Jean Liénard

3D Tree Dimensionality Assessment Using Photogrammetry and Small Unmanned Aerial Vehicles

Detailed, precise, three-dimensional (3D) representations of individual trees are a prerequisite for an accurate assessment of tree competition, growth, and morphological plasticity. Until recently, our ability to measure the dimensionality, spatial arrangement, shape of trees, and shape of tree components with precision has been constrained by technological and logistical limitations and cost. Traditional methods of forest biometrics provide only partial measurements and are labor intensive. Active remote technologies such as LiDAR operated from airborne platforms provide only partial crown reconstructions. The use of terrestrial LiDAR is laborious, has portability limitations and high cost. In this work we capitalized on recent improvements in the capabilities and availability of small unmanned aerial vehicles (UAVs), light and inexpensive cameras, and developed an affordable method for obtaining precise and comprehensive 3D models of trees and small groups of trees. The method employs slow-moving UAVs that acquire images along predefined trajectories near and around targeted trees, and computer vision-based approaches that process the images to obtain detailed tree reconstructions. After we confirmed the potential of the methodology via simulation we evaluated several UAV platforms, strategies for image acquisition, and image processing algorithms. We present an original, step-by-step workflow which utilizes open source programs and original software. We anticipate that future development and applications of our method will improve our understanding of forest self-organization emerging from the competition among trees, and will lead to a refined generation of individual-tree-based forest models.

An Appraisal of the Classic Forest Succession Paradigm with the Shade Tolerance Index

In this paper we revisit the classic theory of forest succession that relates shade tolerance and species replacement and assess its validity to understand patch-mosaic patterns of forested ecosystems of the USA. We introduce a macroscopic parameter called the “shade tolerance index” and compare it to the classic continuum index in southern Wisconsin forests. We exemplify shade tolerance driven succession in White Pine-Eastern Hemlock forests using computer simulations and analyzing approximated chronosequence data from the USDA FIA forest inventory. We describe this parameter across the last 50 years in the ecoregions of mainland USA, and demonstrate that it does not correlate with the usual macroscopic characteristics of stand age, biomass, basal area, and biodiversity measures. We characterize the dynamics of shade tolerance index using transition matrices and delimit geographical areas based on the relevance of shade tolerance to explain forest succession. We conclude that shade tolerance driven succession is linked to climatic variables and can be considered as a primary driving factor of forest dynamics mostly in central-north and northeastern areas in the USA. Overall, the shade tolerance index constitutes a new quantitative approach that can be used to understand and predict succession of forested ecosystems and biogeographic patterns.

The Essential Complexity of Auditory Receptive Fields.

Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.

An individual-based forest model links canopy dynamics and shade tolerances along a soil moisture gradient

Understanding how forested ecosystems respond to climatic changes is a challenging problem as forest self-organization occurs simultaneously across multiple scales. Here, we explore the hypothesis that soil water availability shapes above-ground competition and gap dynamics, and ultimately alters the dominance of shade tolerant and intolerant species along the moisture gradient. We adapt a spatially explicit individual-based model with simultaneous crown and root competitions. Simulations show that the transition from xeric to mesic soils is accompanied by an increase in shade-tolerant species similar to the patterns documented in the North American forests. This transition is accompanied by a change from water to sunlight competitions, and happens at three successive stages: (i) mostly water-limited parkland, (ii) simultaneously water- and sunlight-limited closed canopy forests featuring a very sparse understory, and (iii) mostly sunlight-limited forests with a populated understory. This pattern is caused by contrasting successional dynamics that favour either shade-tolerant or shade-intolerant species, depending on soil moisture and understory density. This work demonstrates that forest patterns along environmental gradients can emerge from spatial competition without physiological trade-offs between shade and growth tolerance. Mechanistic understanding of population processes involved in the forest–parkland–desert transition will improve our ability to explain species distributions and predict forest responses to climatic changes.

Data-intensive modeling of forest dynamics

Forest dynamics are highly dimensional phenomena that are not fully understood theoretically. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a data-intensive methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

US forest response to projected climate‐related stress: a tolerance perspective

Although it is widely recognized that climate change will require a major spatial reorganization of forests, our ability to predict exactly how and where forest characteristics and distributions will change has been rather limited. Current efforts to predict future distribution of forested ecosystems as a function of climate include species distribution models (for fine-scale predictions) and potential vegetation climate envelope models (for coarse-grained, large-scale predictions). Here, we develop and apply an intermediate approach wherein we use stand-level tolerances of environmental stressors to understand forest distributions and vulnerabilities to anticipated climate change. In contrast to other existing models, this approach can be applied at a continental scale while maintaining a direct link to ecologically relevant, climate-related stressors. We first demonstrate that shade, drought, and waterlogging tolerances of forest stands are strongly correlated with climate and edaphic conditions in the conterminous United States. This discovery allows the development of a tolerance distribution model (TDM), a novel quantitative tool to assess landscape level impacts of climate change. We then focus on evaluating the implications of the drought TDM. Using an ensemble of 17 climate change models to drive this TDM, we estimate that 18% of US ecosystems are vulnerable to drought-related stress over the coming century. Vulnerable areas include mostly the Midwest United States and Northeast United States, as well as high-elevation areas of the Rocky Mountains. We also infer stress incurred by shifting climate should create an opening for the establishment of forest types not currently seen in the conterminous United States.

Data-intensive multidimensional modeling of forest dynamics

Forest dynamics are highly dimensional phenomena that are not fully understood theoretically. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a data-intensive methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management.

Modelling of hardwood forest in Quebec under dynamic disturbance regimes: a time‐inhomogeneous Markov chain approach

Summary We investigate the consequences of global warming scenarios in Quebec forests using an inhomogeneous Markov chain model. This allows us to unify predictions from climate change models and mechanistic models of forest disturbance and growth and allows predicting the potential impacts of climate change on Quebec forests. The model predicts changes in fire rate in Quebec hardwood forests as well as possible growth enhancements due to increasing CO2 and temperature. Our original method consists of three steps. (i) We estimate biomass transition matrices from forest inventories using the Bayesian method. (ii) We incorporate dynamic disturbance and forest growth scenarios, and (iii) we simulate transient dynamics and stationary states. This modelling approach allows for sensitivity analysis and quantitative assessment effects of variability of climate change scenarios. We have considered published climate change scenarios for Quebec and conducted simulations for the most extreme predictions (the smallest and largest predicted changes). None of the considered scenarios is able to counterbalance the currently observed trend of increasing biomass in the next 40 years. By the beginning of 2090, the extreme scenarios diverge within about ±5% mean biomass. Synthesis. In this work, we have developed an original modelling approach incorporating time- inhomogeneous effects within the Markov chain framework. We applied this approach to examine effects of climate change in Quebecs forests. The results demonstrate that the current trend of increase in forest biomass is robust with respect to a broad range of climate change scenarios. This study was not possible with previously employed homogeneous Markov chain models. The model can also be extended to include different harvesting methods and land-use practices, enabling better long-term management of Quebecs forest.

Correction to: ‘Intercomparison of photogrammetry software for three-dimensional vegetation modelling’

[This corrects the article DOI: 10.1098/rsos.172192.].

Invariance to frequency and time dilation along the ascending ferret auditory system

The sense of hearing requires a balance between competing processes of perceiving and ignoring. Behavioral meaning depends on the combined values of some sound features but remains invariant to others. The invariance of perception to physical transformations of sound can be attributed in some cases to local, hard-wired circuits in peripheral brain areas. However, at a higher level this process is dynamic and continuously adapting to new contexts throughout life. Thus the rules defining invariant features can change. In this project, we test the idea that high-level, coherent auditory processing is achieved through hierarchical bottom-up combinations of neural elements that are only locally invariant. The main questions we address in the context of an auditory system are: 1. What kinds of changes in sound do not affect initial stages of auditory processing? 2. How does the brain manipulate these small effects to achieve a coherent percept of sounds? Local probabilistic invariances, defined by the distribution of transformations that can be applied to a sensory stimulus without affecting the corresponding neural response [1,2], are largely unstudied in auditory cortex. We assess these invariances at two stages of the auditory hierarchy using single neuron recordings from the primary auditory cortex (A1) and the secondary auditory cortex (PEG) of awake, passively listening ferrets [3,4]. Our results show that stimulus invariance to frequency and time dilations are present at every tested stage and increase along the hierarchical auditory processing. At least in the early stages, parametric models having invariance properties by design are well-suited to describing biological functions. We were further able to characterize meaningful relationships among receptive field shapes. Preliminary observations indicate that joint time/frequency receptive fields are oriented toward central frequencies; receptive field widths are proportional to the best frequency; and late-onset neurons are also exhibiting the most sustained activity. Figure 1 95% confidence interval in frequency for the highest responses in A1 (A) and PEG (B) as tested with narrowband noise stimuli. The higher values found in PEG compared to A1 demonstrate higher invariance to frequency shifts in PEG than in A1for both stimuli. ...