Historically, ecosystem models have treated rainfall as
the primary moisture source driving litter decomposition. In many arid and
semi-arid lands, however, non-rainfall moisture (fog, dew, and water vapor)
plays a more important role in supporting microbial activity and carbon
turnover. To date though, we lack a robust approach for modeling the role of
non-rainfall moisture in litter decomposition. We developed a series of
simple litter decay models with different moisture sensitivity and
temperature sensitivity functions to explicitly represent the role of
non-rainfall moisture in the litter decay process. To evaluate model
performance, we conducted a 30-month litter decomposition study at 6 sites
along a fog and dew gradient in the Namib desert, spanning almost an eightfold
difference in non-rainfall moisture frequency. Litter decay rates in the
field correlated with fog and dew frequencies but not with rainfall. Including
either temperature or non-rainfall moisture sensitivity functions improved
model performance, but the combination of temperature and moisture
sensitivity together provided more realistic estimates of litter
decomposition than relying on either alone. Model performance was similar
regardless of whether we used continuous moisture sensitivity functions
based on relative humidity or a simple binary function based on the presence
of moisture, although a Gaussian temperature sensitivity outperformed a
monotonically increasing
Drylands play an important part in the global carbon cycle, but we still lack a strong understanding of carbon cycling in these systems. Historically, ecosystem models have underestimated dryland litter decomposition rates (Parton et al., 2007; Adair et al., 2008). This is partly because the models are driven by rainfall, assuming little to no decay between precipitation events. While rainfall pulses play a large role in dryland systems (Noy-Meir, 1973; Seely and Louw, 1980), considering rain alone does not fully capture litter decomposition in these systems. This may be partially because much decomposition occurs at and above the soil surface, and aboveground litter decomposition is less sensitive to large rain pulses than belowground decay (Austin, 2011; Jacobson and Jacobson, 1998). Abiotic processes including photodegradation, aeolian erosion, and thermal degradation that drive aboveground litter decomposition can degrade litter regardless of moisture conditions (Austin, 2011) and rain events as little as 1 mm can facilitate microbial activity (Collins et al., 2008). Finally, non-rainfall moisture (NRM: fog, dew, and water vapor) can support substantial biotic decomposition of plant litter, even in the absence of rain (Jacobson et al., 2015; Dirks et al., 2010; Wang et al., 2017; Logan et al., 2021). These findings demonstrate that carbon and nutrient cycling in drylands are not restricted to precipitation pulses and that NRM is a crucial driver of dryland biogeochemical cycles. As our understanding of the importance of NRM in arid and semi-arid ecosystems evolves, we need to update our conceptual and predictive models to incorporate these important drivers of ecosystem processes.
Despite growing recognition of NRM's importance, current litter decay models do not explicitly account for its ability to support decomposition. This is partly because field-based studies of NRM-driven decomposition are scarce and so far, have mostly focused on documenting single cases and understanding mechanisms. Recent studies have shown that the rate of NRM-driven decomposition depends on many factors including the frequency of humid conditions (Evans et al., 2020), the composition of decomposer communities (Logan et al., 2021; Wenndt et al., 2021), and interactions with other processes, such as photodegradation (Wang et al., 2015; Gliksman et al., 2017; Logan et al., 2022). These insights have been very helpful in demonstrating that NRM-driven decomposition occurs and identifying its various mechanisms. However, before we can incorporate NRM into mechanistic Earth system models, we need multiyear studies that quantify the relationship between NRM and mass loss across a range of environmental conditions (Bonan et al., 2013), something that has not been done to date.
One recent attempt to model NRM-driven decomposition has shed light on this challenge. Evans et al. (2020) developed a model that treated decomposition as a pulse process that could be triggered by either rain or NRM when conditions met a given criterion (i.e., when relative humidity was above a given threshold or when dew was present as determined by a leaf wetness sensor). They found that accounting for NRM produced mass loss estimates that were considerably higher than those from a rain-only model and that these new estimates were within the range observed in the field. This approach showed that NRM can improve mass loss estimates, but it included several simplifying assumptions that need to be tested before NRM can be incorporated into models more generally. First, the authors modeled annual mass loss by measuring instantaneous respiration rates and scaling them up to annual time scales. This showed that the NRM-driven biotic activity on the scale of individual events can be used to estimate long-term mass loss rates over several months, albeit with wide error estimates. A better approach would be to validate model predictions by formally integrating rates of mass loss at multiple sites and in multiyear field studies (Bonan et al., 2013). Studies where NRM meteorology and decomposition are both measured and quantifiably linked to one another are currently lacking.
Second, their model treated decomposition as essentially a pulse process
that could be triggered by either rainfall or NRM, but responded similarly
to both (in other words, as long as the threshold condition was met,
decomposition was considered to be “on”). While rainfall and NRM may
induce similar decomposition rates for a similar moisture level, this
approach does not allow the possibility of continuous responses. For
example, CO
Finally, their model did not include temperature dependence, despite decomposition being highly sensitive to temperature in almost all terrestrial systems (Sierra, 2012; Sierra et al., 2015). Relative humidity is closely linked to air temperature, and average temperature during NRM events is often considerably lower than during rain events (Logan et al., 2021). Developing more powerful NRM-driven litter decay models may therefore require incorporating continuous moisture responses and temperature sensitivities to accurately capture decomposition dynamics, although to date these remain untested.
We set out to determine whether incorporating NRM into a simple litter decay
model improved model performance in an NRM-dominated system. We tested
multiple potential relationships between meteorological variables and litter
decay rates in an attempt to parameterize a model of NRM-driven
decomposition. We had two main objectives:
Use a novel dataset to evaluate multiple methods of modeling litter
decomposition as a function of NRM. Determine how important temperature sensitivity is in NRM-driven litter
decomposition models.
Since existing studies examining decomposition under different moisture regimes
are limiting (Jacobson et al., 2015;
Evans et al., 2020), we draw upon literature on soil organic matter
decomposition and rainfall-driven litter decomposition to identify potential
moisture and temperature sensitivity functions (Sierra et
al., 2015). To evaluate models, we conducted a 30-month, multisite litter
decomposition study that spanned an eightfold magnitude of NRM frequency.
By placing litter across this gradient and making continuous meteorological
measurements alongside mass loss, we were able to quantify the relationship
between NRM and litter decay on a multiyear time scale for the first time.
Finally, we used a Bayesian-Monte Carlo approach to parameterize mass loss
models using several temperature and moisture sensitivity functions and used
model selection criteria to identify the best models.
We conducted our study in the central Namib desert in western Namibia. The
Namib desert is a coastal fog desert, with a steep NRM gradient across a
narrow geographic range
(Eckardt et al.,
2013). Rain is scarce in the Namib desert and NRM is expected to be responsible for
the vast majority of litter decomposition (Evans et al.,
2020). We leveraged the FogNet weather array, a network of meteorological
stations throughout the central Namib desert that is part of the Southern
African Science Service Centre for Climate Change and Adaptive Land
Management (SASSCAL;
At six sites, we deployed senesced tillers of
Precolonization is a very important step in standing-litter decomposition
since it can “prime” litter to be more ready to degrade once it reaches
the soil surface; this contributes to changes in litter decay rates over
time. To assess the effects of NRM on litter decomposition throughout the
decay process, we deployed litter at two stages of decay. Categories were
based on previous observations of
To model the effect of NRM on litter decomposition, we began by modeling
decay rates using a simple exponential model of the form:
This approach, whereby we fit a separate effective decay rate for sites with
different climates, is a common approach to describe how litter
decomposition varies under different climatic conditions
(Zhang et al., 2008). However, because it treats mass loss as
solely dependent on the decay rate and time, this approach does not
explicitly include temperature or moisture. To determine how moisture and
temperature influenced litter decay, we incorporated NRM and temperature
dependence by allowing them to modify an intrinsic litter decay
(
Since litter decomposition can occur in response to dew and fog
(Jacobson et al., 2015) or water vapor under
humid conditions even in the absence of liquid water
(Dirks et al., 2010), we tested separate sensitivity
functions based on either relative humidity levels, or based on a
measurement of the presence of liquid water. Sensitivity functions are
presented in Table 1 and shown in Fig. 1. The threshold model is binary,
allowing decomposition to happen at the intrinsic litter decay rate if and
only if relative humidity is above a specified threshold (
Temperature and NRM sensitivity functions included in the
models. Each curve shows one parameter combination chosen by random
sampling using a normal distribution around a specified set of priors as
identified in Table 2 (
To model temperature dependence, we tested two common temperature
sensitivity functions: a
Moisture and temperature sensitivity functions. The first
three moisture functions are based on relative humidity and the fourth is
based on leaf wetness state. Moisture functions are normalized to 1 at
100 % relative humidity and temperature sensitivity functions are
normalized to 1 at
To understand the nature of the different models and compare them across a range of conditions, we performed two model runs. First, we explored a large parameter space to determine how parameters interact with one another across a wide range of hypothetical conditions. This included parameter values outside of realistic ranges (for example, relative humidity thresholds from 5 % to 99 % and an intrinsic litter turnover time from 0.1 to 100 years). This allowed us to see how parameters interacted with each other within the different models and explore general properties of each model. Next, to assess which models performed best under realistic conditions, we constrained the parameter space to more accurately reflect real world parameter values. For this model run, we determined optimal values for each parameter based on laboratory and field incubations and then randomly varied parameter combinations around these values; see next section for details. Parameter definitions as well as constrained values used in the second model run are reported in Table 2. Figure 1 shows the range of temperature and moisture sensitivities we used in the constrained model run.
We used the Akaike information criterion (AIC) to compare the constrained models to one another to determine which was the best fit to the data. AIC is a model selection criterion that rewards goodness of fit based on a log likelihood function while penalizing models with greater parameters to reduce overfitting biases (Aho et al., 2014). We report AIC values for all combinations of models from the constrained parameter run to compare model performance under realistic scenarios.
Parameter definitions and values used to constrain the
second model run to realistic conditions. Values were randomly varied around
means and standard deviations shown, with “
We parameterized the models using a brute force approach where we randomized
parameter inputs to represent conditions seen in the field (Table 2) and
then selected the model-parameter combinations with the lowest AIC scores.
To constrain temperature parameters, we performed a laboratory incubation of
To calculate
The turnover time represents the litter's intrinsic decay rate under ideal
temperature and moisture conditions and is equivalent to the inverse of
Moisture conditions varied substantially among the sites. Duration of
wetness during the study period (as determined by leaf wetness sensors)
ranged from 672 h (3.1 % of total hours) at the driest site (Garnet
Koppie) to 5672 h (25.3 % of total hours) at the wettest site
(Kleinberg). Drier sites tended to be warmer; mean temperature when dry was
2.3
Temperature sensitivity of respiration from
Summary of meteorological conditions at each site during the study showing mean temperature when dry, mean temperature when wet, wet hours during the entire study period (as determined by leaf wetness sensors), the proportion of total time when conditions were wet, accumulated rainfall during the study period, and mean relative humidity throughout the study period. Temperature ranges in parentheses report the middle 95 % of data. Mean temperatures apply to the time period used in this study but should not be used to infer mean annual temperatures for each site since the study lasted 2.5 years and therefore data from January–August are represented more than September–December. Full names for sites are included in Fig. A1.
In general, mass loss was greater at sites with more NRM and lower at sites with less NRM (Figs. 3, A7). There was a significant three-way interaction between litter stage, site, and time (Table A1 in Appendix). Within each site, early-stage and late-stage litter decomposed at comparable rates for the first 18 months, but diverged after that depending on the site (Fig. 3). After 24 months at the 2 driest sites, early-stage litter lost more mass than late-stage litter. At the four wettest sites however, late-stage litter experienced the greater mass loss (Fig. 3).
Mass loss for early-stage (yellow) and late-stage (gray)
tillers at each site (mean
When we used a simple exponential decay model without temperature and
moisture sensitivity (Eq. 1), the effective decay rate at each site was
correlated with NRM duration but not with accumulated rainfall (Fig. 4).
Late-stage litter (i.e., tillers with more well-established fungal
communities) responded more strongly to NRM than did early-stage litter; for
every additional 1000 h of wetness at a site, the effective decay rate
increased by 0.0043 yr
Effective decay rate calculated without explicit
temperature or NRM sensitivity (Eq. 1) relative to NRM frequency and
accumulated rainfall during the study period. Among sites, decay rate
constant was strongly correlated with the proportion of time that a site
experienced NRM conditions (early-stage:
For the three NRM sensitivity functions based on relative humidity, parameter values showed a tradeoff between turnover time and RH thresholds (Fig. 5): parameter combinations with the lowest AIC scores featured either slow turnover times and a low RH threshold (bottom right of plots) or faster turnover times and high RH thresholds (upper middle of plots). When we fit parameters separately for each site instead of globally, AIC values improved, but the actual values of the best parameter combinations did not change (Fig. A4). Similarly, fitting parameters separately to early-stage and late-stage tillers did not produce different optimal parameter values (Fig. A5).
Parameter fits for the first model run showing parameter
combinations across a wide range of hypothetical conditions.
Models that included
Models that included NRM sensitivity had better fits than the simple
litter decay model, but the best models included both NRM and temperature
sensitivity (Fig. 6). While model fit improved (AIC scores were lower)
whenever NRM sensitivity was included, the degree to which NRM sensitivity
improved the model fit depended on the temperature sensitivity function. In
particular, models with Gaussian temperature sensitivity performed better
than those with
Frequency distribution of model performance
(log
Including temperature sensitivity alone (without NRM) did not improve model
fit as well as modeling only NRM sensitivity (without temperature). All of
the NRM-only models (Fig. 6, bottom row) had better fits than temperature-only models (Fig. 6, right column), although each showed a wide
range depending on the specific parameter combinations. In fact, an
unconstrained model with
When we compared one of the best models that included temperature and NRM sensitivity (specifically, a Gaussian temperature function and an exponential moisture function) to a simple decay model that had no temperature or NRM sensitivity but varied effective decay rate among sites (Eq. 1), we found that the temperature and NRM model performed better (Fig. 7). The Gaussian exponential model had lower AIC scores and the slope of the observed vs. predicted values was closer to 1, yielding more realistic mass loss predictions (0.85 for Gaussian exponential model, 0.71 for simple decay model).
Decomposition is a crucial component of Earth system models and NRM is an important moisture source in arid and mesic grasslands worldwide. In a first attempt at modeling NRM-driven decomposition, Evans et al. (2020) compared litter decay rates in a hyperarid and a mesic grassland, showing that decay rates are faster when NRM is more frequent. We build on this work by demonstrating a scalable quantification of the relationship between NRM, temperature, and litter decay rates. Doing so is an important step to improving Earth system models, which must be validated with field measurements made under realistic conditions (Bonan et al., 2013). Using a 30-month, multisite field experiment, we show that explicitly accounting for both temperature and NRM sensitivity improved a litter decay model in an NRM-affected system.
While incorporating either NRM sensitivity or temperature dependence
improved model performance, it was the inclusion of both that led to the
largest improvement. Decomposition temperature sensitivity often depends
on moisture conditions (Petraglia et al., 2019). For example,
in soils, temperature typically increases decay rates when moisture is
abundant, but higher temperatures can dry out soils, slowing decomposition
(Bear et al., 2014). Similarly, in our system, NRM
increases litter moisture content (Jacobson et
al., 2015), but fog and dew only form at cooler temperatures, when
decomposition is slower; once temperatures get high enough (in this case,
above 20
The choice of temperature sensitivity function is often very important in
modeling biological processes and can lead to quite different predictions
(Low-Décarie et al., 2017). We found that model
performance was better using a Gaussian rather than a
By deploying both recently senesced and precolonized litter, we were able to study the effect of NRM on litter decomposition at early and late stages in the decay process. The fact that early-stage litter decomposed faster than late-stage litter at the two driest sites is likely because early in the decay process decomposer communities are small and photodegradation of the cuticle is a more important contributor to mass loss than microbial decomposition (Logan et al., 2022). As a result, decomposition becomes more sensitive to moisture later in the decay process. Once fungal communities were well established (as on later-stage tillers), litter decomposition was more sensitive to moisture availability, which is why late-stage tillers decomposed faster at the wetter sites (Fig. 3). By deploying litter at different stages of decay across a wide moisture gradient, we showed that sensitivity of litter decomposition to NRM appears to increase over time.
Surprisingly, early-stage and late-stage litter had similar relative humidity thresholds for decomposition even though older litter tends to absorb more water during fog and dew events (Logan et al., 2022). In the absence of rain, litter moisture content rarely reaches biologically significant levels until relative humidity reaches at least 70 %–80 % (Dirks et al., 2010; Evans et al., 2020; Tschinkel, 1973), but this depends on several factors including the permeability of the litter to water, the amount of time it spends in humid conditions, and the decomposer community's sensitivity to moisture (Tschinkel, 1973; Logan et al., 2021, 2022). While the simple threshold-based moisture function performed very well in this study, future studies will likely need to parameterize the moisture threshold to fit the dominant litter type in their locales.
Despite converging on the same parameter values, model fits were much better for late-stage litter than for early-stage litter (Fig. A5). This could reflect the fact that the larger fungal communities on late-stage tillers enable them to respond to moisture more strongly than early-stage tillers, which do not yet have a large enough decomposer community to have a strong biological response to NRM. This is consistent with the results from our simple decay model (without explicit temperature and moisture sensitivity), which showed that effective litter decay rates for late-stage tillers were 3.3 times more sensitive to changes in NRM frequency than early-stage tillers (Fig. 4). Since the major differences between the early-stage and late-stage tillers used in this study are their degree of prior fungal colonization and their ability to absorb water, this reinforces the importance of fungal communities as mediators of decomposition response to NRM (Logan et al., 2021) and suggests that plant litter properties related to moisture absorption may influence NRM sensitivity (Logan et al., 2022). Examining whether these properties have the same influence on NRM-driven decay of other plant species may increase the generalizability of the response functions we present here.
Developing models that realistically predict carbon turnover is a multistep process that requires determining a model structure, parameterizing, and accounting for external forcings (Luo et al., 2015). Our goal was to compare several potential structures for modeling NRM-driven litter decomposition, but fully incorporating NRM sensitivity into existing Earth system models will require additional work. This includes identifying the appropriate temporal resolution at which to model NRM events. The time steps used by Earth system models have shortened considerably over the last two decades, to the point where processes that were once represented monthly are now modeled on hourly time scales or less (Sokolov et al., 2018; Bolker et al., 1998; Bonan et al., 2013). We used hourly averages of minute data to describe decomposition rates, but do not yet know what temporal resolution is necessary to fully capture NRM events. Future studies can compare estimates using minute data (that have the benefit of capturing the wetting and drying dynamics of litter at the start and end of NRM events) to daily time scales, that can estimate NRM-driven decomposition from daily mean relative humidity. In the case of longer (daily) time scales, temperature dependence may be best determined using the minimum daily temperature instead of mean temperature, since minimum temperatures are likely to occur at night when NRM is most common. Of course, these methods will require additional testing, but since our models were relatively insensitive to the specific nuances of how NRM was modeled, any of several approaches may be appropriate depending on the structure of the decomposition model in use.
We used relative humidity and leaf wetness sensor data to parameterize our moisture sensitivity functions but other methods of modeling moisture may work as well. Many ecosystem models treat soil water content (which regulates soil organic matter decomposition) as related to the ratio of rain to evapotranspiration (Necpálová et al., 2015). If NRM-driven decomposition can be captured by proxies constructed from evaporation, minimum temperature, and other values already included in carbon submodels, it may be easier to incorporate this novel process into existing modeling approaches. Fortunately, relative humidity is measured at meteorological stations worldwide and extensive data are available. Even in regions with data gaps, methods exist to estimate relative humidity from temperature datasets (Gunawardhana et al., 2017) and these can be incorporated into Earth system models to include NRM sensitivity without the need to collect additional data.
While our study focused exclusively on aboveground litter decay, NRM may have other effects on decomposition later in the decay process as well. NRM-driven decomposition removes carbon from the system before it reaches the soil surface, decreasing inputs to belowground pools. Additionally, NRM may accelerate belowground decomposition rates once litter is incorporated into the soil by promoting the development of larger (and specialized) microbial communities early in the decay process (Logan et al., 2021; Jacobson et al., 2015). Such soil-litter mixing often increases litter decomposition in dryland systems (Barnes et al., 2015, 2012; Hewins et al., 2013). Even more broadly, there are other processes for which models ignore the role of NRM that affect carbon cycling, like stimulating plant growth, and suppressing wildfires (Weathers, 1999; Emery et al., 2018). To improve our understanding of NRM-driven decomposition, studies can test the role of NRM-driven decomposition on both aboveground and belowground litter to identify how NRM affects linkages between these two pools.
NRM's role in litter decay has been observed in a wide range of ecosystems including Mediterranean shrublands (Gliksman et al., 2018; Dirks et al., 2010), salt-marshes (Newell et al., 1985), hyperarid deserts (Logan et al., 2021), and temperate steppes (Wang et al., 2017). One study found that NRM played a substantial role even a mesic prairie with mean annual precipitation of 897 mm (Evans et al., 2020), suggesting that NRM is important even when rainfall is relatively frequent. Our contribution does not therefore demonstrate the importance of NRM to litter decomposition in general, but shows that the frequency of NRM events strongly predicts litter mass loss across a wide range of moisture conditions and that this can be easily modeled using readily available moisture data. Although this study was conducted at the dry end of an aridity gradient, it still represented an eightfold magnitude of NRM frequency, showing that NRM can be easily incorporated into litter decay models. Explicitly incorporating NRM into models in mesic systems, where rainfall plays a greater role, will likely require including both rainfall and NRM sensitivity functions to identify the relative role of each as rainfall increases.
Since our goal was to present a first attempt at incorporating NRM into litter decay models in an NRM-dominated ecosystem, we had to make several simplifications that likely underestimated litter decay rates. First, we only looked at standing dead litter not litter at the soil surface. Standing litter often decomposes faster than litter lying at the soil surface (Liu et al., 2015; Gliksman et al., 2018) and represents an important and, until recently, overlooked source of carbon turnover in drylands (Wang et al., 2017). While we did not look at litter at the soil surface, surface litter absorbs atmospheric moisture (Tschinkel, 1973) and may respond similarly to NRM, although to date no models we know of have looked at this across a range of NRM conditions, suggesting important avenues for future work.
Secondly, we focused only on coarse tillers not leafy material. In laboratory
incubations, Jacobson et al. (2015) found
that at high humidity the water content of
Finally, we focused only on the meteorological drivers of litter decomposition, although others factors play important roles as well. Photodegradation (Austin and Vivanco, 2006; King et al., 2012), macrodetritivore activity (Louw and Seely, 1982), and soil-litter mixing (Hewins et al., 2013; Lee et al., 2014) are all important drivers of litter decomposition in drylands. Since our goal was to quantify the relationship between NRM and litter turnover, we focused solely on NRM, but future studies can build on this work by combining our approach with other existing models. For instance, photodegradation can interact with NRM to accelerate carbon turnover, especially of standing litter (Wang et al., 2017; Logan et al., 2022), and accounting for photodegradation improves litter decay models (Chen et al., 2016; Adair et al., 2017). Combining these other mechanisms with the relative humidity-based litter decay model we present here may reveal additional interactions that can be validated by field studies. The fact that we were able to describe a large degree of litter decomposition by using a simple relative humidity-based and temperature-based model, however, demonstrates that NRM plays an important role in the litter decay process across a wide range of environmental conditions.
We show that the frequency of NRM is a major predictor of litter decomposition, and for the first time used data from a multisite field study to develop temperature and NRM sensitivity functions for a litter decay model. Temperature and moisture regimes are changing as a result of anthropogenic climate change (Byrne and O'Gorman, 2016) and our ability to predict how ecosystems respond depends, in part, on how well we can link biogeochemical cycles to their environmental drivers. NRM and rainfall are often controlled by different climatic drivers and may therefore respond differently under future climate change (Haensler et al., 2011; Dai, 2013; Forthun et al., 2006). By modeling the contribution of NRM to decomposition, in addition to that of rainfall, we can better predict how drylands will respond to changing moisture regimes, increasing our ability to manage these globally important systems.
Location of the six FogNet sites used in this study. All samples were collected from dunes of the Namib Sand Sea at Gobabeb. Background image © Google Earth.
Frequency distributions of temperature when wet (turquoise) and dry (red) at the six sites during the study.
Parameter fits for the humidity-based moisture models
using
Photos of litter racks from each site (from driest on the left to wettest on the right) after 18 months in the field. The dark color on the racks from the wetter sites is from dark-pigmented fungal growth on both the tillers and the wooden frames after exposure to frequent NRM events.
ANOVA table of general mass loss model showing three-way interaction between time, site, and litter stage.
Data and code used in this paper are available as an R Markdown file at
JRL, SEE, PJJ, and KMJ conceived the study. JRL designed the study and conducted fieldwork. JRL and KTB developed the model code and performed the simulations. PJJ and KMJ performed the laboratory incubations. RV collected and processed meteorological data. JRL conducted data analysis and statistics and prepared the manuscript with contributions from all co-authors.
The contact author has declared that none of the authors has any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We would like to thank the staff at the Gobabeb Namib Research Institute, the Namibian Ministry of Environment and Tourism, the National Botanical Research Institute, and the National Commission on Research, Science and Technology (permit number RPIV000102017) for their permission and support for this study. Special thanks to Martin Handjaba at Gobabeb for fieldwork in support of this project and to Lukas Mendel for assistance with laboratory incubations. The Southern African Science Service Centre for Climate Change and Adaptive Land Management provided the initial funding to establish the FogNet weather network and it is currently maintained by funding from the University of Basel. This work was also supported in part through computational resources and services provided by the Institute for Cyber-Enabled Research at Michigan State University. Thank you to the participants at the 2014 FogLife Colloquium, in particular Mary Seely and Theo Wassenaar, for their contributions in helping generate ideas for this project from the start. Finally, thank you to the Evans Lab, the KBS Writing Group and two reviewers for their feedback on earlier versions of this manuscript. This paper is KBS contribution number 2303.
Funding was provided by the U.S. National Science Foundation’s Graduate Research Fellowship Program, the W. K. Kellogg Biological Station at Michigan State University, Grinnell College, and the taxpayers of the United States and Michigan.
This paper was edited by Akihiko Ito and reviewed by two anonymous referees.