Rémi Louf

Giulia Carra

Hadrien Commenges

Jean-Marie Dembele

Riccardo Gallotti

Maxime Lenormand

Thomas Louail

Marc Barthelemy

Spatial structure and efficiency of commuting in Senegalese cities

Senegal is experiencing an unprecedented urban revolution: according to the latest UN projections, its urban population will be multiplied by 3 in the forthcoming decades, to reach 18 million people by 2050. While cities are often lauded as the solution to mankind's socio-economical and environmental issues, when badly managed, they can also be recipes for disasters. In order to propose well-informed transport and planning policies in Senegal, it is crucial to first measure and understand the key spatial dynamics that shape its cities. In this report, we uncover commuting patterns in the 12 largest urban areas of Senegal and their coarse-grained spatial properties using mobile phone data, allowing us to characterize the efficiency of commuting and to compare cities with each other. At the inter-urban level, we show that for most cities, the vast majority of commuters live and work in the same urban area, meaning that Senegalese cities are well-integrated employment markets. We then compute the geographical area of influence of each city. This confirms the importance of large cities such as Dakar, but also highlights smaller cities which play an important economical role such as Tambacounda or Touba. At the intra-urban level, we quantity the spatial mismatch between residence and workplace locations and we propose a measure of the 'optimality' of the commuting structure. We find that Dakar, with a high optimality index, has a coherent spatial structure with nested residential and employment areas which is reflected in the fact that 80% of the residential and activity hostspots overlap. Smaller cities however-such as Louga, Kolda, Mbour-are far from the optimal commuting solution. The methods proposed in this study could help urban planners in identifying locations and areas which are the most penalized by inefficient commuting, a source of economic loss and stress on people's life and the environment.

Introduction Urban transition in Senegal

Senegal, along with a large part of African countries, experiences a rapid economic growth with an average of 3% GDP growth per year [1]. This economic growth goes hand-in-hand with a dramatic increase of its urban population : while Senegal is estimated to currently host 6 millions people in its cities, the UN estimates that this number should be multiplied by 3 by 2050. It is therefore an understatement to say that Senegalese urban areas are changing fast. The current transition carries a lot of promises in terms of developement; yet, badly managed, it could as well lead to a socio-economic and environmental disaster. In order to make useful investments in transportation infrastructures and propose well-informed planning policies, policy-maker need to understand which areas are going to develop, at what rate, and quantify the ties between different neighbourhoods (at the intra-urban level) and different urban areas (at the inter-urban level).

In general, rapid urbanization goes along with unplanned settlements, health and sanitary problems, as well as congestion problems. In particular, a key in-gredient of sustainable cities is an efficient organization of commuting. Non-pedestrian commuters in Senegal travel almost exclusively by cars, and the longer the commuting, the larger the pollution and CO 2 emissions are. Dakar urban and industrial areas expanded in direction of Thies, and zones of informal settlements appeared. Poor people are pushed toward the periphery which is usually under-equipped and with much lower level of transport infrastructure. It is therefore important to identify areas and trips with large volumes and strong demand for a better, targeted planning. Such information is however usually difficult to obtain and it has been almost a decade now that scientists have realized that geolocated traces passively generated by individuals's ICT devices could revolutionize the quantitative and theoretical understanding of human spatial dynamics, and urban dynamics [START_REF] Ratti | Mobile landscapes: using location data from cell phones for urban analysis[END_REF]. To give a few examples of urban phenomena whose understanding has been enhanced in the recent years through renewed quantitative approaches applied to new sources of ICT data, we can mention the statistical and spatial properties of individuals' mobility in cities [START_REF] González | Understanding individual human mobility patterns[END_REF][START_REF] Roth | Structure of urban movements: polycentric activity and entangled hierarchical flows[END_REF][START_REF] Noulas | A tale of many cities: universal patterns in human urban mobility[END_REF][START_REF] Schneider | Unravelling daily human mobility motifs[END_REF][START_REF] Louail | Uncovering the spatial structure of mobility networks[END_REF]; the universal structure of subway networks and streets networks [START_REF] Roth | A long-time limit of world subway networks[END_REF][START_REF] Louf | A typology of street patterns[END_REF]; the number and the spatial organisation of centers in urban areas [START_REF] Louf | Modeling the polycentric transition of cities[END_REF][START_REF] Louail | From mobile phone data to the spatial structure of cities[END_REF]; the spatial properties of social networks in countries and cities [START_REF] Expert | Uncovering space-independent communities in spatial networks[END_REF][START_REF] Herrera-Yagüe | The origins of searchability in social networks[END_REF]; and the scaling of diverse quantities with the population size of the city [START_REF] Pumain | An evolutionary theory for interpreting urban scaling laws[END_REF][START_REF] Bettencourt | Growth, innovation, scaling, and the pace of life in cities[END_REF][START_REF] Louf | Scaling: lost in the smog[END_REF].

For a few years now, we can investigate these urban questions in much more detail than during previous decades, by analyzing vast amounts of data available at different spatial and temporal scales. Problems that in the past were addressed through surveys by geographers and transport scientists are nowadays addressed by interdisciplinary teams, with many different data sources, data that are more precise both spatially and temporally. Sixty years after its emergence as an academic research field, spatial analysis and quantitative geography may be living their second quantitative and theoretical revolution.

In this paper, we will present an example of such studies, and by using mobile phone data recorded in Senegal, we will show by proposing new measures how we can extract useful information about the spatial structure of urban areas and commuting.

Mobile phone data and urban mobility

Limitations of classic data sources Traditionally, urban planning has relied on travel surveys and censuses. However these data sources have several limitations:

• They require an important logistic, time, and are expensive.

• For these reasons, they are performed over long time intervals, typically every decade or so. With such data it is then impossible to estimate changes of the urban structure over short time scales.

• Transport surveys are often based on samples of a view thousands of individuals only.

In contrast, individual mobile phone data provide anonymized location information about a large fraction of the population with a temporal resolution below 24h, and with a spatial resolution which depends on the density of antennas. For most cities this resolution is of the order of a few hundred meters in the centers, and 1 to 2 kms in the peripheric neighbourhoods. In Senegal the mobile phone penetration rate was estimated to be about 85% in mid-2013 and projections estimated a 110% figure at the end of 2014 [START_REF]NB: a penetration rate value superior to 100 means that there are more active phone numbers than individuals living[END_REF]. It thus allows to monitor population displacements, such as the daily journey to work, important events implying many travels such as Touba's Magal, or long term residential migrations (mostly rural to urban migrations).

Interestingly, a recent study in Madrid and Barcelona -the two largest Spanish urban areas -demonstrated that new sources of mobility data (mobile phone data in the first place, but also geolocated tweets) provide at the city scale a very comparable picture of the commuting structure, when compared to the information obtained with transport surveys [START_REF] Lenormand | Cross-checking different sources of mobility information[END_REF]. This result opens the door to a more systematic use of new sources of ICT data to work on mobility issues in cities.

Previous studies of urban mobility in developping countries with ICT data. For obvious structural and economical reasons, most of the ICT-related studies of the last decade have focused on cities located in rich and developed countries. Few results have focused on cities of other regions of the world and other continents, notably Eastern Europe, South-America and Africa. Several important papers have claimed to uncover universal mobility and urban patterns and propose general models, but are so far limited to a small number of geographical areas [START_REF] González | Understanding individual human mobility patterns[END_REF][START_REF] Bettencourt | Growth, innovation, scaling, and the pace of life in cities[END_REF][START_REF] Simini | A universal model for mobility and migration patterns[END_REF]. Continents and countries have however different urbanization histories, exhibit different spatial properties such as densities and spatial organization [START_REF] Bertaud | The spatial distribution of population in 48 world cities: implications for economies in transition[END_REF][START_REF] Bretagnolle | The organisation of urban systems[END_REF], and the universality of urban patterns is not yet proved to be correct for all regions of the world. To this day, Africa is the less urbanized continent, and is currently experiencing a very fast urban transition [START_REF] Grataloup | [END_REF]. Most large African cities, including Dakar, have no subway network, bike-sharing or car-sharing systems, and their highways systems are much less developped than in US, European or Asian cities of the same size. Apart from the informal collective transport ('bus rapides') and the numerous taxis, public collective transport in Dakar is provided by the municipality bus service ('Dakar Dem Dikk'). It is currently an important issue to obtain a better spatial knowledge of trips in order to develop this service. For these reasons, measuring and comparing the spatial properties and the structure of intra-urban mobility in Senegal is particularly important both for Senegal and Dakar urban planning questions, and also on the scientific side for the elaboration of the emerging science of cities. Such a quantitative knowledge could help to guide planning policies of rapidly urbanizing areas.

The previous edition of Orange's D4D challenge provided communication datasets in Ivory Coast, and allowed for quantitative studies of mobility patterns in Abidjan and Ivory Coast. Numerous interesting studies related to transport and mobility were performed. For example Kung et al. [START_REF] Kung | Exploring universal patterns in human home/work commuting from mobile phone data[END_REF] used the data to test the longlasting hypothesis of a universal, fixed time-budget for daily mobility (often refered as 'Zahavi's law'). They obtained surprisingly large commuting times, and their results questioned the possibility of using individuals mobile phone data to infer travel duration [START_REF] Kung | Exploring universal patterns in human home/work commuting from mobile phone data[END_REF]. Berlingerio et al. [START_REF] Berlingerio | AllAboard: a system for eexploring urban mobility and optimizing public transport using cellphone data[END_REF] developed an interactive and modular application "to optimize the public transport network, with the goal to improve ridership and user satisfaction". They used individuals' travel and activity patterns detected in the data to extract origin-destination (OD) matrices and individuals' travel preferences, to determine optimal design of potential new transit services. A number of studies proposed methods to automatically extract OD matrices from individual data, notably [START_REF] Mamei | Daily commuting in Ivory Coast: development opportunities[END_REF]. Wakita et al. analysed the temporal patterns of communication data and could infer the dominant type of land-use of the geographical area covered by each antenna, and proposed maps of land-use at various scales in the country [START_REF] Wakita | Observation of Human Dynamics in Cote d'Ivoire Through D4D Call Detail Records[END_REF]. Andris and Bettencourt [START_REF] Andris | Information and Social Connectivity in Côte d'Ivoire[END_REF] applied network analysis on the communication network at three different scales (individuals, cities, and the whole urban system), drawed the communication networks upon natural ressource layers and discussed the resulting maps. While providing advices about possible future developments, they didn't characterize the attractiveness of cities, nor their spatial organization and its interplay with mobility patterns of individuals. In addition, none of these 2013 projects proposed a comparison of cities based on the spatial properties of commuting, especially from an efficiency perspective. Such a comparison could help urban planners in identifying cities which are the most penalized by inefficient commuting, a source of economic loss and stress on people's life and the environment.

Objectives of the study and organization of the report.

The objective of our project is to extract from mobile phone data the intra-urban and inter-urban commuting patterns of individuals, discuss these patterns, and to provide a set of quantitative characterizations of the organisation of journey-to-work mobility in Senegalese cities. Another important motivation is to develop coarse-grained indicators summarizing these large amounts of data, able to provide synthetic and large scale pictures of the structure of individual mobility in cities. Such meso-scale information is also particularly useful for validating synthetic results of urban mobility models (such as [START_REF] Eubank | Modelling disease outbreaks in realistic urban social networks[END_REF] for example), for comparing different cities and also for comparing different models. An accurate modeling of mobility is indeed crucial in a large number of applications, including the important case of epidemic spreading which needs to be better understood, especially at the intra-urban level [START_REF] Balcan | Multiscale mobility networks and the spatial spreading of infectious diseases[END_REF][START_REF] Dalziel | Human mobility patterns predict divergent epidemic dynamics among cities[END_REF]. The recent case of the Ebola virus, that didn't spread in Senegal but probably had serious impact on international migrations and touristic activities in the country, provides a contemporary illustration of the societal usefulness of such an understanding.

In this report, we focus on commuting (journey-towork trips) in Senegalese cities, which represents everywhere the largest part of the daily mobility. We have extracted the 12 largest cities from the map of Orange's antennas in Senegal (see Table I), and we have computed the origin-destination (OD) matrices from the twentyfive 2-weeks individual activity datasets (see Methods). We focused successively on two different spatial scales. We first studied the inter-urban case and characterized the relations of each city with the others. We evaluate their geographical area of influence ('attraction basin') and the degree of 'integration' of their labour and housing markets. We then focus on the intra-urban scale, and provide several measures of the spatial organisation of these cities. We first focus on residential and activity hotspots, and discuss their spatial organisation and the shape of the Senegalese cities. We then analyse the structure of commuting in the cities, and propose an original method to characterize the efficiency of the commuting structure, by evaluating the spatial mismatch between residences and workplaces locations. Finally we discuss the results obtained, and the first hints in terms of planning.

Results

The following analysis is based on origin-destination (OD) matrices extracted from the dataset 2 for the largest Senegalese cities. An OD matrix is a classic object in transport planning and mobility studies, and is a n × n matrix F ij where n is the number of spatial units that compose the city at the spatial aggregation level considered. The element F ij represents the number of individuals living in location i and commuting to location j where they have their main, regular activity (work or school for most people). The general idea for extracting the OD matrices from individual mobile phone data is to determine for each individual the pair of locations (i.e. antennas) that are the best proxys for their home and main daily activity locations (see Methods for details). In order to reduce the noise as much as possible with the dataset at hand, the results presented here have been obtained by summing the 25 OD matrices of the dataset 2, each covering a 2-weeks period.

Structure of inter-urban commuting

We first study interactions -in terms of commutingbetween each of the major cities identified in the data, and between each city and the rest of the country.

For a given city i, we can distinguish three types of commuters:

• The internal commuters, who live in i and whose main daily activity is also located in i;

• The divergent commuters, who live in i, and whose main daily activity is based outside i;

• The convergent commuters, who live outside i, and whose main daily activity is based in i;

In the following we denote by N • i , N → i and N ← i the numbers of internal, divergent and convergent commuters, respectively. The total commuting population C of the urban area (for the summed 25 OD matrices) is then given by the total number of internal commuters plus the total number of divergent commuters

C = N o + N → (1)
These three types of commuting are represented schematically on Fig. 1. The values measured for the 12 Senegalese cities we identified are given in Table I, ranked in decreasing order of commuting population size.

In order to estimate the relative importance of the different flows, we define the two following ratios

f 0 = N → + N ← N • f 1 = N ← N →
whose values are also given in the two last columns of Table I. The quantity f 0 characterizes the importance of internal commuters, and f 1 simply compares the number of convergent and divergent commuters.

For most cities studied here, convergent and divergent commuters represent roughly 10% of the internal commute, signifying that Senegalese cities are well-integrated employment markets. The smallest fraction of outcommuting is found in Dakar (3%) and the largest in Tivaoune (31%), which is an important religious center in Senegal, thereby explaining this large value.

The ratio f 1 of the number of divergent and convergent commuters allows us to divide Senegalese cities in three classes:

• Cities for which f 1 > 1 -Diourbel, Louga, Touba, Tambacounda -where the number of ingoing commuters is larger than the number of outgoing commuters. This implies that there are more people present in the city at daytime when compared to nightime.

• Cities with f 1 ≈ 1 -Dakar, Kaolack, Kolda -where the number of outgoing and ingoing commuters is roughly the same, meaning that the total population present in the city is basically the same during day-or nightime.

• Cities with f 1 < 1 -Tivaoune, Thies, Ziguinchor, Saint-Louis -where the number of outgoing commuters is larger than the number of ingoing commuters, also meaning that there are more individuals present in the city during the night than during the day.

This simple indicator f 1 allows to illustrate the balance between jobs and residences. An 'attractive' city (f 1 > 1) for example means that the job/activity market is larger than the residential offer. In contrast, a city with f 1 < 1 is in majority residential. We note however that, surprisingly, there are very few differences between cities, and that the ratios are almost all of order 1, suggesting an equilibrium between the number of people that live outside a city and spend the day in it, and the number of people who live in a city and spend the day outside.

We can further characterize the convergent commuters by measuring the average distance they are traveling. We call this distance the attraction radius r of the city, computed as

r → = 1 N ← n n ( 2
)
where n is the distance traveled by the n convergent commuters. This quantity characterizes the regional influence of cities in terms of commuting and job/activity market. The radius for Senegalese cities are shown on Fig. 2, and their values listed in the Table II. Dakar has a large zone of influence -as expectedhowever there are some surprises such as Tambacounda which is a small city but displays a large attraction radius. It is interesting to note that this large value probably reflects the fact that Tambacounda is an important commercial stop on the road to eastern countries such a Mali and to Casamance, and for stock trading.

We believe that this first study of convergent and divergent commuting should be interesting for planning purposes, but we note that the available data here are too sparse to reach very reliable conclusions. Indeed, when extracting journey-to-work OD matrices (see Methods), we realized that on a 2-weeks period, there is an important proportion of individuals whose mobility is not reg-FIG. 2: Attraction radius for the major Senegalese cities. We represent the radius of attraction computed using the OD matrices that we extracted from the data. The radius for different cities are shown on three different panels for a matter of clarity. ular enough to infer with confidence their home and activity locations (see Methods). To inspect these aspects further, we would need the data provided in dataset 2, but available for a longer period of time for each individual. The dataset 3 could allow to solve this problem, but its spatial resolution is much scarcer, and the small number of individuals per city (Dakar excepted) would raise other difficulties. We thus leave this investigations for further studies and additional datasets. In the following we will focus on what the journey-to-work OD matrices can teach us regarding the intra-urban mobility and the spatial structure of Senegalese cities.

Intra-urban organisation of Senegalese cities

Spatial structure of hotspots A first interesting look on a city's structure is provided by the locations of hotspots, which are local maxima of the density of individuals. Using the extracted OD matrices, we can identify the most important residential and daily activity areas. In order to determine hotspots we used the 'LouBar' method based on the Lorenz curve of the distribution of densities [START_REF] Louail | From mobile phone data to the spatial structure of cities[END_REF]. For most cities the hotspot spatial structure is simple and monocentric. Only for the city of Dakar we have an interesting organization shown on Fig. 3, where we see a polycentric structure as it is observed for other large cities in the world [START_REF] Louf | Modeling the polycentric transition of cities[END_REF].

Work hotspots in Dakar

We show in this figure 3, the map of residential hotspots (i.e. with large night activity) and job/activity hotspots (i.e. daily activity). The overlap between these maps is large which means that important residential areas in Dakar are also the important daily activity centers. Our spatial delimitation of the urban area of Dakar (see Methods) includes major surrounding municipalities (Pikine, Rufisque), and some parts of these municipalities are indeed the most important crowded parts of the urban area. The hotspots of Dakar are not all along the coast but are located in internal crowded neighbourhoods (Liberté, Derklé, Sacré-Coeur, Medina, etc.).

The 'work' hotposts identified in the urban area of Dakar (26% of all the antennas in the city) contain 37% of the total daily population (as identified by our OD matrices). The 'home' hostpots (24% of all the antennas in the city) on the other hand contain 39% of the total population identified by our OD matrices (over the whole year). We note that rich residential neighbourhoods (Les Almadies, Le Plateau), which are also the areas where most of the administration and buildings of the major companies are located, are not residential hotspots. For example one can see on the work hopspots map that some areas of 'Le Plateau' are indeed tagged as employment/daily activity hotspots, but not as residential hotspots.

Surprisingly, home and work hotspots overlap then quite well in the city of Dakar: 80% of the work hostpots are also home hostpots. It gives us a first intuition that in Dakar, the commuting distances could be quite short in average, and that the city seems coherently organized, in the sense that many individuals don't have to travel long distances with cars everyday to travel from their home to their workplace.

Mobility of individuals within cities: hotspots and organization level Once we have extracted the commuting network of individuals we can investigate the spatial properties of this network. We first measure the distance daily traveled by individuals to go from their home to their main activity place. These distances for Dakar and Thies are mapped on Figure 4.

These two figures reveals two typical forms of organization of cities. In the case of Dakar, we observe a polycentric structure with individuals traveling longer distances as they live further from the main activity hotspots (see figure 3), but also with secondary activity centers appearing in the suburbs (Rufisque, Pikine), where many people live and work, resulting in shorter commuting distance on average. Still in Dakar, while several secondary activity centers have developed as the city expanded to Rufisque and Pikine, the historical center remains the most influent and attracts more commuters from these secondary centers than the opposite (see Figure 5). In contrast to this polycentric structure, we observe for the smaller city of Thies a clear monocentric structure with a unique central zone that attracts most daily activity. Consequently, individuals that live in the center commute over short distances, while the further people live from the center, the longer their daily commuting distance is.

An intriguing question is if we can characterize the level of organization of cities from the spatial structure of commuting flows. The first metric that we have is the total commuting distance L. The value of L however does not have a clear interpretation by itself, but we can compare it to reference values. We will then compare L with the two extreme situations:

• A totally disorganized mobility structure, where commuting patterns occur at random, while conserving the static structure of the city given by the numbers of inhabitants and employees attached to each antenna. In this case, we obtain a total commuting distance denoted by L R .

• An optimally (organized) mobility structure, for which mobility patterns are such that the total commuting distance in the city is minimum (while conserving the number of inhabitants and employees attached to each antenna). Given the constraints of numbers of inhabitants and employees, this leads to a minimum total commuting distance denoted by L O .

In order to compute the values of L R and L O for each city, we calculate the corresponding OD matrices of the two extreme cases, optimal and random. The OD matrix for the disorganized state is calculated by addind flows at random, making sure to respect the in-and out-degree of each node in the network (see Methods). The distance L R is averaged over 100 random realisations. The OD matrix for the optimal state is obtained by simulated annealing, a local search optimisation method [START_REF] Mézard | Spin glass theory and beyond[END_REF]. Once we have calculated the two quantities L R and L O we can then define an organization index of the city

O = L -L R L O -L R (3)
This index O is equal to 0 when the mobility patterns are completely disorganised (L = L R ) and equal to 1 when the mobility patterns are completely organised (L = L O ). This measure is indeed a measure of the spatial mismatch in the city: the larger the value, the more organized the city is, and individuals in this case live very close to their activity location. We give in Table III the values of L/L O , L/L R and O for the twelve Senegalese cities identified by our urban areas detection method. We observe in this table that for most cities the ratio L/L R is small and approximately constant (on average equal to 0.25), the ratio L/L O displays larger variation: on average we obtain 3.8 and extremes such as 8.98 (Tivaoune) and 1.92 (Kaolack). Although we would need the corresponding values of cities in other parts of the world, these numbers suggest that there is probably some room for improving the commuting figures in many cities in Senegal. In particular, in the case of Dakar, we obtain the values indicated in Table IV (as a reference we also give the typical distance of the city, taken as the square root of its surface √ A). It is interesting to note that in the largest city of the country, the individuals journey-to-work mobility is rather short, and closer to the optimal situation than to the random, disorganized situation. This result suggests that there is indeed a good match between locations where people live and the ones where they perform their daily activity. It would be interesting to calculate these values for cities in European and US countries for which mobile phone datasets have been used in many papers in the recent years (France, Spain, Portugal, etc.).

Discussion

We extracted the OD matrices for the 12 largest Senegalese urban areas from mobile phone data and proposed several measures that can help in characterizing the spatial structure of commuting and its efficiency, and to compare different cities with each other. At the interurban level, we could show that Senegalese cities display a good integration of labor and housing markets. In addition, the attraction radius of cities allowed us to identify important large cities and also important economical nodes. Cities with the largest attraction radius -such as Dakar, Tambacounda, Touba, Saint-Louis and Ziguinchor -would then naturally benefit from transport infrastructure improvements at the country scale, linking cities.

At the intra-urban level, we characterized the efficiency of the spatial organization of residential and working areas in terms of commuting. In particular, we showed that for Dakar, commuting distances can be very short and that the city seems to be coherently organized in this respect. However for other cities such Louga, Kolda, Mbour, it seems that we are far from the optimal commuting solution. One could have naively expected that the larger the city, the more 'anarchic' it becomes, but our results proove that this naive representation is wrong. These preliminary conclusions would benefit from further investigations, in order to understand the origin of the spatial mismatch for these cities.

Our results show that mobile phone data can effectively be used to characterize how well an urban area is organized. In this respect, they can help in identifying the more fragile urban areas that deserve a particular attention for future intra-urban transport planning.

Material and Methods

Delimitation and selection of cities.

When one wants to compare cities, an important issue is to rely on a common, reasonable spatial definition/delimitation applied to all cities [START_REF] Arcaute | Constructing cities, deconstructing scaling laws[END_REF][START_REF] Bretagnolle | Time and space scales for measuring urban growth[END_REF]. For example, Dakar as a geographical object cannot be restricted to the municipality of Dakar, both in terms of morphology and function. The spatial layers provided in the project include larger spatial delimitations corresponding to administrative entities. Since we didn't find any documentation explaining the territorial criterion chosen -if any -to construct these spatial objects, we cannot assume that such spatial delimitations are suitable to properly define Senegalese cities.

For this reason, we conceived a method to delimitate cities using the spatial points pattern of Orange's mobile phone antennas. We proposed a simple density-based clustering method based on the hypothesis that the density of antennas reflects the density of population. For each antenna we count the number of its neighbors within a growing distance threshold ranging from 0 up to 20 km, with a fixed step of 0.5 km. We then classify those quasi-linear distributions (see Figure 6) and distinguish between Dakar's antennas with a steep slope, Touba's and other cities with a less steep slope, and non-urban antennas with a slight slope. Our delimitation includes urban cores but also peripheric neighbordhood (see Figure 6).

We compare the criterion used to satellite pictures of the corresponding cities [41] and to the the Open-StreetMap boundaries of each city, and find that the definition captures well the built areas of each city (see Figure 7

Extraction of the Origin-Destination matrices

In the following, we call commuters the people identified by our algorithm, and commutes the trips as they appear in the resulting Origin-Destination matrix.

The reference files to calculate the OD matrices are SET 2 P XX.CSV of Dataset 2, where XX varies from 01 to 25. The output of the method is a m × n matrix where C ij is the number of commuters that live in place i and whose main daily activity is located in j. In the following we call 'Home' the residence cell of the user and 'Work' the cell of its main daily activity place.

For each user u, the extraction procedure is the following : for each hour of the two weeks period -weekends excepted -during which the user used her phone at least once, we identify the most visited cell/antenna during this hour. This cell/antenna is the one from which the user has given/received the most calls/sms during this particular hour. Hours are partitionned in two groups:

(1) the daily hours that are spent at work/school for most people during weekdays (hours between min W and max W ) ; (2) the late evening, night and early morning hours, spent at home for most people (hours between min H and max H ). For both groups of hours, we identify the cell to which the user has been 'attached' the greatest number of hours. We then calculate the proportion of time spent in the cell (number of hours / total number of hours during which the user called). Finally, if in both cases these proportions are greater than a parameter prop, then the two cells are tagged as the user's work/home cell and the user's home cell. Otherwise the user is not selected because her locations don't show enough regularity to assume than the two most frequent antennas are resp. her workplace and home.

Once we have applied the extraction procedure to all users we end up with an OD matrix of commuting flows for the whole country, for each two weeks period of the dataset 2.

Sensitivity analysis We analyzed the influence of the value of prop on the number of users selected, and also on the proportions of intra-cell flows (i.e. the proportion of individuals who have the same Home and Work cell) for the file SET 2 P 01.CSV , by using the following parameter values:

• min W = 8 • max W = 17 • min H = 19 • max H = 7
In the figure 8 we can see that the number of users decreases when prop increases, which is an expected effect. However the proportion of intra-cell flows tends to sharply increase when prop increases. In order not to remove users and try to keep the network structure we choosed to fix prop to 1/3. q q q q q q q q q q 0.0 0.2 0.4 0.6 0.8 ) q q q q q q q q q q 0.0 0.2 0. In the following, we detail the methods used to obtain the random origin-destination matrices-corresponding to a situation where people would choose their working location at random from the existing possibilities-and the optimal origin-detination matrices-corresponding to a situation where people would be assigned to a working location so that the total distance commuted at the city level is optimal. What we are interested in here is the optimality of the commuting patterns given the existing spatial distribution of homes and jobs. We therefore constrain the random and optimal OD matrices in such a way that the number of homes and jobs at each antenna are identical to those observed in data. If we intepret the OD matrix as representing a network between homes and jobs in different locations, then both our random and optimal null model preserve the in-and out-degree sequences.

To generate a random graph that conserves the in-and out-degree of each node of the reference graph, we use the Molloy-Reed algorithm [START_REF] Molloy | A critical point for random graphs with a given degree sequence[END_REF] which complexity is in O(n), where n is the sum of the weights of the edges (i.e. the number of individuals in the OD case).

In order to generate the optimal network, we use simulated annealing, a probabilistic method for global optimization problems [START_REF] Mézard | Spin glass theory and beyond[END_REF]. At each step, we invert 2 OD pairs (a, b) and (c, d) to (a, d) and (c, b) (so that the conservation of in-and out-degrees is guaranteed). The move is accepted with probability 1 if the proposed solution is better than the previous one, i.e. if the total commuting distance is smaller than that of the previous situation. If the new situation is worse, however, the move can still be accepted with a probability that depends on temperature T as

P (1 → 2 = exp( L 1 -l 2 T ) (4)
and the temperature is decreased as the search progresses. This trick allows to avoid getting stuck in local minima.

Using calls data to estimate population movements

Framework

We define the activity Ãi of an antenna i as the total number of calls and text messages sent and received from i over a time window τ . We call τ the resolutionthe minimal resolution is 1 hour, imposed by the dataset (D1). In other words, we are theoretically able to follow the change of activity in the city at the one-hour level.

Ãτ (i) =

τ A(i, x) dx [START_REF] Ratti | Mobile landscapes: using location data from cell phones for urban analysis[END_REF] where A(i, x) is the measure of activity (in-call, outcalls, number of users) for the antenna i at the hour interval x. Then we define the mass of the antenna as

M τ (i) = Ãτ (i) i∈N Ãτ (j) (6)
Depending on the area of study N and the timewindow τ we might be able to catch different phenomena. For instance, setting N to be a city, τ to be of the order of an hour, one can identify the patterns of daily activity in cities. Setting N to be an entire country and τ to be of the order of a week, a month... one can possibly identify internal migrations.

Difference between day and night activity

We first start with comparison between day and night activities in the city. Census traditionally give information on the residential population in cities, sometimes also on the working population. Mobile phone data allow us to get information with better time resolution and to follow the locations of people within the city during the day. Such information is important to have, for instance for emergency evacuation plans, for understanding epidemic spreading over short time-scales, etc. For each antenna i we plot the quantity

DN (i) =
M day (i) -M night (i) M night [START_REF] Roth | Structure of urban movements: polycentric activity and entangled hierarchical flows[END_REF] which represents the relative difference of activity of the antenna between night and day. With other data to calibrate the relation between mass of antennas and populations, one should be able to estimate the relative differences in population from these differences in antenna mass. Even without calibration, the differences in the mass of antennas give a good idea of the changes in the spatial location of populations over time.

FIG. 3 :

 3 FIG.3: Location of residential and activity hotspots in Dakar, determined with the method described in[START_REF] Louail | From mobile phone data to the spatial structure of cities[END_REF].

FIG. 4 :FIG. 5 :

 45 FIG. 4: Average commuting distance at the antenna level (Left) Dakar: Average outreach per antenna, i.e. average distance commuted by people living in the area covered by this antenna. When calculating the average we do not take into account the people who are flagged as living and working at the same place. (Right) Same measure for the urban area of Thies. These results are obtained by averaging the OD matrices over the whole year.

FIG. 6 :FIG. 7 :

 67 FIG.6: Illustration of the method used to delimitate cities. For each antenna of the dataset, we count its number of neighbors in a circle of increasing radius (x-axis of the top figure). Each curve represent an antenna. We then apply a hierarchical clustering method on the resulting set of vectors, and represent the average profile of each class on the bottom figure.

FIG. 8 :

 8 FIG. 8: Sensitivity of (a) the number of individuals selected and (b) the proportion of intra-antenna flows, to the parameter prop of the OD matrix extraction method. These tests have been performed on the dataset SET2 P01.

FIG. 9 :

 9 FIG. 9: Relative difference of antenna mass between night and day in Dakar. Each Voronoi cell is colored with respect to the valueM day (i)-M night (i)M day calculated for the corresponding antenna i.

TABLE I :

 I

Partition of commuting flows into internal, divergent and convergent commuting flows, for the 12 largest urban areas identified in Senegal (ordered by total commuting population).

TABLE II :

 II Attraction radius r ← . Cities are ordered by total number of commuters, showing that there is no clear correlation between the attraction radius and the total number of commuters.

		r ← (km)
	Dakar	137
	Thies	60
	Touba	109
	Mbour	53
	Kaolack	77
	Saint-Louis	91
	Ziguinchor	91
	Tambacounda	141
	Tivaouane	72
	Diourbel	69
	Louga	69
	Kolda	98

TABLE IV :

 IV

Comparison of the average commuting length as measured on the data ( ), on the optimal OD matrix ( O ), the random OD matrix ( R), and the typical size of the city ( √ A).

Acknowledgements

MB is supported by the European Commission though the FET-Proactive project PLEXMATH (Grant No. 317614), and the project EUNOIA (FP7-DG.Connect-318367). TL is supported by the EU commission through project EUNOIA (FP7-DG.Connect-318367). ML acknowledges funding from the PD/004/2013 project, from the Conselleria de Educacin, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017. RG is supported by the European Commission FET-Proactive project PLEX-MATH (Grant No. 317614)

Authors contributions

TL, RL, GC, RG, HC, ML, J-MB and MB designed the study; RL, TL and MB coordinated the study. RL, ML, HC, GC, TL and RG processed and analysed the data. RL, GC, HC and ML made the figures. TL, RL, and MB wrote the paper. All authors read, commented and validated the final version of the manuscript.


Florence D Hulot
email: florence.hulot@universite-paris-saclay.fr

S Msiteli-Shumba

Mathias Iung

Camille Noûs

Elisa Thébault

Spatio-temporal beta diversity of plankton species and their interactions in 1 permanent and temporal waterholes in a semi-arid savannah 2 3

Keywords:

Freshwater communities can vary greatly across space and time. Studying these variations, 2 that is spatial and temporal beta diversity, provides fundamental information on the processes 3 that maintain diversity and on the consequences of environmental changes on communities.

4

Recently, drying events have been shown to strongly affect the spatial and temporal beta 5 diversity of temperate freshwater ecosystems, but the effects of such events are mostly 6 unknown for freshwater communities in semi-arid climates that are frequently submitted to 7 drying up. In addition, studies have so far focused on variations in species composition that is 8 species beta diversity, while variations in species trophic interactions that is food-web beta 9 diversity can give additional insights on how community functioning vary in space and time.

10 Here, we combine species and food-web perspectives to explore the spatiotemporal beta 11 diversity of plankton species and their trophic interactions in waterholes undergoing different 12 water-level regimes: either an alternation between dry and water-full states, or a permanent 13 water-full state due to water pumping. Our results show that waterholes with artificial water 14 pumping do not differ from natural waterholes in their contribution to spatiotemporal beta 15 diversity. Instead, beta diversity is strongly driven by temporal variations of species 16 composition and food-web structure during the dry season, which is characterized by 17 degraded planktonic communities with a low richness, diversity and connectance. Species-18 and interaction-based approaches give complementary information on the spatiotemporal beta 19 diversity, as they highlight different planktonic communities with contrasted functioning.

Introduction 1

The study of the variations of species composition across space and time (i.e. beta diversity) is 2 of major importance to explain diversity patterns on earth and to provide fundamental 3 information on the processes that maintain this diversity (Whittaker 1972). Planktonic 1 Such alternation between dry and water-full states is expected to strongly affect both spatial 2 and temporal beta diversity. Repeated drying events might decrease spatial beta diversity as a 3 result of increasing environmental filtering in harsh conditions (Chase 2007) but it could also 4 increase spatial beta diversity if it leads to greater habitat heterogeneity (Sarremejane et al. species interaction diversity may be explored at the local level (which species interactions 15 occur in a given place at a given time) but also at the spatial regional level (which species 16 interactions are common between communities or specific to some communities) and at the Artificial waterholes are then expected to contribute less than natural and temporal waterholes 23 to overall beta diversity. We also expect that temporal beta diversity plays a significant part of 24 overall beta diversity in this highly seasonal system. Finally, we expect that interaction beta diversity will give a complementary perspective to species beta diversity. While both are 1 expected to be related with species diversity because species poor communities often tend to 2 be more dissimilar (Chase and Myers 2011; da Silva Brito et al. 2020), food web complexity 3 is also expected to matter for interaction beta diversity.

16

The waterholes investigated in this study are located in HNP and the adjacent Sikumi Forest 17 and communal areas situated in the periphery of the park (Figure 1). The HNP is devoted to during the dry season. Please note that some artificial waterholes were also sampled 13 occasionally as some of the pumps may have been out of order causing the artificial 14 waterholes to dry up. In short, 28 waterholes were sampled in February and March, 26 in April, 21 in July, 14 in September, 12 in November and December.

16

Samples were collected with the use of a 1L polyethene bottle fastened to a 3 m pole. Water 17 samples were taken between 8:00 and 12:00 to minimize variability in the sampling time of 18 the day. About 10L of water was filtrated first through a 100 µm filter and then through a 30 µm filter. Samples were collected in 50 ml falcon tubes and preserved in 4% formalin. We quantified species beta diversity following the method of Legendre and Caceres (2013).

2

Because the waterholes were not all sampled at all dates, we could not compare the total beta 3 diversity among waterholes only, or among dates only. Thus the total beta diversity was 4 computed over all waterholes and sampling dates, and it was expressed as the variance of the 5 Hellinger transformed species-by-sampling unit matrix. Each sampling unit corresponds here 6 to a waterhole-month combination. We then calculated the contribution of each sampling unit The first step to analyze food-web beta diversity is to describe all the potential trophic 19 interactions among all the observed species in the waterholes, which constitutes the meta-food 20 web. To build the corresponding prey-predator interaction matrix, we looked into the 21 literature for data on the predation of the zooplankton species on phytoplankton and 22 potentially other zooplankton species. Not all species could be found in the literature.

23

According to personal expertise, allometric relationships between planktonic prey and predators (Brooks and Dodson 1965; Burns 1968) and species taxonomy (Gauzens et al.

1 2013), we hypothesized that species of the same size and belonging to the same genus have 2 the same diet. With this information, we were able to construct the meta-food web matrix 3 where all species observed in the waterholes are in lines and columns. The predation of the 4 species in the line by the species in the column was coded by a "1". The absence of predation 5 was represented by a "0". In the matrix, except for copepod nauplii, species with ontogenetic 6 omnivory appear in the matrix only once in the column with the multiple prey that they eat 7 during their lifetime, even though the species may be prey as a juvenile and a predator as an 8 adult. Therefore, cannibalism is represented by a "1" at the intersection of the same species in 9 the column and line. Copepod nauplii was represented in the matrix as a tropho-species. We 10 added in the matrix the groups "bacteria", "small phytoplankton" (phytoplankton <30 µm), 11 "protists" and "POM and DOM" (particulate and dissolved organic matter) as some species 12 fed on these groups. Small phytoplankton, protists and POM and DOM are considered to be 13 basal species. These groups are essential in the network construction. Otherwise small filter-14 feeders such as small rotifers and nauplii would be at the same trophic level than primary 15 producers and the diet of generalist filter-feeders such as cladocerans would be narrowed.

16

Once the meta-food web was built, we extracted local food webs for each waterhole at each sampling month resulting in 141 food webs.

Calculating interaction beta diversity 20

To follow the same approach as detailed above for beta diversity on community composition 21 but on food webs, we adapted the method of Legendre and Caceres (2013) to food web data.

22

First, we calculated food web based LCBD values (hereafter LCBDFW) using the interaction-23 by-sampling unit matrix instead of the species-by-sampling unit matrix as used previously (Figure S1, Appendix 2). Because we do not have data on interaction strengths but only on 1 interaction presence or absence, the LCBDFW values were derived from the Jaccard 2 dissimilarities in interactions among all pairs of sampling units. More specifically, the food Second, LCBDFW values were tested for significance against the null hypothesis of random 8 species distributions among waterholes and months (i.e. same null hypothesis as for LCBDSp 9 values). To do so, we built 999 permutations of the species-by-sampling unit matrix 10 (Legendre and De Caceres 2013), on which we derived 999 random interaction-by-sampling 11 unit matrices. Waterhole-month combinations with significant LCBDFW values correspond to 12 sampling observations with trophic interactions that differ from food webs in other months 13 and waterholes.

14 Third, we also proposed a measure of Species contribution to beta diversity for food webs 15 (SCBDFW) adapted from the approach of Legendre and Caceres (2013). We first suggest a 16 calculation of the contribution of each interaction of the meta-food web to overall beta 17 diversity following the same philosophy as Legendre and Caceres (2013) but using the 18 Jaccard dissimilarity instead of the variance of the composition matrix as previously defined.

19

Then we defined the SCBDFWi of species i as half of the sum of the contributions to beta 20 diversity of the interactions in which species i participates as prey or predator. By doing so, 21 the sum of the SCBDFW values is equal to 1 as required. The precise definition and calculation 22 of SCBDFW is detailed in Appendix 2. Please note that by construction species that are not 23 connected in the meta-food web (i.e. inedible basal phytoplankton species) do not contribute 24 to food web beta diversity because the beta diversity calculation is based on the interaction-1 by-sampling unit matrix. Species and trophic groups were first described by their frequency of occurrence in the 5 sampling units. The frequency of occurrence was defined by the number of sampling units in 6 which a given species occurs divided by the total number of sampling units. In addition, 7 species were characterized by their positions in the meta-food web. Species degree 8 corresponds to the number of interactions of a species in the meta-food web (i.e. total number 9 of potential prey and predators). Species trophic level is defined by the average trophic 10 position of its prey plus 1.

11

We also characterized the local diversity and food web structure of each waterhole at each 12 sampled month by the following measures: (i) total species richness; (ii) Shannon diversity as S1). We tested the additive effects of 22 waterhole management (i.e. pump presence), sampling month and location (i.e. HNP, forest or communal area) on LCBDSp and LCBDFW using a mixed linear model with the waterhole as a 1 random effect using the lmer function of the lmerTest package (Kuznetsova et al. 2017). We 2 then selected the more parsimonious model using the Akaike information criteria. We also 3 tested the effects of species diversity, Shannon diversity, average trophic level and 4 connectance on LCBDSp and LCBDFW using a mixed linear model with the waterhole and the 5 sampling month as random effects. Finally, we tested the effects of the species occurrence 6 frequency, the species degree and its trophic level in the meta-food web on SCBDSp and 7 SCBDFW with a linear model. has no effect but the sampling month and, marginally, the location affect the LCBDSp (Table 3 1): the LCBDSp values are significantly higher in November (p<0.001) and marginally higher and Camphylodiscus sp. We note that out of these 20 taxa, only three are herbivorous 16 consumers (nauplii, P. vulgaris and B. calyciflorus), the others are primary producers. Among 17 the phytoplankton taxa, we find mostly Bacillariophyta and Cyanobacteria. Only the sampling month has a significant effect on the LCBDFW (Table 1). The LCBDFW are 3 higher in November (p =0.008) and September (p =0.04) and marginally higher in February (p 4 =0.05).

5

The analysis of the SCBDFW reveals that the two species that have the higher SCBDFW are 6 two copepods, Macrocyclops varicans and Eudiaptomus gracilis (Figure 3B, grey triangles). 2). The local contribution of a waterhole to beta diversity is thus higher when the average 8 trophic level of the planktonic food-web it hosts is high and the species abundances are 9 unbalanced. The LCBDFW decreases significantly with species richness and food-web 10 connectance (Figure 4A and Table 2). Put another way, the more dissimilar waterholes from 11 the interaction perspective are waterholes with few species and simple planktonic food-webs.

12

The SCBDSp increases significantly with species occurrence frequency and decreases 13 significantly with species degree in the meta-food web (Figure 5). In other words, species that 14 have high SCBDSp are also widespread species, even if they might be in small abundances in numerous waterholes, and have a low number of connections in the meta-food web. As for the 16 SCBDFW, it increases significantly with species occurrence frequency, and with species 17 degree and trophic level in the meta-food web (Figure 5). In contrast with the SCBDSp,

Discussion

Contrary to our first expectation, waterholes with artificial water pumping did not contribute 1 less to spatiotemporal beta diversity than natural waterholes. Instead, the contribution of 2 waterholes to species and interaction beta diversity was mostly related to monthly changes in 3 their community composition. Species-and interaction-based approaches also gave 4 complementary information on the spatiotemporal beta diversity of this freshwater ecosystem.

5

While inedible phytoplankton species contributed importantly to species beta diversity, highly 6 connected species in the food web contributed the most to interaction beta diversity. We 7 discuss these different results in detail below. Although we expect to find a similar importance of temporal beta diversity in driving the beta 3 diversity of waterholes in other years than the one considered here, we should acknowledge 4 that our study was restricted to seven months and that the interannual variability should be 5 addressed in future studies. In particular, the drought intensity changes from one year to the 6 other and affects water level changes in ponds and, as a consequence, may affect planktonic 7 community dynamics. Considering this study system over several years would allow to 8 investigate how spatiotemporal beta diversity is affected by severe events such as prolonged 9 droughts, and whether differences between natural and artificial waterholes occur in this 10 context.

11

The degree of uniqueness of the waterholes a given month depends on both diversity and food 15 Indeed, LCBDSp increases with food-web average trophic level while LCBDFW decreases with 16 connectance. This is also true for the SCBD values: while species contribution to food-web 17 beta diversity increases with species degree and trophic level, species contribution to species-18 level beta diversity is lower for more connected species. These differences arise from the fact 19 that by definition species degree matters to the calculation of food-web beta diversity, 20 inedible basal species being necessarily excluded. In addition, our measure of species beta 21 diversity includes species abundance, which is not the case for our measure of food-web beta Waterholes with high LCBDSp and low LCBDFW can be characterized by either planktonic 14 food-webs that are dominated by inedible or poorly connected phytoplankton species or by 15 food-webs with relatively high species abundance at higher trophic levels (e.g. Makwa in 16 March). In the former case, the primary production is diverted to dead-end or does not support 17 efficiently a rich food-web whereas it is the contrary in the latter case. Meanwhile, waterholes 18 with high LCBDFW are generally characterized by the lack of common and highly generalist 19 top consumers, which might play an important functional role in these ecosystems.

20

In conclusion, our analysis reveals that the beta diversity of plankton communities in the 21 waterholes of a semi-arid savanna is mainly driven by strong temporal variations of species

0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 1 0 1 1 1 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 Afrocyclops gibsoni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Supplementary Material -for review MicroPhyto 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 Anabaena sp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ankistrodesmus spiralis 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 Ankistrodesmus spirilliformis 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 0 1 1 1 0 0 1 0 1 0 1 0 0 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 Arthrodesmus sp. 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 Bacteria 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1

457

  communities and their dynamics have been widely studied in isolated freshwater ecosystems.However these ecosystems are connected by biological processes such as individual active 6 and passive dispersal (Kappes et al. 2014; Incagnone et al. 2015; Juracka et al. 2016; Slusarczyk et al. 2017), and as for other types of ecosystems, their diversity depends both on 8 stochastic processes related with colonization and extinction dynamics, and on deterministic 9 processes associated with differences in species niches to environmental conditions (Chase 10 2007). While the variations of communities across space (i.e. spatial beta diversity) have been 11 generally studied separately from the variations across time (i.e. temporal beta diversity), 12 recent studies have emphasized the importance of considering spatial and temporal 13 dimensions at the same time (Cook et al. 2018; Musters et al. 2019; Crabot et al. 2020; 14 Sarremejane et al. 2020). Indeed, the spatial and temporal beta diversity of freshwater ecosystems are together affected by environmental perturbations such as drying events or 16 eutrophication. For instance, Sarremejane et al. (2020) showed that the temporal beta diversity 17 of aquatic invertebrates in streams in UK increased with longer drought events while the 18 spatial beta diversity decreased as flow permanence increased. 19 So far, the study of spatiotemporal variations in freshwater communities has been mainly 20 restricted to a few temperate stream ecosystems. Aquatic ecosystems in semi-arid areas 21 display a wide range of dynamics from temporary to permanent systems, which may vary 22 from year to year. The alternation between completely dry state and water-full state is a strong 23 selective pressure on aquatic species, which have consequences on aquatic systems 24 functioning (Thomas et al. 2000; Coops et al. 2003; Thomaz et al. 2006; Bazzanti et al. 2009; Zohary and Ostrovsky 2011; Michaloudi et al. 2012; Teferi et al. 2014; Medeiros et al. 2015).

5

  2020). Meanwhile, strong seasonal changes in water level may lead to very dissimilar species 6 assemblages at different times of the year, and thus to high temporal beta diversity, as is 7 expected in highly seasonal and predictable environments (Tonkin et al. 2017) . Our 8 understanding of the diversity of planktonic communities in semi-arid aquatic ecosystems 9 should thus gain from a spatiotemporal perspective of beta diversity. 10 The study of beta diversity has been classically considered from a species-based taxonomical 11 point of view, but other aspects of biodiversity such as functional trait diversity or diversity of 12 species interactions in ecological networks can offer complementary perspectives on this issue 13 (Pellissier et al. 2018; Crabot et al. 2019; Ohlmann et al. 2019). As for species diversity,14

  17 temporal level (which species interactions persist or vanish in time) (Poisot et al. 2012; 18 Pellissier et al. 2018). Because the changes in abundance and occurrence of species across 19 space and time can depend on species traits and/or trophic groups, the drivers of beta diversity 20 can differ depending on the diversity metrics considered (Pellissier et al. 2018; Crabot et al. 21 2019). By building on recent developments on the study of the beta diversity of ecological 22 networks, linking species-based and network-based approaches should allow to better 23 characterize how the functioning of planktonic food webs in freshwater ecosystems vary 24 across time and space. Likewise, species interaction beta diversity assessed in a set of aquatic 25 that distinguish waterholes? We hypothesize that artificial pumping decreases both spatial and 20 temporal beta diversity as it might homogenize environmental conditions among artificial 21 waterholes and decrease seasonal variations in environmental conditions in these waterholes.

  22

7

  Hwange National Park (HNP) covers an area of about 14 651 km² and is situated in 8 Matabeleleland North Province, Western Zimbabwe. It is located in a semi-arid area where the 9 rainfall is highly variable with an average of 650 mm and a coefficient of variation of 25% 10 (Arraut et al. 2018). The vegetation in most of the park, especially in the area where this study 11 was carried out, is typical of a highly heterogeneous dystrophic wooded savanna, dominated by 12 mixed woodlands and bushlands on Kalahari sands, with open grasslands along drainage lines 13 (Chamaillé-Jammes, Fritz, et al. 2007). The rainy season lasts from December to April. The 14 cool dry winter months extend from May to July and the hot dry months from August to 15 November.

18

  wildlife conservation and there are no inhabitants. Domestic and wildlife species coexist in the 19 forest reserve and in the communal area but the latter is inhabited. Numerous depressions are 20 fed by rainwater runoff during the rainy season and, according to their size and depth, these 21 natural waterholes may dry out completely during the course of the dry season. To supply 22 wildlife with water during the dry season, underground water is pumped to maintain water in 23 some permanent artificial waterholes. Natural and artificial waterholes are found in the park 1 and forestry areas, while only natural waterholes are found in the communal area. Rainfalls may 2 be restricted to some areas, especially in the beginning and end of the rainy season, leading to 3 heterogeneity in waterholes filling and drying up.

4 5Field sampling and laboratory analyses 6

 46 Thirty waterholes were sampled between February and December 2013 over 7 months, 7 covering the wet, cool dry and hot dry seasons. The waterholes were selected to represent a 8 set of artificial and natural waterholes, located inside and outside the park. These comprised 5 9 waterholes in the communal area, 5 waterholes in the Sikumi Forest, and 20 waterholes in the 10 Hwange Main Camp area in HNP. All waterholes could not be sampled at each sampling date 11 because of impossible access during the wet season or because the waterholes had dried up 12

20

  Taxonomic identification was carried out under an Olympus CK40 inverted microscope with 21 the assistance of taxonomic keys (Msiteli-Shumba et al. 2017). All plankton species were 22 identified to the lowest taxonomic level possible. 23 Species beta diversity 1

7

  to beta diversity (hereafter LCBDSp) following Legendre and Caceres (2013). The LCBDSp 8 values represent the uniqueness of the sampling units, i.e. the waterhole a given month, in 9 terms of species composition. LCBDSp values were tested for significance against the null 10 hypothesis of random species distributions among waterholes and months. We also computed 11 the Species Contributions to Beta Diversity (hereafter SCBDSp) to identify the species that 12 contribute the most to species beta diversity. For instance, species with high abundances in 13 only a few sites have high SCBDSp values. The LCBDSp and SCBDSp indices were computed 14 using the beta.div function of the adespatial package in R (Dray et al. 2019).

3 4 (

 4 web dissimilarity Dbi between sampling units b and i is defined by 𝐷 𝑏𝑖 = 𝐵+𝐶 𝐴+𝐵+𝐶 where B resp. C) corresponds to the number of interactions only present in the food web of sampling 5 unit b (resp. i), while A corresponds to the number of interactions present in the food webs of 6 both sampling units b and i.

  7

2 3

 2 Describing local communities and species positions in the meta-food web 4

13a

  function of species abundances; (iii) connectance as the number of interactions in the food 14 web divided by the number of total possible interactions; (iv) average trophic level as the 15 average of the trophic levels of species present in the food web. were done with R. We checked for spatial autocorrelation in LCBDSp and in LCBDFW at each sampling month. To do so, we assessed the significance of Moran's I 20 using the R package ape. Both LCBDSp and LCBDFW showed no sign of spatial 21 autocorrelation in any of the months studied (Table

11

  The calculation of the LCBDSp reveals dissimilarities between waterholes (Fig.2A). Within 12 the natural waterholes, among those that are significantly dissimilars from the others, only 13 two are in HNP: Ballaballa (April) and Ngwenya (February). The others are either in the 14 forestry area (Jwapi in February) or in the communal area (Mambanje in February and March, as additive factors the effects of the pump presence, the sampling month and the location of 1 the waterholes to explain the LCBDSp. The results of the model show that the pump presence 2

4 in6

 4 February (P=0.09) and they are significantly lower in the forestry (p <0.03) and communal 5 (p <0.04) areas than in HNP.To analyse the SCBDSp, we first looked at the species SCBDSp rank (FigureS2), which 7 reveals several groups of species according to their contribution to beta diversity (Figure3B, 8 black diamonds and inverted grey triangles). Tetraedron regulare and total nauplii explain 9% 9 of the total SCBDSp. With an additional set of 3 species, T. assymetricum, Euglena sp. and 10 Anabaena sp, 20 % of the total SCBDSp is explained. With 15 more species, that is a total of 11 20 species, up to 60% of the total SCBDSp is explained. These species are by decreasing 12 contribution order: Microcystis aeruginosa, T. limnetica, Phacus curvicauda, Aphanocapsa 13 sp., Oscillatoria sp., Spyrogira sp., Melosira sp., Diatoma sp., Polyarthra vulgaris, 14 Pediastrum simplex, Cymbella sp., Chroococcus sp., Brachionus calyciflorus, Cyclotella sp.

  15

  slightly different results than the LCBDSp. Within natural waterholes, 21 Nengasha, located in the communal area differentiates itself from other waterholes in March, 22 July and November and Ngewshla natural, located in HNP, in March (Figure 2B). Within 23 artificial waterholes, most dissimilarities between waterholes are observed during the dry season in July, September and November and apply to waterholes located in HNP (Dom, 1 Dopi, Jambile artificial, Kennedy 2 and Ngewshla artificial) or in the forestry area (Ganda).

  2

7

  They contribute to 9% of the total SCBDFW. The next three higher contributions explaining up 8 to 21% of the total SCBDFW are Daphnia pulex, B. calyciflorus and the microphytoplankton 9 group. To draw a parallel with the SCBDSp, 60% of the SCBDFW is explained by an additional 10 set of 16 species or group of species by order of decreasing contribution: bacteria, T. 11 assymetricum, protists, P. vulgaris, T. limnetica, T. regulare, Filinia pejleri, nauplii, 12 Cyclotella sp., Daphnia longispina, Euglena sp., Macrocyclops albidus, particulate and 13 dissolved organic matter, Diaphanosoma tropicum, Afrocyclops gibsoni and Brachionus 14 patulus (Figure 3B, black diamonds and grey triangles). Unlike SCBDSp, this set of species 15 gather together consumer species and primary producers. 16 As species and food-web based LCBD and SCBD do not provide the same information about 17 waterhole community and functioning dissimilarities, we plotted LCBDFW and SCBDFW as a 18 function of species LCBDSp and SCBDSp respectively (Figure 3). Linear regressions show that 19 they are positively correlated (adjusted R²= 0.11 and p <0.0001 for LCBD and adjusted 20 R²=0.77 and p <0.0001 for SCBD). The waterholes that have high contribution to both 21 LCDBSp and LCBDFW are Jwapi and Mabuyamabema in February, Nengasha in March and 22 Kennedy 2, Ganda and Ngweshla artificial in November (Figure 3A, black diamonds). The 23 species that have the higher contributions to both SCBDFW and SCBDSp are the phytoplankton 24 species T. assymetricum, T. limnetica, T. regulare, Euglena sp., Cyclotella sp. and the small 1 herbivorous zooplankton B. calyciflorus, P. vulgaris and nauplii (Figure 3B, black diamonds).

2 3

 2 Effects of diversity and food-web indices on LCBD and SCBD values 4We explored the relationships between diversity, food-web indices, and species and food-web 5 beta diversity. The LCBDSp is positively correlated with the average trophic level of the 6 waterhole food-webs and negatively correlated with Shannon diversity (Figure4Band Table

  7

  18species that most contribute to the SCBDFW are highly connected with other species of the 19 meta-food web and have a high trophic level. These species may be either top-consumers, 20 such as carnivorous copepods, or intermediate consumers with numerous prey and predators, 21 such as filter-feeder rotifers, cladoceran and nauplii.

8 9 1 whereas

 81 No significant effects of the presence of pumps on the contribution of waterholes to beta10 diversity 11 Depending on the drought intensity and on the waterhole size and depth, most of the natural 12 waterholes dry up during the dry season while the artificial ones only undergo water level 13 fluctuations thanks to water pumping. While we expected that artificial pumping would 14 decrease both spatial and temporal beta diversity by homogenizing environmental conditions 15 across space and time, we show it has no significant impact on the contribution of the 16 waterholes to species and interaction spatiotemporal beta diversity (LCDBSp and LCBDFW 17 respectively). This result is in agreement with the previous analyses of species community 18 composition in this system. Indeed, Msiteli-Shumba et al (2017) showed that ground water 19 pumping had no effect on the phytoplankton and zooplankton community structure, except in 20 July for zooplankton (Msiteli-Shumba et al. 2017). 21 The absence of difference in the beta diversity contribution between artificial and natural 22 waterholes, and the relatively weak spatial beta diversity observed, might be explained by 23 several complementary mechanisms. First, it might be explained by the fact that 24 environmental conditions become harsher during the dry season in the artificial waterholes 1 too and not only in the natural waterholes. Such harsh conditions in both artificial and natural 2 waterholes might lead to similarly strong environmental filtering in both types of waterholes, 3 consequently decreasing spatial beta diversity (Chase 2007). Indeed, in waterholes that do not 4 dry, the water level strongly decreases and fluctuates from day to day. The surface area varies 5 from 21 903 m² to 531 m² in the park from the rainy to the dry season (Msiteli-Shumba et al. 6 2017). In addition, large herbivores such as elephants, zebras, kudus and giraffes tend to 7 aggregate around waterholes for drinking water (Chamaillé-Jammes, Valeix, et al. 2007; 8 Ferry et al. 2016). Attendance favors dung deposition around waterholes which may induce 9 water nutrient enrichment (Hulot et al. 2019). As a consequence, water conductivity, 10 hardness, turbidity and ammonium concentration strongly increase during the dry season, 11 while the macrophyte abundance decreases as well as water level (Msiteli-Shumba et al. 12 2018). Ammonium can reach concentrations that are toxic to plankton species, leading to 13 strong differences in community composition between the dry and wet seasons in artificial 14 waterholes, as found in natural waterholes. Our analysis is based on species that easily form 15 resting stages and allow them to withstand adverse environmental conditions (Incagnone et al. 16 2015; Ellegaard and Ribeiro 2018). Such resting stage strategy may be developed by these 17 species to avoid unfavorable habitats in permanent waterholes as much as in nature 18 waterholes, resetting plankton communities during the dry season. At the onset of the rainy 19 season, plankton communities could thus build up from the sediment egg bank in all 20 waterholes whatever their dynamics during the dry season. 21 Second, dispersal among waterholes might also contribute to reduced spatial beta diversity 22 and to the lack of difference among natural and artificial waterholes. Plankton species are 23 efficient passive dispersers (Incagnone et al. 2015) provided there is no barrier between 24 favorable habitats (Juracka et al. 2016). The savannah landscape studied here is quite homogeneous and we did not find any sign of spatial autocorrelation in the contribution to 1 species and interaction beta diversity. The mean distance between two waterholes is 23.1 km 2 (SD: 12.9 km), the longest distance among waterholes being 59 km (i.e. distance between 3 Mambanje and Jambile Artificiel). These distances are easily covered by potential vectors of 4 dispersion such as wildlife and birds, which could contribute to connect waterholes and 5 decrease spatial beta diversity of plankton communities. In summary, the similarity of species 6 and interaction spatiotemporal beta diversity in natural and artificial ponds might rely on 7 plankton resting stages that allow to endure the dry season and on dispersion. However, we do 8 not know the weight of resting stages responding similarly to harsh conditions vs. migration 9 in driving the observed spatial beta diversity, and more generally the relative importance of 10 species sorting and mass effects on the spatial dynamics of these waterholes. 11 12 The contribution of waterholes to beta diversity depends on seasonal community changes 13 Our results show that temporal beta diversity can be an important component of the beta 14 diversity of aquatic ecosystems in semi-arid areas, which are characterized by harsh 15 environmental conditions during the dry season. Both the LCDBSp and the LCBDFW of the 16 waterholes depend significantly on the sampling month, the waterholes sampled in November 17 during the dry season showing larger contributions to beta diversity. The importance of the 18 seasonal dynamics for understanding the structure of these aquatic communities is consistent 19 with previous findings (Msiteli-Shumba et al. 2017; Msiteli-Shumba et al. 2018; Hulot et al. 20 2020). Previous studies on this system indeed showed the planktonic community composition 21 as well as food web structure varied mostly between the wet and the dry seasons rather than in 22 relation with water pumping. Our results also highlight the importance of considering 23 temporal beta diversity along with spatial beta diversity. Different waterholes indeed 24 contribute significantly to beta diversity at different times. For instance, Nyamandlovu contributes significantly more than expected at random to species beta diversity in April only, Ganda contributes significantly to species beta diversity in November and December.

  2

  been shown to distinguish ecosystems that are either very rich or degraded 14 (Legendre and De Caceres 2013): high LCBD values might indeed indicate strongly different 15 communities with either rare species combination or communities missing otherwise 16 abundant and common species. In our study, higher values of LCBDSp and LCBDFW are 17 associated with plankton communities with lower species richness and diversity as well as 18 lower connectance. Common species contribute the most to both species and food-web beta 19 diversity, and highly connected species and top consumers are also the main contributors to 20 food-web beta diversity. These results are consistent with the fact that our analysis of beta 21 diversity highlights the waterholes sampled during the driest months. Several studies showed 22 that catastrophic events such as drought may initiate losses of species and interactions, 23 especially top predators inducing a food-web simplification (Ledger, Brown, Edwards, 24 Hudson, et al. 2013; Ledger, Brown, Edwards, Milner, et al. 2013; Jackson et al. 2020). In a study analyzing the planktonic food-webs of roughly the same set of waterholes sampled in 1 November 2012, February and July 2013, Hulot et al (2020) also showed that the food-webs 2 during the dry season have lower connectance, less intermediate species and more basal 3 species, lower omnivory index and lower mean trophic level than during the rainy season. Our 4 present results of higher LCBD values in dry months are in line with these findings except for 5 the mean trophic level, which increases with the LCDBSp. This relationship may rely on the 6 rich planktonic communities found during the rainy season where the top-consumers are more 7 abundant. It might also relate with strong top-down effects of consumers on phytoplankton 8 community composition in food webs with high mean trophic level, as phytoplankton species 9 are the main contributors to species-level beta diversity in our system. Thus, the opposite 10 response of the LCDBSp to the average trophic level and the Shannon diversity is consistent 11 with the fact that the dissimilarity pointed out by the LCBD pertains to both rich and degraded 12 waterholes. 13 Species and food-web beta diversity bring complementary understanding 14 LCBDSp and LCBDFW are only partly related to the same indices of community structure.

  22diversity. Consequently, while the set of waterholes identified as unique by both indices are 23 characterized by simple planktonic food-webs, waterhole uniqueness can also be defined by 24 the opposite values of LCBDSp and LCBDFW of some waterholes. Waterholes that do not 25 contribute more than expected to beta diversity for one value may have a high value for the 1 other ones. That is for instance the case for Nengasha and Jambile artificial in July, and Dopi 2 in September and November, which have both low LCBDSp and high LCBDFW. Although 3 their planktonic communities are composed of common, evenly distributed phytoplankton 4 species, they miss highly connected top consumer species that have pivotal role in the 5 network such as E. gracilis and M. varicans. Conversely, some waterholes, such as Livingi in 6 February, Ballaballa, Nyamandlovu and Tshebetshebe in April and Marist in November, have 7 high LCBDSp and low LCBDFW. These waterholes are characterized by the dominance of a 8 few phytoplankton species in high abundance but which are poorly connected to other species 9 in the food web. These species include cyanobacteria, which are excluded for the computation 10 of the LCBDFW and SCBDFW because they have not involved in trophic interactions, and 11 poorly edible phytoplankton species. In summary, the combination of the two indices allows 12 to identify different sets of waterholes, which are likely related to very different functioning.

  13

22 7 8

 7 composition and food-web structure during the dry season. Meanwhile, the spatial variation of 23 these communities does not appear related with water management (i.e. pumping) contrary to 24 our initial hypothesis. This study highlights the importance of considering the spatio-temporal dynamics of communities, and of combining approaches based on species composition and 1 trophic interactions. To predict the effects of global change stressors on food-web structure 2 and dynamics, we need simplified and intuitive indices related to community health (Heleno 3 et al. 2020). The species and food-web indices are complementary as they help to understand 4 why waterholes are dissimilar in space and time, bridging further the taxonomical and 5 functional perspectives needed to the study of beta diversity. 6 This work was supported by the ANR through the SAVARID 2011 CEPL-003 project and 9 partially conducted within the framework of the Research Platform "Production and 10 Conservation in Partnership" (RP-PCP). The Zimbabwe National Parks and Wildlife 11 Management Authority, Forestry Commission, and Hwange traditional leadership granted 12 access to the study sites. ET was funded by the ANR projects ECOSTAB (ANR-17-CE32-13 0002) and ECONET (ANR-18-CE02-0010). MI was funded by the Fédération-Ile-de-14 France de Recherche sur l'Environnement (FR3020). We thank two anonymous reviewers for 15 their meaningful comments, which helped us to improve the manuscript. ponds (Central Italy): Influence of water permanence and 6 mesohabitat type. Ann Limnol-Int J Limnol. 45(1):29-39. 10.1051/limn/09005 7 Brooks JL, Dodson SI. 1965. Predation, body size, and composition of plankton. Science. 8 150(3692):28-35. 9 Burns CW. 1968. The relationship between body size of filter-feeding Cladocera and the 10 maximum size of particle ingested. Limnol Oceanogr. 13:675-678. 11 Chamaillé-Jammes S, Fritz H, Murindagomo F. 2007. Climate-driven fluctuations in surface-12 water availability and the buffering role of artificial pumping in an African savanna: 13 Potential implication for herbivore dynamics. Austral Ecology. 32:740-748. 10.1111/j.1442-14 9993.2007.01761.x 15 Chamaillé-Jammes S, Valeix M, Fritz H. 2007. Managing heterogeneity in elephant 16 distribution: interactions between elephant population density and surface-water availability.17J Appl Ecol. 44(3):625-633. 18 Chase JM. 2007. Drought mediates the importance of stochastic community assembly. PNAS.

19 104 :

 104 17430-17434. 10.1073pnas.0704350104 20 Chase JM, Myers JA. 2011. Disentangling the importance of ecologicalniches from stochastic 21 processesacross scales. Phil Trans R Soc B. 366:2351-2363. 10.1098/rstb.2011.0063 22 Cook SC, Housley L, Back JA, King RS. 2018. Freshwater eutrophication drives sharp 23 reductions in temporal beta diversity. Ecology. 99(1):47-56. 10.1002/ecy.2069 Coops H, Beklioglu M, Crisman TL. 2003. The role of water-level fluctuations in shallow 1 lake ecosystems -workshop conclusions. Hydrobiologia. 506(1-3):23-27. 2 10.1023/b:hydr.0000008595.14393.77 3 Crabot J, Heino J, Launay B, Datry T. 2019. Drying determines the temporal dynamics of 4 stream invertebrate structural and functional beta diversity. Ecography. 42:1-16.

6

  Crabot J, Heino J, Launay B, Datry T. 2020. Drying determines the temporal dynamics of 7 stream invertebrate structural and functional beta diversity. Ecography. 43(4):620-635. 8 10.1111/ecog.04835 9 da Silva Brito MT, Heino J, Pozzobom UM, Lemes Landeiro V. 2020. Ecological uniqueness 10 and species richness of zooplankton in subtropical floodplain lakes. Aquatic Sciences. 11 82:42-55. 0.1007/s00027-020-0715-3 12 Dray S, Bauman D, Blanchet G, Borcard D, Clappe S, Guénard G, Jombart T, Larocque G, 13 Legendre P, Madi M et al. 2019. adespatial: Multivariate multiscale spatial analysis. R 14 package version 0.3-8. https://cran.r-project.org/package=adespatial. 15 Ellegaard M, Ribeiro S. 2018. The long-term persistence of phytoplankton resting stages in 16 aquatic 'seed banks'. Biological Reviews. 93(1):166-183. 10.1111/brv.12338 17 Ferry N, Dray S, Fritz H, Valeix M. 2016. Interspecific interference competition at the 18 resource patch scale: do large herbivores spatially avoid elephants while accessing water? 19 Journal of Animal Ecology. 85(6):1574-1585. 10.1111/1365-2656.12582 20 Gauzens B, Legendre S, Lazzaro X, Lacroix G. 2013. Food-web aggregation, methodological 21 and functional issues. Oikos. 122(11):1606-1615. 10.1111/j.1600-0706.2013.00266.x 22 Heleno RH, Ripple WJ, Traveset A. 2020. Scientists' warning on endangered food webs. Web 23 Ecology. 20(1). 10.5194/we-20-1-2020 24 Hulot F, Msiteli-Shumba S, Bonhomme M, Legendre S. 2020. Temporal changes in food web 1 structure in semi-arid waterholes. Inland Waters. 10:542-550. 2 10.1080/20442041.2020.1754112 3 Hulot FD, Prijac A, Lefebvre J-P, Msiteli-Shumba S, Kativu S. 2019. Mega-herbivores 4 subsidies in artificial pounds in Hwange National Park, Zimbabwe. Hydrobiologia. 837:161-5 175. 10.1007/s10750-019-3968-x 6 Incagnone G, Marrone F, Barone R, Robba L, Naselli-Flores L. 2015. How do freshwater 7 organisms cross the "dry ocean"? A review on passive dispersal and colonization processes 8 with a special focus on temporary ponds. Hydrobiologia. 750(1):103-123. 10.1007/s10750-9 014-2110-3 10 Jackson MC, Fourie HE, Dalu T, Woodford DJ, Wasserman RJ, Zengeya TA, Ellender BR, 11 Kimberg PK, Jordaan MS, Chimimba CT et al. 2020. Food web properties vary with climate 12 and land use in South African streams. Functional Ecology. 34(8):1653-1665. 13 10.1111/1365-2435.13601 14 Juracka PJ, Declerck SAJ, Vondrak D, Beran L, Cerny M, Petrusek A. 2016. A naturally 15 heterogeneous landscape can effectively slow down the dispersal of aquatic 16 microcrustaceans. Oecologia. 180(3):785-796. 10.1007/s00442-015-3501-5 17 Kappes H, Tackenberg O, Haase P. 2014. Differences in dispersal-and colonization-related 18 traits between taxa from the freshwater and the terrestrial realm. Aquatic Ecology. 48(1):73-19 83. 10.1007/s10452-013-9467-7 20 Kuznetsova A, Brockhoff P, Christensen R. 2017. lmerTest Package: Tests in Linear Mixed 21 Effects Models. Journal of Statistical Software. 82(13):1-26. 10.18637/jss.v082.i13 22 Ledger ME, Brown LE, Edwards FK, Hudson LN, Milner AM, Woodward G. 2013. Extreme 23 Climatic Events Alter Aquatic Food Webs: A Synthesis of Evidence from a Mesocosm Drought Experiment. In: Woodward G, Ogorman EJ, editors. Advances in Ecological 1 Research, Vol 48: Global Change in Multispecies Systems, Pt 3. p. 343-395. 2 Ledger ME, Brown LE, Edwards FK, Milner AM, Woodward G. 2013. Drought alters the 3 structure and functioning of complex food webs. Nature Climate Change. 3(3):223-227.

5

  Legendre P, De Caceres M. 2013. Beta diversity as the variance of community data: 6 dissimilarity coefficients and partitioning. Ecology Letters. 16(8):951-963.7 10.1111/ele.12141 8 Medeiros LdC, Mattos A, Lurling M, Becker V. 2015. Is the future blue-green or brown? The 9 effects of extreme events on phytoplankton dynamics in a semi-arid man-made lake. Aquat 10 Ecol. 49(3):293-307. 10.1007/s10452-015-9524-5 11 Michaloudi E, Moustaka-Gouni M, Pantelidakis K, Katsiapi M, Genitsaris S. 2012. Plankton 12 Succession in the Temporary Lake Koronia after Intermittent Dry-Out. Int Rev Hydrobiol. 13 97(5):405-419. 10.1002/iroh.201101498 14 Msiteli-Shumba SM, Kativu S, Hulot FD. 2017. Influence of environmental variables on 15 plankton community composition in permanent and temporal pools in and around Hwange 16 National Park, Zimbabwe. Trans Roy Soc S Afr. 72(3):266-279.

  17

  10.1080/0035919X.2017.1293571 18 Msiteli-Shumba SM, Kativu S, Utete B, Makuwe E, Hulot FD. 2018. Driving factors of 19 temporary and permanent shallow lakes in and around Hwange National Park, Zimbabwe. 20 Water SA. 44(2):269-282. 10.4314/wsa.v44i2.12 21 Musters CJM, Hunting ER, Schrama M, Cieraad E, Barmentlo SH, Ieromina O, Vijver MG, 22 van Bodegom PM. 2019. Spatial and temporal homogenisation of freshwater macrofaunal 23 communities in ditches. Freshwater Biology. 64(12):2260-2268. 10.1111/fwb.13415 Ohlmann M, Miele V, Dray S, Chalmandrier L, O'Connor L, Thuiller W. 2019. Diversity 1 indices for ecological networks: a unifying framework using Hill numbers. Ecology Letters. 2 22(4):737-747. 10.1111/ele.13221 3 Pellissier L, Albouy C, Bascompte J, Farwig N, Graham C, Loreau M, Maglianesi MA, 4 Melian CJ, Pitteloud C, Roslin T et al. 2018. Comparing species interaction networks along 5 environmental gradients. Biological Reviews. 93(2):785-800. 10.1111/brv.12366 6 Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D. 2012. The dissimilarity of species 7 interaction networks. Ecology Letters. 15(12):1353-1361. 10.1111/ele.12002 8 Sarremejane R, England J, Sefton CEM, Parry S, Eastman M, Stubbington R. 2020. Local and 9 regional drivers influence how aquatic community diversity, resistance and resilience vary 10 in response to drying. Oikos. 10.1111/oik.07645 11 Slusarczyk M, Grabowski T, Pietrzak B. 2017. Quantification of floating ephippia in lakes: a 12 step to a better understanding of high dispersal propensity of freshwater plankters.

13 4 14

 4 Hydrobiologia. 798(1):61-72. 10.1007/s10750-015-2437-Teferi M, Declerck SAJ, De Bie T, Lemmens P, Gebrekidan A, Asmelash T, Dejenie T, 15 Gebrehiwot K, Bauer H, Deckers JA et al. 2014. Strong effects of occasional drying on 16 subsequent water clarity and cyanobacterial blooms in cool tropical reservoirs. Freshw Biol.

  17

59( 4 )

 4 :870-884. 10.1111/fwb.12312 18 Thomas S, Cecchi P, Corbin D, Lemoalle J. 2000. The different primary producers in a small 19 African tropical reservoir during a drought: temporal changes and interactions. Freshw Biol. 20 45(1):43-56. 10.1046/j.1365-2427.2000.00616.x 21 Thomaz SM, Pagioro TA, Bini LM, Murphy KJ. 2006. Effect of reservoir drawdown on 22 biomass of three species of aquatic macrophytes in a large sub-tropical reservoir (Itaipu, 23 Brazil). Hydrobiologia. 570:53-59. 10.1007/s10750-006-0161-9 Tonkin JD, Bogan MT, Bonada N, Rios-Touma B, Lytle DA. 2017. Seasonality and 1 predictability shape temporal species diversity. Ecology. 98(5):1201-1216. 2 10.1002/ecy.1761 3 Whittaker RH. 1972. Evolution and measurement of species diversity. Taxon. 21:213-251. 4 Zohary T, Ostrovsky I. 2011. Ecological impacts of excessive water level fluctuations in 5 stratified freshwater lakes. Inland Waters. 1(1):47-59. 10.5268/iw-1.

Figure 1 .

 1 Figure 1. Map of the study site and food-web examples. (A) Map with Hwange National

Figure 2 .

 2 Figure 2. Species (a) and food-web (b) based LCBD over all waterholes and dates.

Figure 3 .

 3 Figure 3. Species and food-web (A) LCDB and (B) SCBD over all waterholes and dates.

Figure legends (

 legends Figure legends

Figure 4 .

 4 Figure 4. Food-web (grey square) and species (yellow triangle) based LCBD as a

Figure 5 .

 5 Figure 5. Food-web (grey square) and species (yellow triangle) based SCBD as a

Figure S1 :

 S1 Figure S1: Schematic diagram representing the procedure to build the interaction-by-

Figure S2 .

 S2 Figure S2. Species and food-web based SCBD and LCBD as a function of species (A) and

Table 1 :

 1 Effects of sampling month, pump presence and location on LCBD values. The table reports the results for the complete models.

				Chisq (for
	LCBD values	Explanatory variables	df	LCBDSp) or F (for	P value
				LCBDFW)
		Month	6	22.80	0.0008
	LCBDSp	Pump presence	1	0.018	0.89
		Location	2	5.167	0.07
		Month	6	2.955	0.0097
	LCBDFW	Pump presence	1	0.03	0.8628
		Location	2	0.471	0.6256

Table 1

 1

Table 2 :

 2 Effects of diversity and food web indices on LCBD and SCBD values. The table reports the results for the best models.

	LCBD and SCBD values	Explanatory variables	df	Estimates	Chisq (for LCBD) or F (for SCBD)	P value
	LCBDSp	Average trophic level Shannon diversity	1 1	4.587e-03 -2.290e-03	34.113 97.793	<0.0001 <0.0001
	LCBDFW	Species richness Connectance	1 1	-3.020e-05 -1.234e-03	15.152 7.2067	<0.0001 0.0072
	SCBDSp	Species occurrence frequency Species degree in metaweb	1 1	2.917e-02 -9.339e-05	115.99 5.4712	<0.0001 0.0212
		Species occurrence frequency	1	3.413e-02	188.822	<0.0001
	SCBDFW	Species degree in metaweb	1	1.611e-04	55.633	<0.0001
		Trophic level in metaweb	1	4.590e-03	19.492	<0.0001

Table 2

 2

Table S1 : Spatial autocorrelation of LCBDSp and LCBDFW by sampling month, as measured by Moran's I.

 S1

		LCBDSp	LCBDFW
	Month
		Moran's I	P value	Moran's I	P value
	February	-0.003	0.64	0.086	0.08
	March	-0.004	0.65	-0.018	0.79
	April	-0.03	0.89	-0.029	0.88
	July	-0.11	0.51	0.069	0.20
	September	0.082	0.25	-0.099	0.88
	November	-0.094	0.97	-0.17	0.46
	December	-0.18	0.55	0.06	0.35

Supplementary Material -for review

Acknowledgements a. b. c. d.