Guillaume Péron

The time frame of home-range studies: from function to utilization

Keywords: territory, utilization distribution, functional habitat, buffer size, space use, movement scale, step selection function, network theory

As technological and statistical innovations open new avenues in movement ecology, I review the fundamental implications of the time frame of home-range studies, with the aim of associating terminologies consistently with research objectives and methodologies. There is a fundamental distinction between (a) extrapolations of stationary distributions, associated with long time scales and aiming at asymptotic consistency, and (b) period-specific techniques, aiming at specificity but typically sensitive to the sampling design. I then review the difference between function and utilization in home-range studies. Most home-range studies are based on phenomenological descriptions of the time budgets of the study animals, not the function of the visited areas. I highlight emerging trends in automated pattern-recognition techniques for inference about function rather than utilization.

I. INTRODUCTION

The home-range concept, i.e. the notion that individual animals are spatially restricted, occurs in every branch of ecology and evolution, from the study of the underlying cognitive and behavioural mechanisms that lead to spatial fidelity [START_REF] Schmidt | Site fidelity in temporally correlated environments enhances population persistence[END_REF][START_REF] Hinsch | What do territory owners defend against?[END_REF]Hulse, Fowler, & Honig, 2018), to the genetic [START_REF] Spear | Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis[END_REF], demographic [START_REF] Fretwell | On territorial behavior and other factors influencing habitat distribution in birds. I. Theoretical development[END_REF], and macroecological [START_REF] Kelt | The ecology and macroecology of mammalian home range area[END_REF] consequences. Accordingly, the home-range concept is one of the most prevalent concepts in the ecological literature (>200,000 Google Scholar hits, 6550 in 2018 alone). Almost all species are predicted to exhibit some form of spatial fidelity associated with the way they perceive, access, and use resources. Indeed even species that traditionally were considered nomadic * Author for correspondence (Tel: +33472448111; E-mail: guillaume.peron@univ-lyon1.fr).

are increasingly recognized actually to perform recursions within a finite range, albeit at a slow pace, and also to exhibit short-term site fidelities [START_REF] Owen-Smith | Spatial ecology of large herbivore populations[END_REF][START_REF] Webb | Location matters: using spatially explicit occupancy models to predict the distribution of the highly mobile, endangered swift parrot[END_REF][START_REF] Nandintsetseg | Challenges in the conservation of wide-ranging nomadic species[END_REF]. Yet, the concept remains significantly blurred, as evidenced by a number of recent reviews whose titles are framed as questions (Börger et al., 2008;[START_REF] Kie | The home-range concept: are traditional estimators still relevant with modern telemetry technology?[END_REF]Fieberg & Börger, 2012;[START_REF] Powell | What is a home range?[END_REF][START_REF] Walter | Is there a single best estimator? Selection of home range estimators using area-under-the-curve[END_REF].

The seminal article about the home-range concept was written by [START_REF] Burt | Territoriality and home range concepts as applied to mammals[END_REF]. [START_REF] Burt | Territoriality and home range concepts as applied to mammals[END_REF] clearly separated two aspects or functions of the home range: on the one hand there was the territory, defended against conspecifics, and on the other hand, the area that encompasses the resources necessary for maintenance and reproduction. As is often reported in mammals, [START_REF] Burt | Territoriality and home range concepts as applied to mammals[END_REF] represented the resource-based home range as larger than the territory, and moving into unfamiliar areas is risky and bears a social cost [START_REF] Eason | New birds on the block: new neighbors increase defensive costs for territorial male willow ptarmigan[END_REF][START_REF] Geffen | Climate and habitat barriers to dispersal in the highly mobile grey wolf[END_REF][START_REF] Gautestad | Memory matters: influence from a cognitive map on animal space use[END_REF], so that some areas are in practice barely reachable (Soberón, 2007;Fig. 1). Clearly not all species and individuals have the same intrinsic movement ability, and this should impact the way they form home ranges. Overall, this complexity has led to increasingly phenomenological approaches to the home-range concept [START_REF] Worton | Kernel methods for estimating the utilization distribution in home-range studies[END_REF][START_REF] Laver | A critical review of home range studies[END_REF][START_REF] Powell | What is a home range?[END_REF]. However, recently, fine-resolution long-term tracking technologies have motivated a surge in innovative data-analysis methods that go beyond the description of animal time budgets. These statistical innovations often pertain to new quantities that become available for scrutiny, sometimes before their biological relevance is fully or widely understood [START_REF] Patterson | Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges[END_REF]. Herein I will illustrate my points using an analysis of tracking data from a plains zebra (Equus quagga). Methods for that case study are provided as online Supporting Information in Appendix S1.

II. TIME SCALES IN HOME-RANGE STUDIES

As reviewed above, space use is associated with a time frame. Yet, out of more than 200,000 hits for the phrase 'home range' in Google Scholar (May 13, 2019) and 13,073 hits for which the full text was available via the INEE-CNRS literature search database (http://web.a.ebscohost.com.inee .bib.cnrs.fr), only 8,850 and 702, respectively (5.3%) used a temporal modifier such as 'summer (home) range' or 'daily (home) range', and 9,230 and 613, respectively (4.8%) associated 'home range' with 'timescale', 'time frame', 'temporal scale' or similar phrases. In most other cases, the time frame of the study was determined implicitly by logistical constraints such as the battery life of the tracking unit, the longevity of the study individuals, or the timing of field operations. This is further emphasized by the relative prevalence of studies that quantified weekly or monthly home ranges (791 studies in Google Scholar), two timescales with arguably little biological underpinning, and whose choice was probably motivated by logistical constraints and the need to standardize analyses across individuals.

If we return to the fundamental objectives of movement ecology, one is to understand how individual movement responses to environmental change give rise to emergent patterns like species range shifts and demographic fluctuations (Soberón, 2007). This requires the choice of a time frame that captures the cumulative effect of instant resource acquisition rate on demographic parameters that typically pertain to a long time scale, e.g. annual survival. Many species have evolved strategies for storing energy and resources [START_REF] Drent | The prudent parent: energetic adjustments in avian breeding[END_REF], meaning that the lack of resources at any given time may not correlate with overall performance in the long term. In other words, ideally the time scale for home-range studies should be the same as the scale used to measure individual demographic performance, that is the breeding cycle, the generation time, or the encompassing it [START_REF] Mertl-Millhollen | Olfactory demarcation of territorial but not home range boundaries by Lemur catta[END_REF][START_REF] Grant | Defended versus undefended home range size of carnivores, ungulates and primates[END_REF]. Another important notion that many ecologists have retained from [START_REF] Burt | Territoriality and home range concepts as applied to mammals[END_REF] seminal article is that home ranges should not include the locations that are visited during 'excursions' [START_REF] Olson | Spring excursions of mature male white-tailed deer (Odocoileus virginianus) in north Central Pennsylvania[END_REF], which are interpreted as forays to acquire information but not resources directly [START_REF] Doligez | When to use public information for breeding habitat selection? The role of environmental predictability and density dependence[END_REF]. This essentially resource-based definition of the home range later raised a number of conceptual issues about the significance of territorial behaviours for home-range studies (Fieberg & Börger, 2012;[START_REF] Riotte-Lambert | How memory-based movement leads to nonterritorial spatial segregation[END_REF][START_REF] Hinsch | What do territory owners defend against?[END_REF][START_REF] Kirk | Our use, misuse, and abandonment of a concept: whither habitat?[END_REF]. Species and individuals may exhibit spatial fidelity without being territorial, or may defend access to mating opportunities but not food resources [START_REF] Maher | Definitions of territoriality used in the study of variation in vertebrate spacing systems[END_REF][START_REF] Low | Factors influencing mate guarding and territory defence in the stitchbird (hihi) Notiomystis cincta[END_REF][START_REF] Hinsch | What do territory owners defend against?[END_REF].

In addition, the framework laid out by [START_REF] Burt | Territoriality and home range concepts as applied to mammals[END_REF] did not specifically associate a time frame with the home-range concept. Space use, by definition, can only increase with time [START_REF] Rivrud | Temporal scales, trade-offs, and functional responses in red deer habitat selection[END_REF][START_REF] Van Beest | What determines variation in home range size across spatiotemporal scales in a large browsing herbivore[END_REF], but nevertheless the observed patterns of visits and revisits and the density of use may change over time [START_REF] Benhamou | Beyond the utilization distribution: identifying home range areas that are intensively exploited or repeatedly visited[END_REF][START_REF] Benhamou | Of scales and stationarity in animal movements[END_REF]. Some authors employ the concept of 'daily home range', which makes intuitive sense given the widespread occurrence of circadian cycles in animal activities (e.g. [START_REF] Russo | Daily home range and activity of wild boar in a Mediterranean area free from hunting[END_REF]. Macroecologists, on the other hand, tend to favour a whole-lifespan definition of the home range to enable comparisons across species with different lifespans and different movement rates [START_REF] Lindstedt | Home range, time, and body size in mammals[END_REF]. Conservation biologists also favour a long time frame, because space use measured over a short period of time will not be as inclusive of critical habitats necessary for population and species conservation than space use measured over a long period of time [START_REF] Mcdonald | Territory fidelity, reproductive success and prey choice in the brown falcon, Falco berigora: a flexible bet-hedger[END_REF][START_REF] Schofield | Inter-annual variability in the home range of breeding turtles: implications for current and future conservation management[END_REF][START_REF] Fleming | Rigorous home-range estimation with movement data: a new autocorrelated kernel-density estimator[END_REF]. Indeed, resource requirements may vary over time with environmental drivers, for example droughts or cold spells [START_REF] Frafjord | Home range and movements of arctic foxes Alopex lagopus in Svalbard[END_REF][START_REF] Foley | Severe drought and calf survival in elephants[END_REF][START_REF] Tuqa | Impact of severe climate variability on lion home range and movement patterns in the Amboseli ecosystem, Kenya[END_REF]. Lastly, the influence of the time frame depends on movement rates. Some species and individuals never stray more than a few hours away from their core roosting site [START_REF] Sheldon | Age-specific movement patterns of Ascaphus truei[END_REF]. For such species a few hours of monitoring will give a good idea of their space requirements. Others may spend more than a year between visits to different parts of their home range [START_REF] Webb | Location matters: using spatially explicit occupancy models to predict the distribution of the highly mobile, endangered swift parrot[END_REF][START_REF] Nandintsetseg | Challenges in the conservation of wide-ranging nomadic species[END_REF], keep a multi-year memory of resource locations to be able to respond to emergency situations [START_REF] Foley | Severe drought and calf survival in elephants[END_REF], or suddenly move long distances in response to environmental challenges [START_REF] Péron | Escape migration decisions in Eurasian Woodcocks: insights from survival analyses using large-scale recovery data[END_REF]. All these evolved responses to spatial and temporal variance in the environment [START_REF] Mueller | How landscape dynamics link individual-to population-level movement patterns: a multispecies comparison of ungulate relocation data[END_REF] mean that for some populations, multiple years of monitoring are required to represent the use of space.

A last limitation to [START_REF] Burt | Territoriality and home range concepts as applied to mammals[END_REF] framework is that he did not explicitly address the role of physical and cognitive constraints. Yet, moving is energy-demanding [START_REF] Terrier | Can accelerometry accurately predict the energy cost of uphill/downhill walking?[END_REF][START_REF] Yamada | Long-term calorie restriction decreases metabolic cost of movement and prevents decrease of physical activity during aging in rhesus monkeys[END_REF] and In this Venn diagram inspired by an analogy with the mechanistic drivers of species distribution (Soberón, 2007), the illustrated principle is that individuals look for the intersection that contains enough resources, that can be acquired at a low-enough cost, for the individual to perform as good as or better than its competitors, as measured by its contribution to population growth rate (sensu [START_REF] Coulson | Estimating individual contributions to population growth: evolutionary fitness in ecological time[END_REF]. ξ denotes the sum of the individual's survival plus its surviving offspring at the end of the focal time period t. λ denotes the population growth rate over that period. N here denotes the population size at the beginning of the period.

(A) (B)
The comparison with competitors in the population may be removed, in which case the home range is defined as the location that provides enough resources for a positive contribution to the population growth rate. With that proposed link to demography, home-range properties, such as home-range size, centroid location, or landcover selection coefficients, could eventually be treated like quantitative individual life-history traits that co-vary with population growth ('integral projection model'; Coulson, Tuljapurkar, & Childs, 2010). Once modified to include environmental forcing [START_REF] Ozgul | Coupled dynamics of body mass and population growth in response to environmental change[END_REF], this framework would make it possible to project the future distribution of home-range size and composition in a population, thereby paving the way for space-use-based mechanistic models of species distributions. (B) A simplistic variant. The model animal must meet a given need for resource, R, covering maintenance and reproduction. The resources are patchily distributed with A resources per patch. If movement is costless, the home-range size is R/A [START_REF] Wolf | The effects of density, food, and interspecific interference on home range size in Peromyscus leucopus and Peromyscus maniculatus[END_REF]. If movement is costly, with a cost C to travel from one patch to the next, the animal must acquire R + C resources to offset the cost of movement [START_REF] Mcnab | Bioenergetics and the determination of home range size[END_REF][START_REF] Ford | Home range in a patchy environment: optimal foraging predictions[END_REF] leading to an exponential increase in space use. Note the units on the x-axis: a travel cost of C = 4 means that almost all the resources acquired in a patch are allocated to traveling to the next patch. In practice, C might not be that large relative to A, because resources are concentrated enough, or are self-renewable.

total lifespan. At such scales, biodemographic tools can be used to articulate space use with demographic performance (Fig. 1). Another more practical and immediate argument in favour of choosing a demographically relevant time frame to study the home range lies in the presence of an asymptote in the net squared displacement of home-range-bounded animals [START_REF] Spencer | Operationally defining home range: temporal dependence exhibited by hispid cotton rats[END_REF][START_REF] Giuggioli | Theory of home range estimation from displacement measurements of animal populations[END_REF][START_REF] Bunnefeld | A model-driven approach to quantify migration patterns: individual, regional and yearly differences[END_REF]Börger & Fryxell, 2012;[START_REF] Bastille-Rousseau | Flexible characterization of animal movement pattern using net squared displacement and a latent state model[END_REF]. This emphasis on the asymptotic behaviour neatly aligns with the above arguments about the choice of a long time frame. Studying the asymptote also makes it more straightforward to compare across species and individuals with different transitory dynamics and different sampling schedules. In practice, the meaning of 'a long time' depends on the duration of the transitory period and therefore on the intrinsic movement rates of the focal animal (Johnson et al., 2008a;[START_REF] Fleming | From fine-scale foraging to home ranges: a semivariance approach to identifying movement modes across spatiotemporal scales[END_REF]. Importantly, this long time frame may exceed the monitoring period, calling for extrapolative and model-based methodologies. In the continuous-time stochastic modelling framework, the asymptote can be further explained as the balance between attraction to a central position and random diffusion drawing the animal away from that central position [START_REF] Dunn | Analysis of radio telemetry data in studies of home range[END_REF][START_REF] Blackwell | Random diffusion models for animal movement[END_REF]. Fitting such a continuous-time stochastic process makes it possible to estimate its asymptotic Gaussian distribution whose spread, called the movement variance σ 2 , also constitutes a direct measure of the scale of the home range [START_REF] Giuggioli | Theory of home range estimation from displacement measurements of animal populations[END_REF][START_REF] Fleming | From fine-scale foraging to home ranges: a semivariance approach to identifying movement modes across spatiotemporal scales[END_REF]. In addition, the position autocorrelation time τ measures the rate at which the semivariance of the points of interest are essential for the home range to function as such, but the animals do not spend much time in them [START_REF] Panzacchi | Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths[END_REF][START_REF] Jesmer | Is ungulate migration culturally transmitted? Evidence of social learning from translocated animals[END_REF][START_REF] Scharf | Habitat suitability does not capture the essence of animal-defined corridors[END_REF] compare Fig. 3A with Fig. 3B). By contrast, roosting sites are often over-represented in animal tracking data (Te [START_REF] Wong | Home range, movement and activity patterns, and bedding sites of Malayan sun bears Helarctos malayanus in the Rainforest of Borneo[END_REF]Körtner, Pavey, & Geiser, 2007), but this concentration of use of a few places does not systematically reflect the rarity of the features that led the animals to select these locations. Indeed, the selection process is typically constrained by the way the animals access and acquire information about potential roosting sites [START_REF] Midford | Social learning of a novel foraging patch in families of free-living Florida scrub-jays[END_REF][START_REF] Aplin | Social networks predict patch discovery in a wild population of songbirds[END_REF], perceive risk in novel places [START_REF] Brown | The ecology of fear: optimal foraging, game theory, and trophic interactions[END_REF][START_REF] Gaynor | Landscapes of fear: spatial patterns of risk perception and response[END_REF], and also by physiological and ecological constraints on the response to new information (Fig. 1).

In this context, recently available automated pattern-recognition algorithms pave the way for function-based definitions of the home range, via the detection of different types of points of interest in animal tracking data. There are two main categories of automated pattern-recognition algorithms. Unsupervised algorithms essentially look for patterns in the data using a threshold of dissimilarity to delineate clusters. The user then needs to interpret the final outcome. In the zebra case study, I drew from that philosophy when performing a graph analysis of the movement track (Bastille-Rousseau et al., 2018; Appendix S1). In the graph that the zebra movement steps created, I isolated pixels of high 'betweenness' [sensu [START_REF] Cohen | Complex networks: structure, robustness and function[END_REF] the frequency with which a pixel is on the shortest path between two recorded locations] but low 'centrality' [sensu [START_REF] Cohen | Complex networks: structure, robustness and function[END_REF] the number of steps that start or depart from a pixel]. I then incorporated them into a minimum spanning tree connecting the pixels with a high ratio of betweenness/centrality (Fig. 3A; see Appendix S1 for methods). Some of the branches of that tree connected clusters of high centrality (Fig. 3A). I intuitively interpreted these as corridors between foraging areas. These corridors may correspond to natural features in the landscape that provide safety during travel, to features that are used as landmarks in the cognitive map, maybe as a form of animal culture, or simply to energetic least-cost paths between foraging areas. Other branches of that tree did not end in an area of high centrality (Fig. 3A). The interpretation of these branches is more speculative: they may correspond to excursions as part of the fission-fusion social system [START_REF] Olson | Spring excursions of mature male white-tailed deer (Odocoileus virginianus) in north Central Pennsylvania[END_REF], preferential travel routes when foraging for thinly distributed low-quality resources [START_REF] Schmidt | Ungulate movement in an extreme seasonal environment: year-round movement patterns of high-arctic muskoxen[END_REF], immediate response to predator attacks [START_REF] Courbin | Reactive responses of zebras to lion encounters shape their predator-prey space game at large scale[END_REF], prospection for mates [START_REF] Lovari | Going out to mate: excursion behaviour of female roe deer[END_REF], or possibly visits to rarely needed resources such as self-medication and trace elements [START_REF] Villalba | Sheep self-medicate when challenged with illness-inducing foods[END_REF].

On the other hand, supervised algorithms require a training data set that the user (or perhaps a neural network) must pre-process, e.g. if the objective is to identify barriers to movement, by specifying which movement step corresponds to an interaction with an obstacle and which corresponds modelled positions increases with the time lag between positions, before reaching the σ 2 asymptote [START_REF] Giuggioli | Theory of home range estimation from displacement measurements of animal populations[END_REF][START_REF] Fleming | From fine-scale foraging to home ranges: a semivariance approach to identifying movement modes across spatiotemporal scales[END_REF]. τ can be interpreted as the home-range crossing time, effectively capturing the relationship of the focal animal to space: if τ is small, the animal has a relatively small home range with respect to its movement abilities, and vice versa. One of the key advantages of this type of model-based metrics of home-range scale is the robustness to variation across individuals in sampling rates and in the amount of telemetry error (Johnson et al., 2008a;Fleming et al., 2017). This make these metrics suitable for comparative analyses across individuals, populations, or time periods. However, while these metrics (σ and τ ) capture the scale of the home range, they do not measure its precise shape or composition. Similarly, methods geared to extrapolate the stationary utilization distribution, such as the autocorrelated kernel density estimation (AKDE) [START_REF] Fleming | Rigorous home-range estimation with movement data: a new autocorrelated kernel-density estimator[END_REF]Fig. 2) or the mechanistic home-range estimator [START_REF] Moorcroft | Mechanistic Home Range Analysis[END_REF], are geared towards robustness to variation in sampling design and asymptotic consistency with respect to monitoring duration [START_REF] Noonan | A comprehensive analysis of autocorrelation and bias in home range estimation[END_REF]. They thus ensure that the time scale associated with the home-range estimate is consistently long irrespective of the actual time frame of the data, but they often lack the specificity required to delineate precise home-range contours [START_REF] Péron | Modified home range kernel density estimators that take environmental interactions into account[END_REF]Fig. 3; but see [START_REF] Moorcroft | Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone[END_REF].

Taking all of this into consideration, I suggest a restrictive use for the phrase 'home range', only for situations where the time frame makes demographic sense (Fig. 1). At other time scales, I advocate for a choice of more generic, available terms like 'space use', 'movement domain' (e.g. Johnson et al., 2008b), 'points of interest' (e.g. [START_REF] Benhamou | Beyond the utilization distribution: identifying home range areas that are intensively exploited or repeatedly visited[END_REF], or 'movement amplitude' (e.g. [START_REF] Péron | Modified home range kernel density estimators that take environmental interactions into account[END_REF], rather than 'home range'. Thereby, one would for example talk about seasonal variation in the amplitude of the movements within the home range, instead of seasonal variation in the home-range size itself [START_REF] White | Spatio-temporal patterns of home range use by foxes (Vulpes vulpes) in urban environments[END_REF][START_REF] Rivrud | How does local weather predict red deer home range size at different temporal scales?[END_REF][START_REF] Walter | Is there a single best estimator? Selection of home range estimators using area-under-the-curve[END_REF]. Within the home-range terminology, I further suggest reserving the phrase 'home-range scale' for the asymptotic variance of the movement process and similar second-moment metrics. Lastly, I emphasize the powerful inference about emergent processes and the comparative analyses that the choice of a demographically relevant time scale would eventually make possible.

III. THE DIFFERENCE BETWEEN UTILIZATION AND FUNCTION

The vast majority of home-range studies are based on the phenomenological description of where the animals spend their time or are predicted to spend their time [START_REF] Laver | A critical review of home range studies[END_REF]. But, the time spent at a location is not necessarily representative of its biological importance [START_REF] Powell | What is a home range?[END_REF]. For example, preferred travel corridors that connect [START_REF] Benhamou | Dynamic approach to space and habitat use based on biased random bridges[END_REF]. (C) 50% and 95% isopleths of the autocorrelated kernel density estimator corrected for the reference function approximation bias (AKDEc)-estimated utilization distribution (Fleming & Calabrese, 2017). The diagonal dashed line represents a railway that acts as a partly permeable border to the national park where the zebra was captured. Landcover data for C were extracted from [START_REF] Arraut | The 2013-2014 vegetation structure map of Hwange National Park, Zimbabwe, produced using free images and software[END_REF].

to unimpeded travel. The task of the supervised algorithm is then to find criteria to discriminate between user-defined categories, e.g. what is an obstacle as seen through the lens of animal movement [START_REF] Valletta | Applications of machine learning in animal behaviour studies[END_REF]. Eventually the algorithm uses these criteria to clusterize the rest of the data set, yielding a map of movement-detected obstacles. Such a framework is probably more promising for non-parametric tasks, like identifying territory borders that are not systematically made tangible by a physical feature, than for parametric tasks like identifying the relative effects of different mapped features (Appendix S1). Currently however, to the best of my knowledge, movement ecologists lack a user-friendly framework to implement supervised machine learning for specific movement ecology applications. [START_REF] Ostro | A geographic information system method for estimating home range size[END_REF][START_REF] Long | Home sange and habitat analysis using dynamic time geography[END_REF], but it seems warranted to find ways to make the choice less arbitrary, for example by considering how flight distances and risk avoidance [START_REF] Dill | The influence of distance to refuge on flight initiation distance in the gray squirrel (Sciurus carolinensis)[END_REF][START_REF] Brown | The ecology of fear: optimal foraging, game theory, and trophic interactions[END_REF] impact on the functionality of points of interest. On the other hand, mechanistic approaches start by fitting an empirical mechanistic movement model, typically using ancillary environmental data (e.g. Johnson et al., 2008b). Examples of movement mechanisms include selection for specific landcover types, reluctance to cross linear features, attraction for the home range of potential mates, and repulsion for the presence of territorial neighbours [START_REF] Moorcroft | Mechanistic Home Range Analysis[END_REF][START_REF] Horne | A synoptic model of animal space use: simultaneous estimation of home range, habitat selection, and inter/intra-specific relationships[END_REF]. One of the weaknesses of mechanistic approaches is however that they are sensitive to the fit of the underlying mechanistic model. For example, in the zebra case study (Fig. 3), I used a time-constant landcover selection model, but in reality landcover selection was temporally variable [START_REF] Courbin | Reactive responses of zebras to lion encounters shape their predator-prey space game at large scale[END_REF], and this likely led to overestimating the use of bushland. Another particular caveat is that some mechanistic methods rely strongly on an equilibrium assumption [START_REF] Moorcroft | Mechanistic Home Range Analysis[END_REF] whereas space use may not be stationary. To address these non-stationarities, individual-based simulations again appear promising [START_REF] Wang | Home range dynamics and population regulation: an individual-based model of the common shrew Sorex araneus[END_REF]. More generally, the main usage for mechanistic extrapolations might be to compare them to observed space use, as a way to assess the realism and parsimony of the set of movement rules that were used to extrapolate.

IV. CONCLUSIONS

(1) Space-use studies are by construction associated with a time frame. I advocate for the systematic reporting of that time frame in publication abstracts, for the increased use of standardized time frames, and whenever possible, for the use of demographically relevant time scales such as the breeding cycle, generation time, and total lifespan. I also suggest restricting the use of the phrase 'home range' to studies that are performed at demographically relevant time scales.

(2) The extrapolation of stationary utilization distributions and the model-based estimation of movement variance pertain to long time scales. They correct for the effect of the actual duration of the monitoring period and are more likely to match demographic schedules than period-specific estimates.

(3) I recommend a more consistent association between terminology, methodology and research objectives. For example, model-based extrapolations of stationary utilization distribution lend themselves to comparative analyses because of their asymptotic consistency, and to conservation biology applications because they extrapolate space requirements over representative periods of time. By contrast, interpolations are geared for period-specific inference and for the quantification of process uncertainty.

Both these options are data-driven, meaning that they are by construct limited to the information contained in the relocation data. The special case of resources that are critical to animal fitness but are seldom visited represents a strong limit to this type of approach [START_REF] Powell | What is a home range?[END_REF]. Examples of such resources include salt licks that the animals may only visit after ingesting toxic forage [START_REF] Villalba | Sheep self-medicate when challenged with illness-inducing foods[END_REF]. Visits to salt licks may be too far apart in time to be recorded. However, as long as adequate information is available to parameterize the simulating model, e.g. knowledge about physiological requirements for trace elements [START_REF] Villalba | Sheep self-medicate when challenged with illness-inducing foods[END_REF], they can be simulated using individual-based models [START_REF] Wang | Home range dynamics and population regulation: an individual-based model of the common shrew Sorex araneus[END_REF][START_REF] Signer | Estimating utilization distributions from fitted step-selection functions[END_REF]; Fig. 2D). The flexibility of individual-based simulations could also offer ways to weigh the density of use according to factors other than the time budget, e.g. according to the internal state, as a way to further the difference between function and utilization.

Even if maintaining the focus on utilization, the choice of method should be made according to two major dichotomies, depending on the time frame of the inference and the objectives of the research (e.g. broad space requirements versus specific points of interest; Fig. 2). First, the utilization distribution, that is the probability p(r) that location r is used at any given time, can be either extrapolated or interpolated. The leading home-range methodology, kernel density estimation (KDE) [START_REF] Worton | Kernel methods for estimating the utilization distribution in home-range studies[END_REF], unambiguously belongs to the class of extrapolation methods [START_REF] Silverman | Density Estimation for Statistics and Data Analysis[END_REF][START_REF] Turlach | Bandwidth selection in kernel density estimation: a review[END_REF], but importantly, its standard implementation can yield flawed results when naively applied to animal tracking data [START_REF] De Solla | Eliminating autocorrelation reduces biological relevance of home range estimates[END_REF][START_REF] Fleming | Rigorous home-range estimation with movement data: a new autocorrelated kernel-density estimator[END_REF]. The robustized version of KDE offers asymptotic consistency [START_REF] Fleming | Rigorous home-range estimation with movement data: a new autocorrelated kernel-density estimator[END_REF]Fleming & Calabrese, 2017;[START_REF] Noonan | A comprehensive analysis of autocorrelation and bias in home range estimation[END_REF], but typically lacks the level of specificity that biologists may expect from a home-range delineation technique [START_REF] Benhamou | Incorporating movement behavior and barriers to improve kernel home range space use estimates[END_REF][START_REF] Slaght | Home range and resource selection by animals constrained by linear habitat features: an example of Blakiston's fish owl[END_REF]compare Fig. 3C with Fig. 3A). By contrast, interpolations focus on one single realization of the movement path during a finite period of time, affording them more specificity especially with regard to customizing the time frame and delineating specific points of interest [START_REF] Benhamou | Beyond the utilization distribution: identifying home range areas that are intensively exploited or repeatedly visited[END_REF][START_REF] Kranstauber | A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement[END_REF]. However, the spread of the interpolated distribution quantifies the process uncertainty around the interpolated movement path (sensu De [START_REF] De Valpine | Fitting population models incorporating process noise and obervation error[END_REF], the home-range size [START_REF] Horne | Analyzing animal movements using Brownian bridges[END_REF][START_REF] Benhamou | Dynamic approach to space and habitat use based on biased random bridges[END_REF][START_REF] Fleming | Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data[END_REF].

The second dichotomy pertains to the level of mechanistic realism in the models that underlie the interpolations or extrapolations (Fig. 2). Phenomenological approaches use the tracking data, they assume that the movements are purely stochastic, and that space is homogeneous. They are not designed to accommodate landcover selection rules for example, but instead to compute statistically robust buffers around the recorded locations. As a side note, adding arbitrary buffers around recorded locations is something that many spatial ecologists are clearly not reluctant to do (4) Automated pattern-recognition techniques, such as those based on the graph analysis of movement tracks, pave the way for a function-based definition of the home range instead of, or in complement to, utilization-based definitions. Examples of functions that can readily be mapped out include roosting, foraging, and travelling.

(5) Individual-based simulations offer a potentially important way to fine-tune that inference by (a) incorporating features not documented during the monitoring period, e.g. rarely visited yet critical resources such as trace elements; and (b) furthering the distinction between utilization and function by incorporating a process about the internal state of the animal and its effect on movement rates.

Fig. 1 .

 1 Fig. 1. (A) Three main classes of factors are expected to influence animal movement decisions shaping the home range: (1) food and shelter resources for maintenance and reproduction; (2) social interactions with congeners, including reproduction, competition, and sociality; (3) costs of and constraints on movement, including energy expenditure, environmental barriers and cognitive biases.In this Venn diagram inspired by an analogy with the mechanistic drivers of species distribution(Soberón, 2007), the illustrated principle is that individuals look for the intersection that contains enough resources, that can be acquired at a low-enough cost, for the individual to perform as good as or better than its competitors, as measured by its contribution to population growth rate (sensu[START_REF] Coulson | Estimating individual contributions to population growth: evolutionary fitness in ecological time[END_REF]. ξ denotes the sum of the individual's survival plus its surviving offspring at the end of the focal time period t. λ denotes the population growth rate over that period. N here denotes the population size at the beginning of the period. The comparison with competitors in the population may be removed, in which case the home range is defined as the location that provides enough resources for a positive contribution to the population growth rate. With that proposed link to demography, home-range properties, such as home-range size, centroid location, or landcover selection coefficients, could eventually be treated like quantitative individual life-history traits that co-vary with population growth ('integral projection model';Coulson, Tuljapurkar, & Childs, 2010). Once modified to include environmental forcing[START_REF] Ozgul | Coupled dynamics of body mass and population growth in response to environmental change[END_REF], this framework would make it possible to project the future distribution of home-range size and composition in a population, thereby paving the way for space-use-based mechanistic models of species distributions. (B) A simplistic variant. The model animal must meet a given need for resource, R, covering maintenance and reproduction. The resources are patchily distributed with A resources per patch. If movement is costless, the home-range size is R/A[START_REF] Wolf | The effects of density, food, and interspecific interference on home range size in Peromyscus leucopus and Peromyscus maniculatus[END_REF]. If movement is costly, with a cost C to travel from one patch to the next, the animal must acquire R + C resources to offset the cost of movement[START_REF] Mcnab | Bioenergetics and the determination of home range size[END_REF][START_REF] Ford | Home range in a patchy environment: optimal foraging predictions[END_REF] leading to an exponential increase in space use. Note the units on the x-axis: a travel cost of C = 4 means that almost all the resources acquired in a patch are allocated to traveling to the next patch. In practice, C might not be that large relative to A, because resources are concentrated enough, or are self-renewable.

Fig. 2 .Fig. 3 .

 23 Fig. 2. A classification of the utilization-based home-range estimators. The illustrations are from the zebra case study, using the estimator indicated in bold (see Appendix S1 for methods) Dark and light grey areas respectively represent the 50% and 95% isopleths of the estimated utilization distribution. The diagonal dashed line represents a railway that acts as a partly permeable border to the national park where the zebra was captured. Landcover data for the movement-based kernel density estimator (MKDE) and individual-based simulation (IBS) were extracted from Arraut et al. (2018).

V. ACKNOWLEDGMENTS

I thank anonymous reviewers for constructive comments that greatly improved the quality of this article. S. Chamaillé-Jammes provided the zebra data and helpful comments on a previous draft. Data collection was funded by ANR-08-BLAN-0022 and ANR-16-CE02-0001-01, under Zimbabwe Parks and Wildlife Management Authority permit # 23(1)(c)(ii)03/2009. I thank C.H. Fleming and J.N. Calabrese for introduction to and discussion about AKDE, and the Mov-it working group coordinated by A. Loison (ANR-16-CE02-0010) for insightful discussions that motivated this review.

VI. REFERENCES


Alexandra Bourdot
email: alexandrabourdot@gmail.com

Camille Magniont

Méryl Lagouin

César Niyigena

Philippe Evon

Sofiane Amziane

Impact of Bio-Aggregates Properties on the Chemical Interactions with Minerai Binder, Application to Vegetal Concrete

Plant concretes were developed and are currently used as filling material in a timber frame. Their properties are strongly related to the bio-aggregates characteristics. ln addition, since hemp shiv, the reference bio-based aggregate, has a lin1ited availability, it is necessary to consider alternative bio-aggregates largely and locally available. Thus, this paper fo cused on identifying and understanding mechanisms of interaction between different bio-aggregates and minerai binders. To address this issue, the first objective was to determine the properties of fi ve hemp shives and two alternative bio aggregates for vegetal lightweight concrete: corn and sunflower bark particles. The study of the chemical interactions between bio-aggregates and a pozzolanic binder was conducted on mode! pastes mixed with filtered solutions contain ing bio-aggregates extractives. The mechanical properties of the paste, as well as their hydration and their mineralogical evolution were studied. ln the last part, the mechanical behavior ofvegetal concretes was assessed. The results highlight a strong relation between the mechanical behavior of pastes and concretes and the extractive content of the different tested bio-aggregates. Finally, pastes appeared as a relatively good model to predict the behavior of concretes by follow ing their early age performances: setting delay and 3-day mechanical strength.

Introduction

The building sector is responsible for major environ mental impacts: constrnction accounts for about 40% of CO 2 emissions in developed countries, 37% of energy consumption and 40% of waste produced [START_REF] Deshayes | The building sector and the challenges of sustainable development: innovation strategies and/or change issues[END_REF]. Consequently, the design and characterization of i nnovative eco-friendly building materials has become a priority. The incorporation of bio-based raw materials could be a response to this environmental challenge since they are renewable, mainly by-products of local crops, and carbon neutral (Cherrett el al. 2005;[START_REF] Maalouf | An energy and carbon footprint assess ment upon the usage of hemp-lime concrete and recycled-PET façades for office facilities in France and Italy[END_REF]Pretot et al. 2014). Over the last fifteen years, these environmental benefits have contributed to the development of a specific building material called "hemp concrete". This composite combines a minerai binder and a plant aggregate: hemp shiv, i.e., the ligne ous by-product resulting from the mechanical extraction of technical fibers from hemp stalk. Many studies have underlined the great interest of the hygrothen11al proper ties of bio-based materials (Bourdot et al. 2017; Collet and Pretot 2012; Rahim et al. 2014; Ratiarisoa et al. 2016). Use of bio-aggregates reduces the thermal con ductivity of many materials because of the high porosity ofhemp shives (Bennai et al. 2018; Bourdot et al. 2017; Rahim et al. 2016), while moisture exchanges with the air around the material could enhance indoor comfort. Nevertheless, hemp bio-based materials can have variable mechanical properties in accordance with dry ing process (Benfratello et al. 2013), curing conditions and tin1e of demolding [START_REF] Pa Via | Effect of testing variables on the hydration and compressive strength of lime hemp concrete[END_REF], nature of the binder matrix (Balciünas el al. 2013; Dinh 2014; Stevulova et al. 2013), composites design [START_REF] Balciünas | Physical properties and strncture of composite made by using hemp hurds and different binding materials[END_REF], or hemp shives characteristics (Arnaud and Gourlay 2012; Stevulova et al. 2012). Hemp shives characteristics can be length or nature of shives, includ ing with hemp shives in compliance with the require ments of the label hemp aggregate of "Construire en chanvre", meaning "Build with hemp". Indeed, the re placement of hemp type by another can generate me chanical performance differences up to a factor 10 (Niyigena et al. 2018). In addition, stiffness problems with crumbling can be observed with minerai binder (Sinka et al. 2014).

According to the literature, these observations could result from chemical interactions between water-soluble compounds of the plant aggregates and minerai species of the binder. Severa! researchers showed a negative impact of hemp or other lignocellulosic particles on the setting and the early hardening of cement paste. Dique lou et al. (2015) studied the short term interaction be tween hemp and flax shives with a cernent matrix and showed a halo of non-hydrated cernent surrounding hemp particles. The formation of hydrates appears to be inhibited in composites combining bio-aggregates and a pozzolanic binder [START_REF] Dinh | Contribution to the development of precast hempcrete using innovative pozzolanic binder[END_REF] or Portland cernent (Diquélou et al. 2015) involving a deficit about 20 to 25% in compressive strength (Diquélou et al. 2015; Ratiarisoa et al. 2016). Severa! mechanisms were pro posed to explain the action of lignocellulosic com pounds with numerous parameters but observations dif fered as reviewed by [START_REF] Magniont | Chemical composition of bio-aggregates and their interactions with minerai binders[END_REF]. At early age, it should be a migration of extractives into the paste inducing the inhibition of the formation of some hydrates: a decrease in C-S-H and undetected cal cium hydrates. The alkaline degradation of bio aggregate particles induces the release of calcium car bon dioxide, which could be responsible for the car bonation of portlandite. Consequently, a deficit in port landite can be highlighted. Distinct ways of action were summarized by [START_REF] Magniont | Chemical composition of bio-aggregates and their interactions with minerai binders[END_REF]: trap ping of calcium ions present in the binder solution by plant components, formation of a thin adsorption layer on the surface of cernent grains, poisoning of the nu cleation sites by adsorption on the first Ca(OH) 2 and C S-H hydrates, formation of calcium carbonate.

These short tenn chemical interactions are signifi cantly influenced by the nature of the inhibitory mole cules, the concentration of plant extracts, the nature of the binder, the production process of plant aggregates, the storage condition of bio-aggregates and the part of the plant involved. The species responsible for the dele terious effect could be water-extractives of lignocellu losic partiel es or even products of the alkaline attack of the particles by the cement or lime-based matrixes (Diquélou et al. 2015; Govin et al. 2005). These early age in1pacts could have direct consequences on medium tenn perfo1111ances. However, other long-term interac tions between plant particles and binder could occur, affecting the performances of the concrete. Until now, few studies have been made on these phenomena in bio aggregates based materials. In the context of the devel opment of plant concretes, one of the challenges would be proposing a straightforward characterization test to qualify the level of interaction with the minerai matrix inducing acceptable or unacceptable mechanical strength.

ln addition, although hemp has been considered as the reference for bio-aggregates based building materi als, its availability is limited. Therefore, it is necessa1y to consider other bio-aggregates largely and locally available. Some alternative bio-aggregates, such as flax shives (Rahim et al. 2015), rape straw (Rahim et al. 2016), lavender stalks [START_REF] Ratiarisoa | Assessment of distilled lavender stalks as bio-aggregate for building materials: hygrothermal propetiies, mechanical perfom1ance and chemical interactions with minerai pozzolanic binder[END_REF] etc. have been studied. This paper proposes to focus on bark par ticles from sunflower and corn stalks, both selected for their large availabilities in South-West part of France. Corn is the second most cultivated cereal in France, generating large quantities of by-products: 5.2 tons of stalks in 2015. Sunflower is grown for its seeds to pro duce vegetal oil with about 614 000 ha cultivated na tionwide. 230 000 tons of by-products were produced each year due to this agricultural crop. ln comparison, only 17 000 t/year of hemp by-products are available (Laborel-Préneron el al. 2018).

Therefore, the present study aims to characterize five sources of hemp shives and to evaluate the potential of two other agricultural by-products ( corn and sunfl ower bark particles) as alternative bio-aggregates for vegetal lightweight concrete. The paper focuses on the extrac tives, which could internet with the minerai binder. The extractives could play a rote in the stiffuess of pastes and thus in the mechanical behavior of bio-aggregate based concretes. The first part of the study focused on the plant particles characterization. According to the recommendations of RILEM Te chnical Committee 236-BBM [START_REF] Amziane | Recom mendation of RILEM TC 236-BBM: characterisation testing of hemp shiv to detennine the initial water content, water absorption, dry density, particle size distribution and thern1al conductivity[END_REF]), bulk density, particle size distribution and water absorption capacity were deter mined on different bio-aggregates. Because of the vari ability of hemp properties due to their nature, five hemp shives are studied. Two other aggregates largely and locally available, as replacement of hemp are character ized: bark particles extracted from corn and sunflower stalks. ln a second part, the impact of the water-soluble components extracted from the different bio-aggregates on the binder paste setting and hardening mechanisms was investigated.

The identification of water-soluble substances being far from obvious, the objective of the article is to evalu ate if the quantity of water-soluble substances easy to measure, the hydration delay or the decrease in the me chanical strength of mode! pastes, can be a reliable indi cator regarding interactions and the impact on the me chanical strength of hemp concrete. The mechanical performances were studied through compressive strength and explained by the hydration and hardening mechanisms of the lime-metakaolin binder from XRD, TGA and calorimetric measurements. Finally, the me chanical performances of the associated bio-aggregate concretes were compared and a discussion was con ducted to link the results obtained on bio-aggregates, mode] pastes and plant concretes.

Materials and methods

Materials

( 1) Bio-aggregates Different hemp shives from five strains were character-ized to observe the influence of their properties on the mechanical behavior of hemp concretes. The samples are presented in Fig. 1. They are denoted as H 1 (KANABAT), H2 (Agrofibre), H3 (CVF), H4 (Standard Lézoux) and H5 (ISOcanna, CESA). Corn and sun flower bark particles, denoted as Co and S, are also evaluated and compared to hemp shives. Alternative aggregates result from the combination of a preliminary stage of grinding of the entire stalk and a second phase of separation of the pith from the bark using a tilted conveyor belt and a blowing system. The bark particles under 1 mm were then eliminated by sieving.

(2) Minerai binder and admixtures The binder is composed of 70% by weight of metakao lin and 30% by weight of aerial lime. Metakaolin is a pozzolanic admixture produced by flash calcination of kaolinite at 700 °C, produced by ARGECO Développe ment Co. in France. It is mainly composed of quartz, silicon and aluminium oxides with an amorphous sili coaluminate mineralogical form. The latter phase is re sponsible for the pozzolanic activity of metakaolin, which reactswith calcium hydroxide (Ca(OH) 2 ] to fom1 C-S-H gel, calcium aluminate hydrates [C 4 AH 13 ] and [C 3 AH 6 ] -hydrogamet and calcium alumino-silicate hydrates [C 2 ASH 8 ] -straetlingite [START_REF] Rojas | T he effect of temperature on the hydration rate and stability of the hydration phases of metakaolin-lin1e-water systems[END_REF]. The flash metakaolin has a lower water demand than traditional rotary metakaolins, leading to a better workability of composites [START_REF] San Nicolas | Characteristics and applications of flash metakaolins[END_REF]. The aerial lime is around 92% [Ca(OH) 2 ]. Two admix tures were used to improve the performance of the com posite at early age: potassium sulfate, a chemical activa tor to encourage ettringite fomrntion and accelerates the pozzolanic reaction, and a superplasticizer to improve the workability.

Methods of aggregate characterization

The physical characterization of hemp shives was real ized according to the RlLEM Technical Committee 236-BBM recommendations on bio-based materials (Amziane et al. 2017).

(1) Bulk density The material was dried at 60 °C until constant mass (variation Jess than 0.1 % bet\veen two readings at 24 h). The sample of hemp shiv was put in a cylindrical con tainer of 28.3 cm in height and of 12 cm in diameter (Fig. 2A). The quantity of the material was adjusted to be half the volume of the container. The cylinder was then upend ten times. The volume was marked by a cardboard and measured with water. The test was re peated 3 times.

(2) Oust content Oust is defined as particles with length or width lower than 0.5 mm. 5 samples of 50 g were sieved mechani cally with 315 mm diameter sieves for 30 minutes.

(3) Particle size distribution by image processing The image analysis method used a scanner and "lm ageJ" software, which is an open source image process ing program designed for scientific images. This method was carried out on bio-aggregates samples of 3 to 6 g each, in order they include at least 2000 identified parti cles. A scanner was used in order to acquire 8-bit gray scale image at 600 DPI resolution. Paiiicles were laid on a transparent paper on the glass of scanner and covered by a dark background in order to obtain the maximum contras!. Before scanning, the particles were arranged in such a way that they did not touch or overlap one an other. Images were analyzed using the ImageJ software (1)

Aggregates were dried at 60 °C until a constant mass ( change in mass of the sample less than 0.1 % over 24 h).

The initial water content W 0 is calculated with the fol lowing equation:

M -M W.= 0 0 xl00 o D ( 2
)
where M 0 the initial mass of aggregates (g) and M D the mass of the dry aggregates (g). Three dried samples were put in synthetic permeable bags (with square mesh around I mnl) and immersed in water. The gain in mass was measured after I min, 15 min, 4 h and 48 h: bags were placed in a "salad spinner" and wrung by 2 rotations per second for 50 s to elimi nate the water adsorbed at the surface of plant particles or remaining among them. The water absorption capac ity W(t) in % weight is obtained by the following equa tion:

W(t) = M(t)-M o xlOO M o (3)
where !vl(t) is the mass ofwet aggregates after each time of the immersion (g) and M D the mass of d1y aggregates (g).

The results presented are mean values of measure ments taken on three different samples.

(5) Extractives proportion Before the tests, samples were crushed to a grain size of less than 1 mm (Fig. 28). The water-soluble content, adapted from the TAPPI 204 cm-97 standard [START_REF] Tappi | Solvent extractives of wood and pulp[END_REF], is determined by boiling aqueous extraction for 1 h. The content of water-soluble compounds is deter mined by a gravimetric method. About I g of raw mate rial, previously dried, is introduced into a sintered po rous crucible and placed on a hot extractor (Fibertec Tecator M 1017) shown in Fig. 2C. The material im mersed in 100 ml of water is boiled for 1 h. The extract is then sucked up using a vacuum tube. Once filtered, the sintered sin ter is put to dl)' in an oven at 105 °C for 24 h. The weighing of the sintered material after drying allows the dete1mination of the overall content ofwater soluble compounds of any biochemical nature. Ali de terminations were carried out in duplicate.

Experimental methods on binder pastes ( 1) Elaboration of pas tes

Eight different mixes were tested and, for each mix, the test was repeated 3 limes. The eight mixes differed by the nature of the mixing solution: a control paste was made with demineralized water, and the other seven pastes were mixed with the filtrate of bio-aggregates containing extractives. The filtrates were obtained after immersion of crushed particles (under I mm) of bio aggregates in demineralized water for 48 h at ambient temperature. The water to particles mass ratio was 15 in order to extract high quantity of filtrates. Paste was mixed with a Controls mixer complying with the NF EN 196-1 standard (AFNOR 2006). The binder was composed of 70% metakaolin and 30% aerial lime by weight for ail pastes. The distillated water or bio aggregate filtrate to binder ratio was 0.55. The potas sium sulfate to binder ratio was 0.03 and the superplas ticizer to binder ratio was 0.016. The mixing sequence is presented in Table 1.

After mixing, the binder paste was cast in 40 x 40 x 160 mm moulds complying with NF-EN 196-3 (AFNOR 2017). The samples were then demolded at 3 days, eut in three samples of 40 x 40 x 40 mm and continuously . (2) lsothermal calorimetry The hydration process of the pastes was studied by iso the,mal calorimetry using a microcalori. meter (TAM A[R model 3116). The mixing of the ingredients, previ ously balanced at the calori.meter temperature (20 °C), was carried out manually outside the calorimeter. Heat flow due to the hydration reactions was recorded during 6 days.

(3) Compressive strength of pastes Compressive strength tests were perfo1med on 40 x 40

x 40 mm specimens with ages at 3, 14 and 150 days.

They were conducted with a constant loading speed of 2.4 kN/s according to NF-EN 196-1 standard (AFNOR 2006).

(4) Thermogravimetric analysis Thermogravimetric analyses were performed on the d1y powdered samples, weighing around 1050-1550 mg each that passed through an 80 �un sieve. The thermal analyzer (NETZSCH STA 449 F3 Jupiter®) operates at a heating rate of 10 °C/min up to 1000 °C. Analyses were perfonned 24 h, 48 h, and 9 months. For each age, the hydration is stopped by inunersion in liquid nitrogen and f r eeze-drying.

(5) X-Ray diffraction The measuring system was a diffractometer (Bruker D8 Advance) using Ka (A= 1.542 A) copper anticathode. The 20 values, which ranged from 40 to 700, were re corded in 0.020 steps with an acquisition lime of 0.25 s per step. Measurements were performed on crushed samples, of ages 24 h, 48 h, and 9 months passing through an 80 pm sieve. For each ages, the hydration is stopped by immersion in liquid nitrogen and freeze d1ying by placing under vacuum (13.3 Pa) in a freeze d1yer for 24 h.

Experimental methods on concretes

(1) Elaboration of plant aggregates-based con cretes Two concrete types were manufactured. The influence of hemp shive nature is studied using a lime binder pro ducing "hempcrete". The alternative bio-aggregates are employed with the alternative pozzolanic binder in or der to characterize a total alternative bio based concrete.

On the one hand, hemp-concrete was elaborated with hemp shives H3, H4, H5 and a commercial lime binder. Table 2 and Table 3 report respectively the correspond ing mix proportioning and the mixing sequence. The cylindrical test specimens (011 x 22 cm) were cast fol lowing a methodology described in the work of Niy igena et al. (2018). The moulds were fi lied in four lay ers manually compacted. The upper surface of each layer was scratched to help the next layer to adhere properly. To ensure a constant compacting energy, the same operator realized ail the compactions and a s.i milar fresh weight were reached for each sample.

When filled and weighted, the specimens were left in their moulds for 72 h. After removal, open-air drying took place in a test room (average conditions: 21 °C and 35% RH). Finally, speci.mens were oven dried at 60 °C for 48 h, prior to the mechanical testing.

Thereafter, the hemp concretes are referred to H3C, H4C and H5C for samples made f r om H3, H4 and H5 hemp shives respectively.

On the other hand, sLmflower and corn bark particles were used in association with the pozzolanic binder composed of 70% by weight of metakaolin and 30% by weight of aerial lime for the design of an alternative plant concrete. Additionally, in order to improve the perfonnance of the concrete at early age, potassium sui fate was added (2.9% by weight of binder). K 2 S0 4 is a chemical activator that encourages ettringite formation and accelerates the pozzolanic reaction [START_REF] Dinh | Contribution to the development of precast hempcrete using innovative pozzolanic binder[END_REF]. Moreover, a superplasticizer was introduced in water to improve the concrete workability (1.65% by weight of binder). The total amount of water results f r om the addi tion of pre-wetting water, adjusted to the absorption capacity of plant aggregates after l minute, and mixing water added after the anhydrous binder. After mi., xing, the mix was cast in 011 x 22 cm cylin drical moulds in three layers under manual vibro compaction for 5 s. The moulds were removed from concrete samples in two phases. First, top and bottom of the mou Ids were tumed out of the specimen 7 days after concrete pouring. 48 days later, samples were com pletely demoulded.

(2) Mechanical strength The compressive strength tests on the specimens (0! 1 x 22 cm) were performed respectively after 30 and 60 days of curing for hemp concrete and sw1flower/corn concrete. The tests were performed using a 100 kN ca pacity hydraulic press. At least three samples were tested for each formulation. The load was applied at a constant deflection rate of 3 mm/min while the un loading was realized at 6 mm/min. The following four step loading cycle was applied to samples.

Loading up to 1 % of strain -unloading up to 0%; Loading up to 2%unloading, 0%; Loading, 3%unloading, 0%; Loading, 15% (resp. 20%) for the sunflower-based (resp. corn-based) material itemsunloading, 0% The Young's modulus E of each specimen was then calculated according to a procedure named "floating modulus" from [START_REF] Niyigena | Variability of the mechanical properties of hemp concrete[END_REF][START_REF] Niyigena | Assessing the impact of calculation methods on the variability of Young's modulus for hemp concrete material[END_REF], which is the mean value of maximum values obtained at the sec ond, third and fourth loading steps.

Results and Discussion

Bio-aggregates characterization ( 1) Bulk density

The bulk densities of bio-aggregates are summarized in (2) Dust content Dusts are particles with a diameter lower than 500 µm. Table 6 summarizes the measurements. The percentage of dust is largely variable according to hemp shiv. Hl, H2 and H3 have a percentage of 1 to 2% by weight, whereas H4 has less dust, under 1 % by weight, and H5 has a high percentage of dust with 5.74% by weight.

Regarding the alternative bio-aggregates, corn particles present the second highest propo11ion of dust. On the contraiy, sunflower aggregates exhibit the lowest dust content (0.66%) among the seven bio-aggregates.

(3) Particle size distribution The image analysis method allows plotting the cumula tive area of particles as a function of three morphologi cal parameters: the mean equivalent diameter (abbrevi ated as EAD: equivalent average diameter), the average size assuming that particles have spherical shape, and the major and minor axes, i.e., length and width as pre sented in [START_REF] Nozahic | Design of green concrete made of plant-derived aggregates and a pumice-lime binder[END_REF]. These size distributions of the 5 hemp shives are plotted on Fig. 3. The circular ity of particles is a shape factor representing how close to a circle the shape of the particle is (it is equal to 1 for a perfect circle). This is also represented for the five hemp shives on Fig. 4. Hemp shiv H4 displays the biggest particles, more than 80% of the particles have a length higher than 10 mm, and the highest difference between minor and ma jor axis. This is in accordance with a low circularity. On the contrary, H3 has very close minor, major and EAD curves. Regarding the EAD cw-ve, the cutting of the H3 shiv is regular around 2 to 3 mm. This particle size dis tribution results from a higher circularity compare to the other shives. Hl and HS have greater size variability with EAD ranging from I to 10 mm. H2 has an inter mediate particle size with EAD ranging from 1 to 7 mm. These observations are consistent with circularity. Con sequently, from the histogram of the circularity of the aggregates, large differences appear between the differ ent hemp aggregates. Circularity can be classified as H4 < Hl < HS < H2 < H3.

These observations can be correlated to bulk density measurements with a low value for H4 explained by a big length and large aspect ratio of these shives, whereas for H3 and H2, the reduced size and enhanced circularity encourage the higher compactness of the bulk arrangement.

The same methodology was applied on corn and sun flower particles. The fitted curves and histograms of particle size distribution are presented in Figs_ 5 and 6.

On the figures, the curves and histograms are compared to extreme values of hemp shives H3 and H4.

0 0 i-o 1-" N N i � i � i � 0 1-" 1-" N N VJ V, 0 V, 0 V, 0 1000
bark particles. A more adequate calibration process for these alternative aggregates could correct this point. Their circularity is also lower than for the majority of hemp particles in particular for corn particles. This re sult is consistent with bulk density measurement. Corn par1icles are more elongated. Consequently they induce a larger inter-particle porosity resulting in lower bulk density.

( 4) Water absorption The initial water contents are presented in Table 6. lni tially, H2 has the higher moisture value of9% by weight. HI has a proportion of 8% by weight. H4 and HS have sirnilar initial water content of nearly 7% by weight. H3 has the lowest rnoisture value of 6% by weight. After a drying procedure, the kinetics of water ab sorption of hernp shives and alternative aggregates were assessed and are plotted in Fig. 7. The figure presents the evolution of the water content of aggregate as a logarithmic function of lime as proposed by [START_REF] Nozahic | Towards a ne w approach to the design of lignocellulosic plant concretes based on understanding and improving the binderlplant interface: application to chenevot and sunjlower stem aggregates combined with a pumice/lime binder[END_REF]. Two specific pararneters were deterrnined basing on the following equation:

w =IRA+ K I X ln(t) (4)
where W is the water absorption, K 1 can be linked to diffusion rate of water in bio-aggregate cells with a To compare the results, Table 7 presents the parame ters IRA and K, for ail the bio-aggregates. After I min ute of immersion, the initial rate of absorption (IRA) of the different hemp shives range from 140 to 200% by weight. H4 absorbs less than the other shives with 148% by weight white H3 has the highest value of absorption at 195% by weight. This observation is consistent with the previous measurements. H4 particles are the biggest and the most elongated particles inducing a reduced specific surface and, in particular, a reduced cross sec tion exposed to the water. This would be responsible for the lowest water adsorption and very short-tem1 absorp tion in large pores when immersed in water. H 1, H2 and H5 have similar IRA between 160 and 170% by weight. On the other hand, H 1 has a di fferent rate of absorption with a K 1 equal to 21.9, higher than that of the other hemp shives around 15 to 16.

The two alternative bio-aggregates present high water absorption capacity of250 to 300% by weight after 48 h immersion. Figure 7 evidences distinct initial rate of absorption (IRA) between the particles. After I minute, in the same way as hemp shives, sunflower aggregates already retain more than 150% by weight white corn particles only absorb 100% by weight. This could be a benefit for corn particles at the time of mixing with the minerai binder.

i � i � i � i � 0 ,_. ,_. N N IJJ IJJ � � V, 0 V, 0 V, 0 V, V, 0 � V, 0 (

5) Extractives proportion

In the literature, the extractives from lignocellulosic particles were identified as potential retarding agents for minerai binder hydration. They include different fami lies of extractives as monosaccharides (i.e., free su gars), polysaccharides (Blanes el al. 2004) which have the ability to form strong acid-base bonds with metal hydroxylated species present on minerai surfaces (Las kowski el al. 2007), peptides, phenolic compounds, ter penes, pectin which have the ability to trap Ca 2+ ions in their polymeric strncture, thus contributing to the lack of calcium close to par ticles and modifying the forma tion of hydrates, and organic acids.

In this study, 7 distinct bio-aggregates of three differ ent plant species were characterized. The extractives content ofthese 7 bio-aggregates were measured and are reported in Table 8. The proportions are consistent with values of the literature summarized by [START_REF] Magniont | Chemical composition of bio-aggregates and their interactions with minerai binders[END_REF].

For hemp shives, the content of water-soluble con stituents varies from 5.4 to 15.0%. H 1, H2 and H5 have a proportion about 5 to 7% white H3 and H4 have a higher propo1iion of 15.0 and 10.5%, respectively. Sun- ? ? 0 ? ? ? p ? 0 0 flower bark particle S has similar value as H4. However, corn bark particle has a very high value of extractives, about 23.5%. These proportions are important to note because of their supposed role in the mechanical behav ior ofbio-based materials. This effect was reported in the literature during the hy dration of Po1tland cement in the presence ofwood [START_REF] Na | Wood cement compatibility review[END_REF], hemp [START_REF] Diquélou | Impact of hemp shiv on cement setting and hardening: influence of the extracted components from the aggregates and study of the interfaces with the inorganic matrix[END_REF] or lavender [START_REF] Magniont | Distilled lavender straw potential as a bio-aggregate with a minerai binder[END_REF]). The highest negative effect of extrac tives is observed for H3P by the highest reduction of the heat flow intensity and retarding effect. On the contrary, extractives from H2 exhibit the less negative impact regarding hydration. The deleterious effect can be clas sified at 3 days as H2 < HS <Hl< H4 < H3.

V, V, a, (1) -..J -..J 0) 0) ÏD i � i � i � i � i V, (1) (1) -..J -..J 0) 0) 1.0 1.0 0 V, 0 V, 0 V, 0 V, 0 V, 0 Clrcularlty

Interactions between bio-aggregates extrac tives and pozzolanic binder

The isothermal calorimetty tests results of pastes with 110000 Time(s) sunflower and corn extractives are presented in Fig. 9 and compared to extreme behavior of hemp shives, i.e., H2P and H3P. In comparison with the pastes with hemp extractives, sunflower extractives achieve an intern1edi ary i mpact regarding to the hydration delay. The heat flow intensity related to the reaction [l] is reduced.

-CP -••HlP • ••••• • • • H2P •••••H3P ---
-CP •••••••••H2P •••••H3P -CoP -SP
However, the hydration reaction takes place and sun flower extractives appear to have a lower impact on reaction [2], not implying intensity reduction. On the contrary, pastes based on corn extractives show a dis similar behavior than H2P to H3P. The corn extractives induce the highest impact, reflected by a delay of 27 h and a very important reduction of the heat flow intensity as well as a deep modification of the hydration mecha nisms of the pozzolanic binder since one unique heat flow peak is evidenced.

(2) Mechanical performance of pastes The compressive strengths of pastes containing bio aggregate extractives are presented in Fig. 10. The re sults confirm that filtrate of bio-aggregates significantly influence the strength of binder pastes as previously observed by [START_REF] Sabathier | Incidence of the water-soluble compounds contained into lavender and sunflower bio-aggregates on the hardening process of minerai binders[END_REF] with water-soluble compounds from lavender and sunflower pa11icles.

The effect of extractives can be observed after 3 days with a compressive strength value of 15.2 MPa for con trol CP, white, for the other pastes made with filtrates containing extractives, the strength reduction is ranging from 19 to 93%. Hemp filtrates H2, H4 and H5 as well as sunfl ower one have sin1ilar impact (reduction of20 to 25%). Hl fi ltrate is the most affecting hemp filtrate by reducing the strength of more than 40%, white H3 fi l trate induces an intermediary drop of 34%. Corn filtrate stands out with the strongest negative effect on the binder paste compressive strength at 3 days, the latter reach i ng only 1 MPa.

Until 150 days, the trend follows a similar pattern with the exception of the H3P whose hardening between 14 and 150 days is significantly higher than for all the other pastes. (3) Impact of extractives on the hydration and hardening mechanisms of the model pastes The short tenn and long tenn effects of plant extractives on the mineralogical composition of the mode! pastes were studied by XRD and TGA. TGA curves for control paste and pastes containing plant aggregates extractives after 24 h, 48 h and 9 months of curing are plotted in Fig. 11. 24 h and 48 h after mixing, the water-soluble fraction from the different plant aggregates has a sig nificant impact on the pozzolanic reaction development by reducing reaction products quantity. The first peak, around 120 °C, is attributed to the dehydration of C-S-H and ettringite (denoted as Et), and the second peak, at l80 °C, corresponds to the dehydration of distinct prod ucts of the pozzolanic reaction, including calcium alu mmtum oxide carbonate hydrate, [Ca 4 A' 2 O 6 CO J (H 2 O) 11 ] (denoted as Mc), and calciLm1 aluminium oxide hemi-carbonate hydroxide hydrate, [Ca i Al 2 O 6 (CO 3 )0.5(OH)(H 2 O) 11 . 5 ] (denoted as He) (Frias and Cabrera 2001;[START_REF] Magniont | The benefits of incorporating glycerol carbonate into an i. nnovati.ve pozzolanic matrix[END_REF]. The small peak at 250 °C is attributed to straetlingite, [C 2 ASH 8 ] (denoted as St). A strong delay was also ob served in the consumption of portlandite in pastes based on corn extractives (peak at 430 °C).

In addition, differences are visible in the peak related to calcium carbonates decarbonation (mass loss between 500 and 850 °C) at 24 h and 48 h. In the presence ofbio aggregates extractives (with the exception of Hl and H5) and to a greater extent in that of corn extractives, the formation of calcium carbonates is increased in comparison with the plain paste. TI1e presence of car bonates could be related to the release of carbon dioxide induced by the alkaline degradation of hemp pa11icles, which was responsible for the carbonation of portlandite [START_REF] Govin | Modification of cement hydration at early ages by natural and heated wood[END_REF]. TI1e thermogravimetric analyses also revealed the presence of hydrogarnet, [C 3 AH 6 ] (340 °C), a usual product of the reaction of metakaolin with [Ca(OH) 2 ].

After 9 months, the gap is attenuated between control and hemp extractive pastes. Nevertheless, the content of pozzolanic products in hemp extractives pastes remains Jess than its content in plain paste (with the exception of H5). In the long tenn, pastes containing corn and sun fl owèr present highest peak around 120 °C that could be linked to the formation of delayed ettringite also visible on XRD analyses (Fig. 12).

X.RD analyses were also performed on the different mode! pastes after 24 h, 48 h and 9 months. The com parison of the diffractograms, visible on In order to correlate the characteristics of the bio aggregates and, in particular, their extractives content with the perfonnances of the composites, the mechani cal compression behavior of several plant concretes made of hemp, sunflower and corn aggregates were assessed and presented in Fig. 13. This figure highlights very distinct compressive behavior between, on one hand, the composites containing the three different hemp shives and, on the other hand between the com posites made of sunflower and corn particles. Two types of strain-stress curves are measured. H3C, H5C and SC vegetal concretes reach a maximum stress value after a quasi-elastic region followed by an elastoplastic phase.

On the contrary, H4C and CoC specimens exhibit a con tinuous increase of stress, typical f r om a highly de form able material. Tw o mechanical characteristic parameters were se lected to compare quantitatively the perfonnances of the plant concretes: the stress corresponding to a strain of 1.5% ( a0 = 1.5%) and the elastic modulus (E), their val ues are repo11ed in Table 9. For hemp concretes, the stress corresponding to 1.5% strain and the elastic modulus vary by a factor 6 between H5C and H3C. Considering sunflower concrete, its characteristic strength is 5 times higher than the one of corn concrete.

For the elastic modulus, the ratio is around 3.8. Various parameters could explain these variations, among which the mechanical strength of the binder phase, the adher ence between this minerai matrix and the bio-aggregates, and especially the extractives content of the bio aggregates. In the next part, a discussion is conducted to 1.2 r--------------.====. link the results obtained on bio-aggregates, model pastes and plant concretes.

Discussion

Considering bulk density, dust content, particle size distribution and morphology of the seven distinct bio aggregates studied, a wide variability has been evi denced, both between the five hemp shives and with respect to the two alternative particles of sunflower and corn. The processes oftransfo1mation, separation and/or calibration applied to the plant stalks have a predomi nant impact of these properties in comparison with the plant species.

The water absorption of sunflower par ticle is in the range of those of hemp shives. On the contrary, corn particles present a significantly reduced absorption ca pacity that could be linked to a hydrophobie character of corn bark.

Corn particles also stand out respecting to their ex tractives content, which largely exceeds those of the other bio-aggregates, including hemp shives and sun flower bark particles.

The study achieved on model pastes aimed to identify the mechanisms of interaction between these water soluble compounds and the minerai pozzolanic binder. Extractives from ail the bio-aggregates negatively influ ence the hydration and hardening of the binder to vari ous extents. The hydration and indirectly setting are delayed, the compressive strength is reduced and this could be related to the limited quantity of pozzolanic reaction products evidenced by TGA analyses. We can make a correlation between the compressive strength at 3 days and the second phase [2), attributed to the poz zolanic reaction. The Hl, H3 and H4 having the highest negative effect on hydration at short time induced a lowest compressive strength of pastes. ln the same way, H2 and H5 have a lowest deleterious effect on mechani cal strength.

Focusing on the alternative aggregates, sunflower particles (S) appear to be in the range of hemp shives, white the different analysis clearly demonstrate the higher negative impact of corn aggregates (Co) on the hardening mechanisms of the binder. The S particles have the same hydration (heat flow intensity) behavior as H2 inducing similar mechanical behavior. The high est impact of Co on the heat flow intensity, consistent with no pozzolanic products according to TGA, is well correlated with the large decrease of mechanical per formances observed on the binder including corn extrac tives.

However, the study of hydration by the heat fl ow in tensity does not explain ail the mechanical behavior. In addition, the reduction percentage of strength is not proportional to the reduction of the heat flow intensity. The induced hydration delay exceeds 20 heurs in com parison with the plain paste. This observation is in ac cordance with the mineralogical analyses realized after 24 h and 48 h, which evidence, for the corn paste, a large reduction of pozzolanic products, together with a residual content of portlandite and a fom1ation of addi tional calcium carbonates at early age. This inhibition, in a large extent, of the pozzolanic reaction together with the fixation of calcium ions in carbonate forms induce the reduction of the compressive strength of the paste of about 90%.

Hence, the filtrates containing the extractives showed a negative impact on the hydration and the compressive strength. The extractives content of the bio-aggregates appear as a key parameter of the reduction of the me chanical perfonnances of the mode] pastes. Indeed, Fig. 14 shows the evolution of the hydration delay and of the compressive strength of the pastes as a fonction of the extractives content.

Considering the five hemp shives, Fig. J4A high lights an increase of the hydration delay with the extrac tives content that can be correctly predicted by a linear relation. Neve1theless, this relation is not valid anymore for sunflower and corn aggregates, evidencing the fact that the content but also the nature of the extractives detennines the deleterious effect of plant aggregates on the hydration.

The compressive strength of the mode! pastes al 3 days is also directly impacted by the extractives content of the bio-aggregates (Fig. 148), in particular above a 10% content. Below, the relation is less evident; this cou Id be related to the variable impact of the extractives on the fonnation of calcium carbonates at early age. Indeed, the paste presenting a significantly lower Com pressive stress Sc with only 6.3% of extractives is the HIP, i.e. the paste containing the lower quantity of cal cium carbonates (Fig. 11). The same phenomenon could explain that the compressive strength of the mode! pastes after 150 days does not increase monotonically with the extractives content, since the inhibition of the pozzolanic reaction and the encouraging of the carbona tion process conversely affect its mechanical perfonn ance. This long-term phenomenon observed on binder pastes would probably not occur in the plant concrete. ln fact, in these composites with a very high open po rosity (around 70%) the carbonation process is fast and facilitated.

To verify these explanations, the mechanical per formances of bio-aggregates based concretes are plotted as a function of the extractives content of the considered bio-aggregates in Fig. 15. This figure allows highlight ing that both compressive strength and elastic modulus of hemp concretes are negatively in1pacted by an in creasing extractives content of the shiv. Nevertheless, the water-soluble extractives concentration does not appear to be the only impacting parameter, since, for [f] 0.9 0.8

.. similar extractives content, the sunflower based con crete exhibits lower perfonnances than hemp concrete. It is difficult, however, to conclude on this point, as both composites were not designed with the same binder.

Conclusion

The present paper aimed to study the impact of the wa ter-soluble components extracted from seven distinct bio-aggregates -five hemp shives and two agricultural by-products (corn and sunflower bark particles) as al ternative bio-aggregates for vegetal lightweight concrete -on the hydration and hardening mechanisms of a poz zolanic binder as well as on the mechanical perfonn ances of plant concretes. Beforehand, the bark particles of the seven bio-aggregates were characterized accord ing to the recommendations of RILEM TC 236-BBM [START_REF] Amziane | Recom mendation of RILEM TC 236-BBM: characterisation testing of hemp shiv to detennine the initial water content, water absorption, dry density, particle size distribution and thern1al conductivity[END_REF]. Bulk density, dust content, parti cle size distribution, circularity and water absorption capacity were determined on hemp shives and on bark particles extracted from corn and sunflower stalks. Par ticle size distribution and corn particles could be corrected through a calibration process. Corn particles present a limited water absorption capacity. This could be valorized in association with a clay matrix for example. On the contrary, corn pa11icles stand out with higher extractives content than the six other bio aggregates tested. The results on pastes showed an impact of the extrac tives (water-soluble components extracted from the dif ferent bio-aggregates) on the compressive strength. This phenomenon was explained by their deleterious effect on the hydration and harden.ing mechanisms of lime metakaolin binder explored by XRD, TG A and calo rimetric measurements. Consequently, the paste could be a relatively good mode! to predict the behavior of concretes by following their early age performances: hydration delay and 3-day mechanical strength.

For the same plant species (i.e. hemp shiv), it was ob served that the hydration delay of the mode! paste and the mechanical perfom1ances of the hemp concrete are directly related to the extractives content of the shiv.

Neve11heless, for distinct plant species, the extrac tives content does not appear sufficient to predict the performances of the vegetal lightweight concretes. The nature of the water-soluble extractives or of the products of the bio-aggregates alkaline hydrolysis by the minerai binder would also influence the fi nal mechanical per formance.

Finally, sunflower bark particles appear to be a good candidate for hemp shives substitution since their physi cal and chemical characteristics are close and their im pact on the hydration mechanisms of the pozzolanic binder negligible.

Fig. 1

 1 Fig. 1 Photographs of the five studied hemp shives: H1 (A), H2 (8), H3 (C), H4 (D), H5 (E); corn Co (F); sunflower S (G) bark pa rticles.

Fig. 2

 2 Fig. 2 Cylindrical container for raw density measurement (A), Crush apparatus (B) and Hot extractor (C).

  Fig. 3 Particle size distribution of hemp shives.20

Fig. 5 Fig. 4

 54 Fig. 5 Particle size distribution of corn Co and sunflower S bark particles compared to hemp shives H3 and H4.

  ,_. N N IJJ tu

Fig. 7

 7 Fig. 7 Water absorption curves of bio-aggregates as a function of lime.

Fig. 6

 6 Circularity of corn Co and sunflower S bark particles compared to hemp shives H3 and H4.

  1) Hydration of the model pastesThe isothermal calorimetry tests allowed exploring the impact of plant extractives on the hydration reactions and mechanisms of the binder paste. The results are . 8 and 9. ln Fig.8, the heat flow curves highlight the major impact of hemp shiv extrac tives on binding mechanisms of the pozzolanic matrix. Indeed, a delay is observed with ail kinds of hemp shiv.In addition, the heat flow intensity related to the reac tion is reduced. However, the hydration reaction took place for ail pastes. Tw o phases are noted. The first [ 1] corresponds to the exothermal reaction between potas sium sulfate and calcium ions forming ettringite. The second [2] is attributed to the pozzolanic reaction. Thus, the results indicate an impainnent of the reaction mechanisms, inducing the pozzolanic binder setting.

Fig. 8

 8 Fig. 8 Heat flow by isothermal calorimetry of control paste with distilled water and pastes containing extractives from hemp shives. 1.2E-03

Fig. 9

 9 Fig. 9 Heat flow by isothermal calorimetry of control paste with distilled water and pastes containing extractives from corn particles CoP and sunflower particles SP compared to H2P and H3P.

Fig

  Fig. 10 Compressive strength of pastes at 3, 14 and 150 days.

Fig

  Fig. 11 Differential TGA curves of contrai paste and pastes containing bio-aggregates extractives after 24 h (A), 48 h (B) and 9 months (C).

Fig

  Fig. 13 Typical stress-strain curves of plant based con crete made of hemp, sunflower and corn aggregates.

Fig. 14

 14 Fig.14Evolution of the hydration delay (A) and of the compressive strength (B) of the modei pastes with the extractives content of the bio-aggregates (hemp: blue; sunflower: red; corn: green).

  Fig. 15 Evolution of the compressive stress Sc at strain of 1.5% (A) and the elastic moduius E (B) of bio-aggregates based concretes with the extractives content of the bio-aggregates (hemp: blue; sunflower: red; corn: green).

Table 1

 1 Mixing sequence of paste.

	Step Action		Mixing time (s)	Speed (rpm)
	1	Introduction of the binder and potassium sulfate	30	140
	2	Introduction of the fïltrate or distillated water and superplatizer	30	140
	3	Final mix		30	280
	and the ImageJ "Analyze particles" tool was used to
	determine the hemp particle dimensions and morpho
	logical parameters such as circularity:
	C. l . 1rcu arrty = 1f. 4	Swface are a 2 Perimeter
	(4) Initial water content and absorption

Table 2

 2 Mix design of studied hemp concretes.Table3Mixing sequence of hemp concretes. °C and RH> 95% until the date of the test. The pastes are denoted as HIP, H2P, H3P, H4P and H5P for samples made from Hl, H2, H3, H4 and H5 extractives respectively. The pastes based on corn and sunflower particles are denoted as CoP and SP re spectively. The control paste is referenced as CP.

	Mix	Binder (kg/m )	Aggregates (kg/m )	W/8
	1-IC	286	137	1.2
	Step Action		Mixing time(s)
	1 2 3 4 5	Introduction ofhemp shives with 25% of water and homogenization Introduction of supplementary 25% of water and homogenization Introduction of anhydrous binder and homogenization Continuous introduction of left 50% of water and homogenization Final mix	120 60 180 300 90
	cured in a room at 20

Table 4

 4 reports the mixing sequence applied for the concretes fabrica tion, white Table5summarizes the mix proportioning of sunflower concrete (SC) and corn concrete (CoC).

Table 4

 4 Mixing sequence of sunflower and corn concretes.

	Mixing time (s)

Table 6

 6 Buk densities, dus! content and initial water content of bio-aggregates.

	Aggregate	Bulk densily (kg/m 1 )	SD	Oust content (wto/o)	SD	Water content (wto/o)	SD
	Hl	99.6	3.6
	I-12	154.2	0.6
	I-13	145.9	4.0
	I-14	70.3	2.8
	I-15	108.4	4.4
	Co s	120.2 168.2	1.0 4.5

Table 6 .

 6 Hemp shives have a bulk-density in the range

	1.4	0.2	8.0	0.0
	1.7	0.3	9.0	0.2
	2.0	0.2	6.2	0.0
	0.7	0.2	6.9	0.1
	5.7	0.8	6.9	0.1
	3.4	0.0	10.4	0.2
	0.7	0.1	12.6	0.0

of 70 to 155 kg/m 3 . H3 and H2 have the highest value with 146 and 154 kg/111 3 , respectively, whereas H4 has a low density of 70 kg/m 3 . HI and H5 have similar bulk densities of 100 and 108 kg/m 3 , respectively. The bulk density of the corn bark particles is in the same range with a value of 120 kg/nl. The sunflower bark partiel es exhibit the highest bulk density of 168 kg/m 3 .

Table 7

 7 Equation of the water absorption curves.

	Bio-aggregate Hl H2 1-13 H4 H5	IRA 159.2 166.5 190.0 144.5 155.2	K1 21.91 16.40 15.23 15.54 16.41	R' 0.98 0.99 0.99 0.99 0.99
	Co S	104.8 147.8	18.52 17.78	0.99 0.98
	close relationship with the intrinsic porosity of the bio
	aggregate, white IRA (initial rate of absorption) is
	mainly related to the externat water adsorption on the
	surface of bio-aggregate.

Table 8

 8 Extractive proportions of bio-aggregates.

	Bio-aggregate	Extractive proportions(%)
	Hl	6.3
	1-12	5.4
	1-13	15.1
	1-14	10.5
	1-15	6.8
	Co s	23.5 10.6

Table 9

 9 Stress at strain of 1.5% (Sc) and elastic modulus E of concretes w�h hemp, corn and sunflower bio-aggregates.

	Mix	Sc cr,=1 5% (MPa)	SD	E (MPa)	SD
	H5C	0.50	0.29	114.2	13.7
	H4C	0.08	0.01	18.4	0.7
	H3C	0.08	0.01	19.0	3.0
	CoC	0.03	0.01	14.6	2.7
	SC	0.13	0.02	55.1	1.8
	3.3 Mechanical performances of plant
	aggregates-based concrete

'

.

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