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Transhumants move their herds based on strategies simultaneously considering several environmental and socio-economic factors. There is no agreement on the influence of each factor in these strategies. In addition, there is a discussion about the social aspect of transhumance and how to manage pastoral space. In this context, agent-based modeling can analyze herd movements according to the strategy based on factors favored by the transhumant. This article presents a reductionist agent-based model that simulates herd movements based on a single factor. Model simulations based on algorithms to formalize the behavioral dynamics of transhumants through their strategies. The model results establish that vegetation, water outlets and the socio-economic network of transhumants have a significant temporal impact on transhumance. Water outlets and the socio-economic network have a significant spatial impact. The significant impact of the socio-economic factor demonstrates the social dimension of Sahelian transhumance. Veterinarians and markets have an insignificant spatio-temporal impact. To manage pastoral space, water outlets should be at least from each other. The construction of veterinary centers, markets and the securitization of transhumance should be carried out close to villages and rangelands.

The core principle shared by most theories and models of succession is that, following a major disturbance, plant–environment feedback dynamics drive a directional change in the plant community. The most commonly studied feedback loops are those in which the regrowth of the plant community causes changes to the abiotic (e.g. soil nutrients) or biotic (e.g. dispersers) environment, which differentially affect species availability or performance. This, in turn, leads to shifts in the species composition of the plant community. However, there are many other PE feedback loops that potentially drive succession, each of which can be considered a model of succession. While plant–environment feedback loops in principle generate predictable successional trajectories, succession is generally observed to be highly variable. Factors contributing to this variability are the stochastic processes involved in feedback dynamics, such as individual mortality and seed dispersal, and extrinsic causes of succession, which are not affected by changes in the plant community but do affect species performance or availability. Both can lead to variation in the identity of dominant species within communities. This, in turn, leads to further contingencies if these species differ in their effect on their environment (priority effects). Predictability and variability are thus intrinsically linked features of ecological succession. We present a new conceptual framework of ecological succession that integrates the propositions discussed above. This framework defines seven general causes: landscape context, disturbance and land-use, biotic factors, abiotic factors, species availability, species performance, and the plant community. When involved in a feedback loop, these general causes drive succession and when not, they are extrinsic causes that create variability in successional trajectories and dynamics. The proposed framework provides a guide for linking these general causes into causal pathway

The emergence of alternative stable states in forest systems has significant implications for the functioning and structure of the terrestrial biosphere, yet empirical evidence remains scarce. Here, we combine global forest biodiversity observations and simulations to test for alternative stable states in the presence of evergreen and deciduous forest types. We reveal a bimodal distribution of forest leaf types across temperate regions of the Northern Hemisphere that cannot be explained by the environment alone, suggesting signatures of alternative forest states. Moreover, we empirically demonstrate the existence of positive feedbacks in tree growth, recruitment and mortality, with trees having 4–43% higher growth rates, 14–17% higher survival rates and 4–7 times higher recruitment rates when they are surrounded by trees of their own leaf type. Simulations show that the observed positive feedbacks are necessary and sufficient to generate alternative forest states, which also lead to dependency on history (hysteresis) during ecosystem transition from evergreen to deciduous forests and vice versa. We identify hotspots of bistable forest types in evergreen-deciduous ecotones, which are likely driven by soil-related positive feedbacks. These findings are integral to predicting the distribution of forest biomes, and aid to our understanding of biodiversity, carbon turnover, and terrestrial climate feedbacks.

The role of intraspecific variability (IV) in shaping community dynamics and species coexistence has been intensively discussed over the past decade and modelling studies have played an important role in that respect. However, these studies often implicitly assume that IV can be represented by independent random draws around species-specific mean parameters. This major assumption has largely remained undiscussed, although a great part of observed IV is structured in space or time, in particular when environmental dimensions that influence individual performance are imperfectly characterised or unobserved in the field. To test the impact of this strong assumption on the outcome of community dynamics models, we designed a simulation experiment where we varied the level of knowledge of the environment in virtual communities, resulting in different relative importance of explained vs unexplained spatial individual variation in performance. We used a community dynamics simulator to generate communities where the unexplained individual variation is, or is not, added as an unstructured random noise. Communities simulated with unstructured IV never reached the community diversity and composition of those where all the variation was explained and structured (perfect knowledge model). This highlights that incorporating unstructured IV (i.e. a random noise) to account for unexplained (but structured) variation can lead to incorrect simulations of community dynamics. In addition, the effects of unstructured IV on community diversity and composition depended on the relative importance of structured vs unstructured IV, i.e. on the level of knowledge of the environment, which may partly explain the contrasting results of previous studies on the effect of IV on species coexistence. In particular, the effect of unstructured IV on community diversity was positive when the proportion of structured IV vs unstructured IV in the model was low, but negative when this proportion was high.

Multivariate count distributions are crucial for the inference of ecological processes underpinning biodiversity. In particular, neutral theory provides useful null distributions allowing the evaluation of adaptation or natural selection. In this paper, we build a broader family of multivariate distributions: the Polya-splitting distributions. We show that they emerge naturally as stationary distributions of a multivariate birth–death process. This family of distributions is a consistent extension of non-zero sum neutral models based on a master equation approach. It allows considering both total abundance of the community and relative abundances of species. We emphasize that this family is large enough to encompass various dependence structures among species. We also introduce the strong closure under addition property that can be useful to generate nested multi-level dependence structures. Although all Pólya splitting distributions do not share this property, we provide numerous example verifying it. They include the previously known example with independent species, and also new ones with alternative dependence structures. Overall, we advocate that Polya-splitting distribution should become a part of the classic toolbox for the analysis of multivariate count data in ecology, providing alternative approaches to joint species distribution framework. Comparatively, our approach allows to model dependencies between species at the observation level, while the classical JSDM's model dependencies at the latent process strata.

Ecology is a science of scale, which guides our description of both ecological processes and patterns, but we lack a systematic understanding of how process scale and pattern scale are connected. Recent calls for synthesis between population ecology, community ecology, and ecosystem ecology motivate the integration of phenomena at multiple organizational levels. Furthermore, many studies leave out the scaling of a critical process: species interactions, which may be non-local through movement or foraging and must be distinguished from dispersal scales. Here, we use simulations to explore the consequences of three different process scales (species interactions, dispersal, and the environment) on emergent patterns of biodiversity, ecosystem functioning, and their relationship, in a spatially-explicit landscape and stable equilibrium setting. A major result of our study is that the spatial scales of dispersal and species interactions have opposite effects: a larger dispersal scale homogenizes spatial biomass patterns, while a larger interaction scale amplifies their heterogeneity. Interestingly, the specific scale at which dispersal and interaction scales begin to influence landscape patterns depends on the scale of environmental heterogeneity – in other words, the scale of one process allows important scales to emerge in other processes. This interplay between process scales, i.e. a situation where no single process dominates, can only occur when the environment is heterogeneous and the scale of dispersal small. Finally, contrary to our expectations, we observe that the spatial scale of ecological processes is more clearly reflected in landscape patterns (i.e. distribution of local outcomes) than in global patterns such as species–area relationships (SARs) or large-scale biodiversity–functioning relationships. Overall we conclude that long-range interactions often act differently and even in opposite ways to dispersal, and that the landscape patterns that emerge from

Species distribution models (SDM) have become one of the most popular predictive tools in ecology. With the advent of new computation and remote sensing technology, high-resolution environmental data sets are becoming more and more common predictors in these modelling efforts. Understanding how scaling affects their outputs is therefore fundamental to understand their applicability. Here, we develop a theoretical basis to understand the consequences of aggregating occurrence and environmental data at different resolutions. We provide a theoretical framework, along with numerical simulations and a real-world case study, to show how these scaling rules influence predictive outputs. We show that the properties of the environment–occurrence relationships change when the data are aggregated: the mean probability of occurrence and species prevalence increases, the optimal environmental values shift and classification rates increase at coarser resolutions up to a certain level. Furthermore, and contrary to the widespread expectation that high-resolution data would produce better predictions, we show here that model performance may increase using coarser resolution data sets rather than the inverse. Finally, we also show that model performance depends not only on the environment–occurrence relationship but also on the interaction between this and the geography and distribution of the available environment. This theoretical framework helps understanding previously incoherent results regarding SDM upscaling and model performance, and illustrates how theoretical and empirical results can provide important feedbacks to advance in understanding scaling issues in macroecology. The interaction between the shape of the environment–occurrence relationship and the rates of change of the environment is fundamental to understand the effects of upscaling in model performance, and may explain why some models are more difficult to transfer to different regions. Most importantly, we argue

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5,6,7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

Intraspecific variability (IV) has been proposed to explain species coexistence in diverse communities. Assuming, sometimes implicitly, that conspecific individuals can perform differently in the same environment and that IV increases niche overlap, previous studies have found contrasting results regarding the effect of IV on species coexistence. We aim at showing that the large IV observed in data does not mean that conspecific individuals are necessarily different in their response to the environment and that the role of high-dimensional environmental variation in determining IV has largely remained unexplored in forest plant communities. We first used a simulation experiment where an individual attribute is derived from a high-dimensional model, representing “perfect knowledge” of individual response to the environment, to illustrate how large observed IV can result from “imperfect knowledge” of the environment. Second, using growth data from clonal Eucalyptus plantations in Brazil, we estimated a major contribution of the environment in determining individual growth. Third, using tree growth data from long-term tropical forest inventories in French Guiana, Panama and India, we showed that tree growth in tropical forests is structured spatially and that despite a large observed IV at the population level, conspecific individuals perform more similarly locally than compared with heterospecific individuals. As the number of environmental dimensions that are well quantified at fine scale is generally lower than the actual number of dimensions influencing individual attributes, a great part of observed IV might be represented as random variation across individuals when in fact it is environmentally driven. This mis-representation has important consequences for inference about community dynamics. We emphasize that observed IV does not necessarily impact species coexistence per se but can reveal species response to high-dimensional environment, which is consistent with

Many systems in life sciences have been modeled by reaction–diffusion equations. However, under some circumstances, these biological systems may experience instantaneous and periodic perturbations (e.g. harvest, birth, release, fire events, etc) such that an appropriate formalism like impulsive reaction–diffusion equations is necessary to analyze them. While several works tackled the issue of traveling waves for monotone reaction–diffusion equations and the computation of spreading speeds, very little has been done in the case of monotone impulsive reaction–diffusion equations. Based on vector-valued recursion equations theory, we aim to present in this paper results that address two main issues of monotone impulsive reaction–diffusion equations. Our first result deals with the existence of traveling waves for monotone systems of impulsive reaction–diffusion equations. Our second result tackles the computation of spreading speeds for monotone systems of impulsive reaction–diffusion equations. We apply our methodology to a planar system of impulsive reaction–diffusion equations that models tree–grass interactions in fire-prone savannas. Numerical simulations, including numerical approximations of spreading speeds, are finally provided in order to illustrate our theoretical results and support the discussion.

Background: Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. Results: The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25–0.88 for plant height, and − 0.29–0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all a

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the f

Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.

Plant phenotyping platforms generate large amounts of high dimensional data at different scales of plant organization. The possibility to use this information as inputs of models is an opportunity to develop models that integrate new processes and genetic inputs. We assessed to what extent the phenomics and modelling communities can address the issues of interoperability and data exchange, using a science mapping approach (i.e. visualization and analysis of a broad range of scientific and technological activities as a whole). In this paper, we (i) evaluate connections, (ii) identify compatible and connectable research topics, and (iii) propose strategies to facilitate connection across communities. We applied a science mapping approach based on reference and term analyses to a set of 4332 scientific papers published by the plant phenomics and modelling communities from 1980 to 2019, retrieved using the Elsevier's Scopus database and the quantitative-plant.org website. The number of papers on phenotyping and modelling dramatically increased during the past decade, boosted by progress in phenotyping technologies and by key developments at hard- and software levels. The science mapping approach indicated a large diversity of research topics studied in each community. Despite compatibilities of research topics, the level of connection between the phenomics and modelling communities was low. Although phenomics and modelling crucially need to exchange data, the two communities appeared to be weakly connected. We encourage these communities to work on ontologies, harmonized formats, translators and connectors to facilitate transparent data exchange.

Ecological phenomena operate at different spatial scales and are not uniform across landscapes or through time. One ecological theory that attempts to account for scaling and spatiotemporal variances is hierarchical patch dynamics. It introduces a hierarchical patch network with smaller spatiotemporal scales being nested within larger scales. However, few studies have modeled its presence within animal population dynamics. Locusts are an excellent model for investigating the spatiotemporal hierarchy of animal population dynamics, due to their high migratory capacity, large geographic ranges that extend across widely differing environments, and available long-term data on distributions. Here, we investigated the influence of preceding vegetation growth on desert locust Schistocerca gregaria and Australian plague locust Chortoicetes terminifera outbreaks on three spatial levels (species range > geographic region > land unit) and between seasons. Both species are dryland herbivores with population dynamics linked to habitat productivity pulses after rain. We used NDVI data (MODIS imagery) as a measure of vegetation growth in hierarchical generalized additive models at different scales. Locust outbreaks were either preceded by vegetation growth between 78 and 32 days (Australian plague locusts) or 32 and 20 days before (desert locust) the observation. Although prior vegetation growth characterized outbreaks of both species, the temporal pattern of NDVI differed between spatiotemporal levels. All model selection criteria selected for a similar spatial hierarchy for both species: geographic region > land unit which supports the hierarchical patch dynamics paradigm. Further, it illuminates important timing differences between geographic regions and land units for preceding vegetation growth and locust outbreaks which can help locust managers identify when and where outbreaks occur. By acknowledging the spatiotemporal patterning of locust abundance, we account for heterogen

Tree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated data sets computed from a set of more than 360 000 random TOY parameter values and random climate values. These data sets were used for training and for validation. For this purpose, we chose VoxNet, a convolutional neural network designed to classify 3D objects represented as a voxelized scene. We recommend further improvements for VoxNet inputs, outputs and training. We were able to teach the network to predict the value of environment data well (mean error < 2 %), and to predict the value of TOY parameters for plants under water stress conditions (mean error < 5 % for all parameters), and for any environmental growing conditions (mean error < 20 %).

Purpose. Current field emission modelling and toxicity characterisation of pesticides suffer from several shortcomings like mismatches between LCI databases and LCIA methods, missing characterisation factors, missing environmental compartments, and environmental impact pathways. The OLCA-Pest project was implemented to address these aspects and to operationalise the assessment of pesticides in LCA. Based on this effort, we propose an approach to integrate pesticide emissions into LCI databases. Methods. The PestLCI Consensus Model has been developed in order to estimate emission fractions to different environmental compartments. The initial distribution fractions should be linked to the compartments air, agricultural soil, natural soil, and freshwater. Emissions to off-field surfaces are hereby distributed between agricultural soil, natural soil, and freshwater by using surface cover data. Deposition on the crop surface should be recorded in an emission compartment crop with 13 sub-compartments for crop archetypes for both food and non-food uses. Default emission fractions are provided to calculate the emission fractions for different pesticide application scenarios. Results and discussion. A sensitivity analysis shows the effects of the application technique, drift reduction, crop and development stage, field width, and buffer zone on the initial distribution fractions of field-applied pesticides. Recommendations are given for the implementation of a set of default initial distribution fractions into LCI databases, for the organisation of metadata, and for the modelling of pesticide residues in food along the supply chain (processing, storage). Priorities for further research are: improving the modelling of pesticide secondary emissions, further extending emission modeling (e.g. additional application techniques, including cover crops), considering metal-based pesticides in emission models, and systematically assessing human health impacts associated with pesticide

In West Africa, very poorly documented are the recovery trajectories of secondary forests, and even less is known about the origin of the observed variability in recovery rates. To understand the relative importance of local and regional environmental conditions on these trajectories, we inventoried all trees larger than 2.5 cm DBH on 236 plots (0.2 ha), aged from 0 to 45 years plus controls, on eight chronosequences representing the typical regional North-South climatic gradient of West Africa. In a hierarchical Bayesian framework, we modelled recovery trajectories of biodiversity, aboveground biomass and floristic composition and tested the influence of variability in local (plot history, landscape context, remnant trees) and regional (climate and soil) conditions on recovery rates. Our results show that (a) diversity recovers faster than composition and biomass, (b) among the local variables, the number of remnant trees has a positive impact on recovery rates while the duration of agricultural cultivation has a negative impact, and (c) among the regional variables, the high seasonality of precipitation and climate, typical of the dry forests of the northern West African forest zone, leads to faster secondary successions. Our simulation approaches have indicated that poor regional conditions can be counterbalanced by adequate local conditions and vice versa, which argues strongly in favour of a diagnosis that integrates these two aspects in the choice of more or less active technical itineraries for forest restoration.

Around the world, SDMs have been widely used to support forest management planning and biodiversity conservation. Beyond the prediction of species distribution provided by the SDMs, this study aimed to analyze the spatial distribution of tree species diversity using SDMs. The study area is a Faidherbia albida parkland in Central Senegal. It is characterized by a tree-based farming system dominated by Faidherbia albida. Using a robust and representative dataset of 9258 tree species occurrence, we first determined by an SDM the current potential spatial distribution of the 16 main tree species forming the parkland. Specifically, using 6 SDM algorithms and applying several modeling techniques with different categories of predictor variables (e.g., climate, topography, soil properties and human impact) we benchmarked 576 SDMs to achieve best model predictions for tree species. Then, tree species diversity maps were created on the basis of the resulting SDM predictions. Finally, the spatial dynamics of tree species diversity were discussed in relation to landscape characteristics, including heterogeneity, composition and human impact. The results showed that there is no single 'best' SDM algorithm (among the 6 algorithms tested) or modeling approach for all species. Benchmarking several modeling techniques allowed strengthening SDM performance, achieving AUC values that ranged from 0.64 (intermediate accuracy) to 0.87 (very good accuracy). The spatial dynamics of tree species diversity is related to the landscape heterogeneity and composition. In the Sahelian agroforestry systems (AFS), tree diversity is sustained by anthropization. A significant negative correlation with the distance to the village was found, i.e. the closer you get to the village, the greater the diversity of trees. This study could be crucial for analyzing tree species diversity when abundance information is not available.

Global patterns of regional (gamma) plant diversity are relatively well known, but whether these patterns hold for local communities, and the dependence on spatial grain, remain controversial. Using data on 170,272 georeferenced local plant assemblages, we created global maps of alpha diversity (local species richness) for vascular plants at three different spatial grains, for forests and non-forests. We show that alpha diversity is consistently high across grains in some regions (for example, Andean-Amazonian foothills), but regional 'scaling anomalies' (deviations from the positive correlation) exist elsewhere, particularly in Eurasian temperate forests with disproportionally higher fine-grained richness and many African tropical forests with disproportionally higher coarse-grained richness. The influence of different climatic, topographic and biogeographical variables on alpha diversity also varies across grains. Our multi-grain maps return a nuanced understanding of vascular plant biodiversity patterns that complements classic maps of biodiversity hotspots and will improve predictions of global change effects on biodiversity.

El servicio ecosistémico de regulación hídrica es uno de los más importantes que ofrecen los ecosistemas altoandinos. Sin embargo, el conocimiento sobre su aporte en términos hídricos es aún escaso y su estimación difícil, debido a los complejos procesos eco-hidrológicos, climáticos y características ambientales de los Andes. Por ello, se estimó la influencia de tres tipos de ecosistemas (pajonal de puna húmeda, bofedal y bosque de Polylepis) sobre flujos de agua, particularmente los flujos que se dirigen hacia el almacenamiento subterráneo, que equivalen al servicio ecosistémico de regulación hídrica. Dicho estudio se desarrolló durante el año hidrológico 2018 - 2019 en la unidad hidrográfica de Rontoccocha, entre los 3 900 a 4 635 msnm, en el Departamento de Apurímac, Perú. Para ello se modeló el balance hídrico por cada tipo de ecosistema con la herramienta eco-hidrológica Hydrobal. Se usaron variables de: a) vegetación, b) parámetros climáticos y c) características del suelo. Los resultados revelan el aporte de la cobertura vegetal en la regulación hídrica. En cada ecosistema, cerca del 15% de toda la lluvia que precipita en la cuenca logra llegar al almacenamiento subterráneo. Dichos datos extrapolados a toda la unidad hidrográfica, muestran que el pajonal de puna húmeda regula el 80%, el bofedal el 17% y el bosque de Polylepis el 3%. A pesar de que la evaluación se realizó por separado para cada ecosistema, para fines de gestión, es necesario abordarlo de manera integral dado que entre ellos se establecen relaciones de interdependencia.

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.

Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14_psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14_p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased

The forestatrisk Python package can be used to model the spatial probability of deforestation and predict future forest cover in the tropics. The spatial data used to model deforestation come from georeferenced raster files, which can be very large (several gigabytes). The functions available in the forestatrisk package process large rasters by blocks of data, making calculations fast and efficient. This allows deforestation to be modeled over large geographic areas (e.g., at the scale of a country) and at high spatial resolution (e.g., _ 30 m). The forestatrisk package offers the possibility of using logistic regression with auto-correlated spatial random effects to model the deforestation process. The spatial random effects make possible to structure the residual spatial variability of the deforestation process, not explained by the variables of the model and often very large. In addition to these new features, the forestatrisk Python package is open source (GPLv3 license), cross-platform, scriptable (via Python), user-friendly (functions provided with full documentation and examples), and easily extendable (with additional statistical models for example). The forestatrisk Python package has been used to model deforestation and predict future forest cover by 2100 across the humid tropics.

Nature's contributions to human well-being within social-ecological systems have been widely studied using multiple conceptual frameworks, yet there is a growing need to better articulate how both humans and nature contribute to quality of life. We present an operationalization of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) conceptual framework with an in-depth analysis of the coproduction of nature's contributions to people (NCP) in a mountain social-ecological system. Based on a participatory process questioning stakeholders on nature's contributions to their quality of life, we propose an analysis of NCP coproduction mechanisms in a multifunctional landscape. We refine the consideration of NCP coproduction in the IPBES framework by distinguishing three coproduction types at different steps of the benefits flow from ecosystems to quality of life: (1) ecosystem management; (2) mobilization, harvesting, and physical access; and (3) appropriation, social access, and appreciation. For each of these coproduction types, we describe the types of natural and human-derived capital involved. This approach highlights: nature's key contributions to people as perceived by participants; landscape multifunctionality and interlinkages among NCP induced by their simultaneous coproduction to improve quality of life; and a gradient of natural and human-derived capital among coproduction types and among material, nonmaterial, and regulating NCP. This approach documents how NCP coproduction creates social-ecological trade-offs and synergies among multiple NCP, as well as collaborations and conflicts among beneficiaries at the landscape level. We conclude that the analysis of NCP coproduction can provide new opportunities for ecosystem services research by tackling the involvement of both humans and nature in quality of life objectives.

Interface areas in Southern Africa that consist of communal lands located at the edge of protected areas face a growing number of human/wildlife coexistence related issues and among them, the risk of pathogen transmission between wild and domesticated species.In this context, the present thesis aims to 1) Characterize the environmental variables, at a landscape scale at three different interfaces located in Southern Africa (hwange National park, Gonarezhou National Park, North Kruger National Park), that potentially influence the movements of two focal species, one wild ungulates species (the buffalo – Syncerus caffer caffer) and one domesticated ungulates species (the cattle – Bos taurus & Bos indicus) 2) Simulate the movements of the focal species, at the individual and herd scales, in relation with their respective environments, 3) Determine the nature, frequency and localization of the contacts between the focal species to better apprehend the risks of pathogen transmission.A temporal series of Sentinel-2 satellite images have been classified to produce monthly surface water and landcover maps at 10 meters spatial resolution. These environmental variables have then been integrated into a spatialized mechanistic movement model based on a collective motion of self-propelled individuals to simulate buffalo and cattle movements and contacts in response to the surface water seasonality and the type of landcover. To spatialized the movements and contacts models, the domain specific language Ocelet has been used. Telemetry data collected in previous studies have been used as reference data to design, calibrate and validate the movement and contact models.Results highlighted strong space and time variabilities of water availability in the three study areas. Landcover classified maps accurately reproduced the specificities in landscape compositions of the three study areas. By only taking surface water into account, the mechanistic movement models showed a positive and s