Drivers of lichen species richness at multiple spatial scales in temperate forests

Only few studies analysing lichen diversity have simultaneously considered interactions among drivers that operate at different spatial and temporal scales. Aims: The aims of this study were to evaluate the relative importance of host tree, and local, landscape and historical factors in explaining lichen diversity in managed temperate forests, and to test the potential interactions among factors acting at different spatial scales. Methods: Thirty-five stands were selected in the Őrség region, western Hungary. Linear models and multi-model inference within an information-theory framework were used to evaluate the role of different variables on lichen species richness. Results: Drivers at multiple spatial scales contributed to shaping lichen species richness both at the tree and plot levels. Tree level species richness was related to both tree and plot level factors. With increasing relative diffuse light lichen species richness increased; this effect was stronger on higher than on lower part of the trunks. At the plot-scale, species richness was affected by local drivers. Landscape and historical factors had no or only marginal effect. Conclusions: Lichen conservation in temperate managed forests could be improved if the complex interactions among host tree quality and availability, micro-climatic conditions, and management were taken into consideration

Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four Dimensional Variational data assimilation

Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, prior or “background” errors (in the initial state and parameter values) and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a Four-Dimensional Variational data assimilation (4D-Var) scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation errors. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we assimilate a single year of NEE observations and then run a forecast for the next 14 years. We compare the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We find that using the new correlated matrices reduces the root mean square error in the 14 year forecast of daily NEE by 44% decreasing from 4.22 gCm−2 day−1 to 2.38 gCm−2 day−

Data from: Estimation of woody and herbaceous leaf area index in Sub-Saharan Africa using MODIS data

Savannas are widespread global biomes covering ~20% of terrestrial ecosystems on all continents except Antarctica. These ecosystems play a critical role in regulating terrestrial carbon cycle, ecosystem productivity, and the hydrological cycle and contribute to human livelihoods and biodiversity conservation. Despite the importance of savannas in ecosystem processes and human well-being, the presence of mixed woody and herbaceous components at scales much fin-er than most medium and coarse resolution satellite imagery poses significant challenges to their effective representation in remote sensing and modeling of vegetation dynamics. Although pre-vious studies have attempted to separate woody and herbaceous components, the focus on greenness indices and fractional cover provides little insight into spatio-temporal variability in woody and herbaceous vegetation structure, in particular, leaf area index (LAI). This paper pre-sents a method to partition 1km spatial resolution Moderate Resolution Imaging Spectroradiome-ter (MODIS) aggregate green leaf area index (LAIA) from 2003-2015, into separate woody (LAIW) and herbaceous (LAIH) constituents in both drought seasonal savannas and moist tropical forests of Sub-Saharan Africa (SSA). In our analysis, we use an allometric relationship describing the variation in peak within-canopy woody LAI of dominant tree species (LAIWpinc) across gradi-ents in mean annual precipitation (MAP), coupled with independent estimates of woody canopy cover (τw), to constrain seasonally changing LAIW. We present the LAI partitioning approach and highlight the broad spatial and temporal patterns of woody and herbaceous LAI across SSA. The long-term average 8-day phenologies of woody and herbaceous LAI (averaged across 2003-2015) are available for evaluation, research and application purposes