. Huising, 1992: Deforestation trends in Atlantic zone of Costa Rica: a case study. Land Degrad. Rehabil., 3, 71–84. Welch, R. M., S. Asefi, J. Zeng, U. S. Nair, Q. Han, R. O. Lawton, D. K. Ray, and V. S. Manoharan, 2008: Biogeography of tropical montane cloud forests. I. Remote sensing of cloud base heights. J. Appl. Meteorol. Climatol., 47, 960–975.