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Estimating Animal Populations

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introduction to the study of animal populations 2 edition reprintedition Manual

Amazon Drive Cloud storage from Amazon. Alexa Actionable Analytics for the Web. Sell on Amazon Start a Selling Account. Towards the description of livestock mobility in Sahelian Africa: Some results from a survey in Mauritania. Animal movements are typically driven by areas of supply and demand for animal products and by the seasonality of production and demand. As animals can potentially spread infectious diseases, disease prevention can benefit from a better understanding of the factors influencing movements patterns in space and time.

In order to better characterize the animal movements patterns, a survey was carried out in , and those data were analysed here using social network analysis SNA metrics and used to train predictive gravity models. More specifically, we aimed to contrast the movements structure by ruminant species, season Tabaski vs.

Non-Tabaski and mode of transport truck vs. The networks differed according to the species, and to the season, with a changed proportion of truck vs. The gravity models were able to predict the probability of a movement link between two locations with moderate to good accuracy AUC ranging from 0. The significant predictor variables of a movement link were the human and sheep population at the source and origin, and the distance separating the locations. Though some improvements would be needed to predict traded volumes and better account for the barriers to mobility, the results provide useful predictions to inform epidemiological models in space and time, and, upon external validation, could be useful to predict movements at a larger regional scale.

Many factors that may influence the dynamics and transmission of infectious diseases have been rapidly changing over the last decades. Alongside climate and land use changes, often considered in emerging infectious diseases literature as main drivers, other factors such as the distribution, growth and connectivity of human populations have also been changing rapidly as result of demographic transitions, conflicts or migrations.

Similarly, the distribution and connectivity of traded animal populations are strongly influenced by agricultural intensification and changes in trade patterns. The combined effect of these societal and environmental changes taking place simultaneously is difficult to assess, but some, particularly mobility of livestock and traditional trading practices, have been associated with the emergence and the spread of infectious diseases [ 1 — 4 ], and can have strong socio-economic impacts [ 5 , 6 ]. In addition to these long-term trends, culture and tradition strongly shape societies at national, regional and global scale.

For example, human population movement during specific periods such as Chinese Spring Festival [ 7 ], annual holidays [ 8 ], or religious feast around Christmas, Ramadan, Thanksgiving or Hindu Holy feast are known to cause substantially affect global mobility [ 9 ] with significant economic and epidemiological implications [ 10 — 12 ]. Large movements of animal populations are also linked to changes in the spatial pattern of food demand, which is anticipated by the market.

In low-income countries, such as Sahelian African countries, rapid changes in demand for animal products linked to cultural and traditional events therefore leads to a large number of animals—notably sheep, being slaughtered to meet the seasonal food demand. As a consequence, in the months and weeks preceding such events, trading of live animals is particularly intense.

Due to the dry ecoclimate of the Sahelian area, agriculture and breeding strongly depend on the amount of rainfall and the availability of pasture.

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As a consequence, successive droughts also dramatically affect the Livestock flow. Another example is Madagascar, where kapsile is a traditional practice consisting in an animal barters linked to the labour need in a period when breeders are cash-poor and which strongly affects the patterns of trade flows [ 3 , 13 ]. In West Africa, similar practices, called loans , have been described as a large number of short term animal exchanges for reproduction, food supply through milk production, animal traction, etc [ 14 ].

On this occasion, young rams are slaughtered in most families on the 10 th day of the month "dhou al-hija", a religious holiday during the last month of the Muslim lunar calendar. The date of this festival changes each year according to the Gregorian solar calendar and strongly structures the volume of traded sheep during the year. Annual and seasonal differences are thus observed in the sheep trade flows.

The drought of was the main climatic event for the area. Since then, the area has been mostly in deficit of rainfall [ 16 ]. Cattle population dropped, whilst the population of small ruminants increased, the latter being more robust than the former to harsh climatic conditions. Apolloni et al. During this period, the price of male lambs sharply increased, and the high demand strongly affected the trading network structure. In this paper, we aimed to understand how the Tabaski festivity changed the trade network in Mauritania compared to the rest of the year.

Here, we first provided a complementary description of inner flows using social network analysis indicators, by contrasting the Tabaski and non- Tabaski periods, the different ruminant species and modes of transport. Mauritania is situated in the hyper-arid Sahara and arid Sahel ecozones [ 18 ], with low annual rainfall 0— mm concentrated in a short rainy season June-September. In the northern part of the country, the driest one, only short-cycle plants grow. Livestock, mainly camels and small ruminants, are reared moving around available water points and grazing areas.

The southern area, more humid and greener is mostly exploited by transhumant herds. Most of cattle population, being less mobile and demanding more water and nutrients, is concentrated in the southern area, mainly in the region around the river Senegal. Because of the harsh conditions, mobility is a key aspect of animal rearing in Mauritania.

Animals are moved almost continuously among grazing areas to optimize the consumption of good quality nutrients.

Introduction to the Study of Animal Populations

In the absence of slaughterhouses, stocking facilities and road infrastructures, animals are traded alive and butchered at consumption markets. Past droughts indirectly contributed to the growth of cities, in particular Nouakchott, due to the migration of previous farmers and herders from the countryside to urban areas in search of jobs.

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Because of this, Nouakchott, the capital city has seen its population dramatically exploding during and following the drought years. In , the drought indicators were at the same levels as those of , indicating that the food emergency is not completely over. The inadequate levels of rainfall and the continuous threat of the droughts force herders to sell their livestock, in particular small ruminants, due to shortages of suitable feeding areas.

The survey aimed to monitor the movement patterns during the year Transhumance movements aiming to gradually move herds for suitable pasture areas were not included. The database was cross-checked against sanitary certificates, the scientific documents describing transhumance patterns, and the knowledge of veterinarian staff [ 17 ].

Both national and international trade-flows were recorded and the transboundary movements were double-checked through surveys on transit sites between Senegal and Mauritania. In , the Ministry for Rural Development and Environment reported a population of Accordingly, and because no finer data was available at national level, we used the most recent version of the Gridded Livestock of the World database GLW , where the subnational livestock statistics for Mauritania dates back to , and were adjusted to match the FAOSTAT national totals [ 20 , 21 ].

The WorldPop database was used for the human population [ 22 ]. Both databases were aggregated at a spatial resolution of 0. In this study, we only considered inner movements of cattle, camels, sheep and goats within Mauritania. Social network analysis SNA [ 24 ] have proved to be of significant interest in animals movements analyses in the past decade [ 25 — 28 ].

Here, it was first used to describe the trade networks according to the species, the transport modality using truck vs. These mentioned periods were defined as strongly influencing the livestock flow within the country by Apolloni et al. A set of network parameters were estimated for the different networks using the simplified definition provided in Wasserman and Faust, [ 24 ]:.

Diameter: a network-level parameter representing the greatest number of links in the shortest path between two nodes. Average path length: a network-level parameter measuring the average number of steps along the shortest paths of all possible nodes pairs, i. The clustering coefficient: a node-level parameter of the density of local ties. It measures the probability that neighboring nodes of a node are connected. The density: a network-level parameter measuring the proportion of observed links among the possible links between nodes, and indicates how strongly a network is connected.

Average degree: a network-level parameter quantifying the average number of links connected with a node in a network. The removal of specific nodes, and their links, cause the network to fragment in a set of smaller subnetworks. The size of the largest component can be thought as the maximum extent a disease can spread after the implementation of the control measure vaccination of animal in the areas surrounding the nodes, market closure, etc. Volkova et al. This parameter estimates the minimum probability for a disease to be transmitted from one node to another to trigger an epidemic.

The lowest the epidemic threshold the higher is the risk of an epidemics. In the case of a weighted network the epidemic threshold can be estimated as:. Following the same procedure as in Lancelot et al. Highlight on the role of occasional links were given connections appearing just once per year. We plotted path intersections between different species, transports modalities and seasons, to highlight possible common or specific links for different combinations.

In addition, metrics quantifying these intersections were estimated, such as the pairwise percentage of common and specific paths between two networks, respectively. Gravity models were used to estimate the probability of a link between two distinct nodes according to their features. These models were developed in the field of socio-economics and human migration studies [ 32 , 33 ]. They provide estimates for the flows of goods or people between two nodes, as a function of node-level variables e.

The equation is linearized into Eq 2 by a log-transformation, and its coefficients can be estimated with generalized linear models. In this study, we first aimed to estimate the probability of a trade connection between two nodes. Therefore, log MIGij was replaced with the logit of this probability and logistic regression was used to estimate the coefficients.

As the response, all pairs of connected nodes were coded with 1, and all other pairs of nodes were coded with 0. The analysis was split according to the main structuring factors of the networks, i. For each sub-model, we tested seven combinations of predictors, considering them both at the origin and destination with inclusion of a distance estimator i great-circle distance, ii cost-path distance based on accessibility friction surface or iii cost-path distance based on elevation friction surface.

The different combinations of predictors that were tested in the models are shown in Table 1. These were used because the nodes did not correspond to any particular administrative division that could have been used i. In each sub-model, we first included the extracted animal population of each species at both the source and destination cattle, sheep, goats, and camels. In a second step, this analysis was repeated using the number of animals traded between these two locations as the response variable.


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To better differentiate the factors influencing the trade probability from those influencing its volume, the latter analysis was restricted to pairs of locations with an existing trade link. All analyses were coded and carried out using R [ 34 ]. The dataset consisted in 2, trade movements involving 7. The subset of national movements, the focus of this analysis, included 1, movement events involving 2.

International movements involved around 5 million head, sold or bought to or from Senegal, Mali, Ivory Coast, Guinea Bissau and Morocco.


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As the destination of these international movements within these countries was unknown, they could not be included in the network analysis or gravity models. Within Mauritania, the trading network was composed of 65 nodes and 84 unique paths. The foot and truck transport modalities presented contrasted patterns. Similarly, those contrasting patterns somewhat matched the seasonal pattern. Great-circle distance of the national movements by truck left and on foot right recorded in the survey. Distance are given in kilometers for each species thick line: small ruminants, dashed line: cattle, thick dash line: camel.

Values represent the number of animals moved for each given period. Movements are defined as national if both the origin and destination are located within the Mauritanian border.

Introduction to the Study of Animal Populations: 2. edition. Reprintedition

Small ruminants SR include sheep and goats. The Mauritanian network was weakly connected. However, each of the exchange networks full network, or species-specific sub-networks contained a single component in which the average length of the shortest path between node pairs was lower than 2 links, the maximum value diameter being 5 links.

The goat and camel trading networks were smaller with a diameter of 2 and 3 links. On average, in the full species network, a given node was directly connected with approximately 2 other nodes on average average degree.

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Both the in-degree and in-weight distributions are right-skewed. With the highest centrality score, the capital city is the most important node of the network. Finally, the betweenness distribution is right-skewed and Boutilimit appears to be the node with highest betweenness. Close Figure Viewer. Browse All Figures Return to Figure. Previous Figure Next Figure. Email or Customer ID. Forgot password? Old Password.

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