Tanzania Journal of Science 47(1): 282-295, 2021 ISSN 0856-1761, e-ISSN 2507-7961 © College of Natural and Applied Sciences, University of Dar es Salaam, 2021

Effects of Plant Species Diversity and Biomass on Grazing Patch Selection by Semi Free-Ranging Cattle

Samora M. Andrew Department of Ecosystems and Conservation, College of Forestry, Wildlife and Tourism, Sokoine University of Agriculture, P. O. Box 3010, Morogoro, Tanzania. E-mail address: [email protected] Received 5 Aug 2020, Revised 2 Jan 2021, Accepted 4 Jan 2021, Published Feb 2021

Abstract Studies examining forage selection by large herbivores have generally focused on single herbivore-plant relationships and few studies link plant species diversity with large herbivore feeding preferences, particularly in species-rich ecosystems. A study was therefore conducted to examine how herbaceous plant biomass, species richness, diversity and evenness, together with elevation and distances to houses, agricultural fields and cattle holding-pen areas (kraals), predict cattle grazing intensity at small (0.5 x 0.5 m) and large (20 x 50 m) patches by semi free-ranging indigenous cattle in Kilombero Valley Floodplain wetland Tanzania. Results showed that cattle grazed patches with high species richness and intermediate level of biomass at a small spatial scale, suggesting that selection of small patches is a trade-off between food quality and quantity. On a larger scale, cattle grazed patches with a higher abundance of palatable plant species with high biomass, but with less plant species diversity. Moreover, grazing intensity in large patches was positively related to elevation and distance from agricultural fields. Overall, this study shows that considering both plant community properties and physiographic variables in forage selection studies facilitates a better understanding of grazing ecology of large domestic herbivores in species-rich ecosystems. Thus, maintenance of plant species richness and biomass in wetland rangelands is important to improve domestic herbivore production and conservation of biodiversity.

Keywords: Domestic herbivore; Forage selection; plant-animal interaction; Rangelands; Species richness.

Introduction for herbivores to meet their optimal nutritional Large herbivores utilize food resources requirements, diet selection must focus on a unevenly by feeding more intensively in some variety of plant species with different areas than others in heterogeneous nutritional quality and quantity, and diet environments (Bailey 1995, Bailey et al. 1998, choices therefore operate at different spatial Wang et al. 2010a, Milligan and Koricheva and temporal scales. Since a single plant 2013). Several theories have been proposed to species will not meet the nutritional demands describe foraging behaviour and associated of animals, large herbivores usually switch grazing patterns among herbivores. According between alternative plant species to diversify to optimal foraging theory, diet selection is a their diet during foraging (Simpson et al. 2004, trade-off between benefits of ingesting a Wiggins et al. 2006). According to the nutrient particular food resource and the cost of balance hypothesis, dietary diversity allows obtaining it (Stephens and Krebs 1986). Thus, individual herbivores to balance the intake of 282 http://journals.udsm.ac.tz/index.php/tjs www.ajol.info/index.php/tjs/

Tanz. J. Sci. Vol. 47(1) 2021 different nutrients (Westoby 1978), which grazing patterns at large scales. Overall, this consequently improves their performance and suggests that the two sets of factors (plant growth (Simpson et al. 2004, Unsicker et al. community properties and physiographic 2008). Switching among plant species during variables) potentially explain the spatial foraging may also buffer the animal against variations in grazing intensities, and that large accumulation of digestion-inhibiting herbivores are more likely to visit species-rich compounds or toxic secondary metabolites that patches and sites close to their congregation could be present in the consumed foliage points more frequently than species poor (Freeland and Janzen 1974, Milligan and patches and distant sites (Bailey 1995, Bailey Koricheva 2013). et al. 1998, Tarhouni et al. 2010). For small invertebrate herbivores (e.g. Studies on forage selection by large arthropods), changes in plant community herbivores have generally focused on properties, particularly reduced species relationships of single species of herbivores diversity, may negatively affect their and plants and studies linking plant species abundance, diversity and performance diversity with large herbivore feeding (Scherber et al. 2006, Unsicker et al. 2008). preferences are only starting to emerge. Two Okullo et al. (2013) found that diversity of notable exceptions are the study by Milligan small savannah mammals increased with and Koricheva (2013), who found that herbaceous species evenness and diversity, and browsing moose (Alces alces) preferred that their biomass increased when few plant species-rich sites, and Wang et al. (2010b), species dominated the herbaceous species who documented an asymptotic relationship assemblage. Moreover, the maintenance of between sheep (Ovis aries) food intake and plant species richness is critical to improve plant species richness. In this study, the nutritional status, performance and overall determinants of foraging decisions by bulk production of large herbivores (Gordon and feeding semi-free ranging cattle were Prins 2008, Wang et al. 2010b, Milligan and investigated in the Kilombero wetlands in Koricheva 2013). This suggests that plant Tanzania. Specifically, plant community community properties affecting forage quality properties (biomass, species richness, evenness and quantity, such as plant biomass, species and diversity) and physiographic variables richness, diversity and evenness, may (elevation, distances to houses, agricultural determine feeding site selection by large fields and kraals) were related to cattle grazing herbivores. intensity at large (20 x 50 m) and small (0.5 x Besides plant community properties, 0.5 m) feeding sites. It was predicted that: (a) physiographic factors, such as slope and plant community properties would be distance to congregation areas, may also important for grazing intensity at a small influence feeding site selection by large spatial scale, while physiographic variables herbivores (Bailey et al. 1998). In general, explain variations in grazing intensities at large domestic herbivores tend to avoid steep slopes patch sizes; consistent with Bailey et al. and feeding sites far away from congregation (1996); (b) since plant species richness offers areas, such as camp sites and water sources an opportunity for large herbivores to optimize (Bailey et al. 1996, Tarhouni et al. 2010). their nutrient balance (Wang et al. 2010b), Moreover, the synthesis of mechanisms behind grazing intensity at small spatial scale would be the grazing distribution patterns of large explained best by species richness, evenness herbivores, Bailey et al. (1996) pointed out that and diversity, and (c) since biomass is plant community properties associated with significant important to bulk feeding cattle forage quality and quantity is the main (Belovsky 1997), it would be an important determinant of grazing patterns at small spatial determinant of spatial variations in grazing scales, while physiographic factors predict intensities at the small spatial scale.

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Materials and methods short herbaceous vegetations, were the focus of Location of the study area this study because they form a substantial The study area, Kilombero Valley portion of the wetland. Agro-pastoralists have Floodplain wetland, is situated in the Morogoro grazed their cattle in these grasslands at least Region in Ulanga and Kilombero Districts, in over the last two decades. The marginal the south-central Tanzania and found between grasslands contain a large number of plant longitude 8° 32′ 0′′ S and latitude 36° 29′ 0′′ E species which is 33% of all species reported in (Figure 1). The Kilombero wetland is the the wetland including economically important largest lowland freshwater wetland in East fodder species (Andrew et al. 2012). Africa rich in biodiversity that made it be declared a Ramsar site in 2002 (Ramsar 2020). Vegetation sampling and physiographic The wetland is also ecologically important as it variables adjoins the Great Selous Ecosystem, a World Following a preliminary survey conducted Heritage Site. It covers an area of 7,967 km2 at in 2009/2010, 60 plots of 20 x 50 m each from an elevation of 210-400 meters (Ramsar 2020). different areas of the marginal grasslands were The area experiences sub-humid tropical randomly selected to assess the livestock climate with a mean annual temperature of 26 grazing pattern in relation to plant community C and a mean annual rainfall of 1200-1400 properties and physiographic variables. Using a mm (Kangalawe and Liwenga 2005). At table of random numbers and compass bearings Kilombero, light and sporadic rainfall occurs (1–360°), twenty 0.5 x 0.5 m quadrats were usually between December and February, while established in each plot. In each quadrat, heavy and regular rainfall occurs between species cover and identities were recorded March and June. The Kilombero wetland using the point intercept method (Bråthen and supports diverse livelihood activities, including Hagberg 2004). A point intercept table 0.5 m agriculture, fisheries and livestock keeping long, 0.5 m wide and 0.5 m high was placed (Kangalawe and Liwenga 2005). Among the above each quadrat and a 0.003 m diameter and livestock (cattle, sheep, goats and donkeys), 0.8 m long pin inserted vertically through 25 cattle is the most abundant (about 300,000 points spread uniformly on the grid. All individuals in an area of 37,058 km2), with the contacts each species made with the pin (i.e. indigenous Tanzania shorthorn zebu cattle even when the same individual was in contact (Sukuma and Maasai strains) being dominant. with the pin more than once) was recorded to Cattle are released in the morning as semi free- estimate the cover of each species (Frank and ranging livestock and are herded back to kraals McNaughton 1990). In addition, all grazed every evening. A more detailed description of foliar parts that contacted the pin were the study area was given by Andrew et al. recorded. Plant species were identified at the (2012). National Herbarium of Tanzania in Arusha, Although the actual number of plant species where all voucher specimens are deposited. is not known in the Kilombero wetland, at least Elevation of plots was estimated at the centre 350 plant species are documented from the area by a hand held calibrated global positioning comprising riverside, papyrus (Cyperus system (GPS). The distances between plots and papyrus L.) swamps, low-lying valley the three closest kraals were measured by GPS grasslands, tall grasslands, marginal grasslands, and the mean distance was used in the analyses. marginal woodlands, combretaceous wooded Distances between a plot and nearest grasslands and miombo woodlands plant agricultural field and house were estimated communities (Starkey et al. 2002). The visually. marginal grasslands, with intensively grazed

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Figure 1: Map showing location of Kilombero Valley wetland in Morogoro Region, Southern- central Tanzania.

Data analyses agricultural field and grazing intensity for To obtain vegetation data for plots, the quadrats (small patches) and plots (large total number of contacts per species over all patches) were created. In this case, all the plant quadrats was used. Data matrices of plant species were considered in the computation of biomass, species richness, evenness, diversity, community properties in each patch type. A elevation and distances to house, kraal and separate data matrix of community properties

285 Andrew - Effects of plant species diversity and biomass on grazing patch selection … and physiographic variables for large patches candidate models. The standard model-fitting where only grazed plant species were and adequacy checking procedures to fit each considered was also created. Finally, using the model was adhered to. Backward elimination three matrices (i.e. all species together in small was used to find the best model for each and large patches, and all grazed species in response variable by removing first the large patches), a relationship between livestock interaction terms, then quadratic and lastly grazing and within-patch plant community linear terms (Crawley 2007). After fitting each properties and physiographic variables was of the candidate models, change in Akaike established. Richness was obtained by adding information criterion (ΔAICc) and Akaike the total number of species that made contacts weight (Wi) for all the models was computed with the pin. The Shannon diversity index was using AICcmodavg package in R (R computed with Biodiversity R package (Kindt Development Core Team 2017). Final model and Coe 2005) and evenness as E = H'/lnS with the lowest ΔAICc or the highest Wi was where S is the number of species (Magurran selected as the most parsimonious (Burnham and McGill 2011). Grazing intensity was and Anderson 2002, Burnham et al. 2011). obtained from the total number of grazed Finally, simple linear regressions were used for leaves. The sum of the number of pin hits was large patches on the most parsimonious models used as a measure of biomass in each data to establish the relationship between grazing matrix (Bråthen and Hagberg 2004). intensity and community properties and Information-theoretic model selection and physiographic variables. Because of the nesting multi-model procedure (Burnham et al. 2011) structures of the data on small patches, was used to examine how community generalized linear mixed models (GLMMs) properties and physiographic variables may analysis was employed on the most influence the grazing patterns of animals. parsimonious model to establish the Multi-model inferences avoid overlooking relationships between grazing intensity and alternative models that could have nearly community properties and physiographic equivalent fit to the single most parsimonious variables at this spatial scale (Crawley 2007). model (Burnham and Anderson 2002). To In the GLMMs analysis, function lmer was determine the most parsimonious model used in the package lme4 (Bates 2005) and plot therefore, a set of candidate models were was used as a random effect. All the statistical developed (for each patch type) using a linear analyses were performed using R statistical combination of non-correlated (Pearson software (R Development Core Team 2017). Product Moment Correlation, r < 0.50), quadratic (second order) and interaction terms Results of community properties and physiological Herbaceous community composition variables, and the grazing intensity. Some of A total of 115 plant species were recorded the plant community properties, such as species in the Kilombero grasslands. Of these, only richness and diversity were strongly correlated twenty-two species (Table 1) were grazed by at the small (r = 0.87, p < 0.05) and large (r = cattle. Echinochloa colona, Paspalum 0.70, p < 0.05) patch scales when all the scrobiculatum, Digitaria velutina, D. ternata, species were considered. Moreover, species Panicum maximum, Brachiaria deflexa, evenness was strongly correlated with species Sporobolus pyramidalis and S. ioclados (all diversity at the small (r = 0.68, p < 0.05) and Poaceae) were the most intensively grazed large (r = 0.85, p < 0.05) patch scales when all species (Figure 2) and together they comprised the species were analysed. Consequently, these more that 53% of the total cover of all the variables (species richness, diversity and species in the area. evenness) were not included in the same

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Table 1: Herbaceous plant species grazed by cattle at Kilombero wetland, Morogoro, Tanzania Scientific name Grazing Growth Functional Family intensity form group level (%) Brachiaria comata (A. Rich.) Stapf 0.7 Grass Graminoid Poaceae Brachiaria deflexa (Schumach) Robyns 7.5 Grass Graminoid Poaceae Crotalaria spp. 0.2 Shrub Shrub Fabaceae Cynodon nlemfuensisVanderyst 2.8 Grass Graminoid Poaceae Digitaria ciliaris (Retz.) Koel. 1.9 Grass Graminoid Poaceae Digitaria longiflora (Retz.) Pers. 2.3 Grass Graminoid Poaceae Digitaria ternate Stapf. 3.4 Grass Graminoid Poaceae Digitaria velutina (Forssk) P. Beauv. 5.9 Grass Graminoid Poaceae Echinochloa colona (Linn.) Link 32.7 Grass Graminoid Poaceae Echinochloa haplocloda (Stapf.) 2.7 Grass Graminoid Poaceae Eragrostis cilianensis (All.) Lutati 0.3 Grass Graminoid Poaceae Hygrophila auriculata (Schumach) Heine 0.3 Forb Forb Acanthaceae Mellinis minutiflora P. Beauv. 1.9 Grass Graminoid Poaceae Oryza punctata Steud. 2.0 Grass Graminoid Poaceae Panicum maximum Jacq 4.8 Grass Graminoid Poaceae Paspalum scrobiculatum L. 19.6 Grass Graminoid Poaceae Pavonia flavoferruginea (Forssk.) Hepper 0.4 and J.R.I. Wood Shrub Shrub Malvaceae Pynostachys ruandensis Del. Wild 0.2 Forb Forb Labiatae Sida rhombifolia L. 0.2 Forb Forb Malvaceae Sporobolus ioclados (Trin.) Nees 3.4 Grass Graminoid Poaceae Sporobolus pyramidalis P. Beauv. 4.7 Grass Graminoid Poaceae Urochloa mossambicensis (Hack.) Dandy 2.2 Grass Graminoid Poaceae Grazing intensity level is the ratio between number of grazed leaves of individual species and the total number of grazed leaves for all the grazed species (22) in percentages.

Figure 2: Herbaceous plant species most grazed by cattle in the Kilombero wetlands, Morogoro Tanzania.

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Grazing intensity in small patches models (models 1-4), with ΔAICc between Grazing intensity in small patches had 3.96 and 717.24, explained less of the no relationship with physiographic variables, variations in selection of small patches when while plant community properties were all the plant species were considered important for grazing intensity when all the (Appendix 1). Grazing intensity in small species were considered (Table 2). The best patches increased with biomass and species two candidate models included plant biomass, richness when all the plant species were species richness and evenness (models 4 and 5, analysed (Table 2). However, grazing intensity Appendix 1), while models with species was highest in small patches with an diversity had relatively poor fit (model 1) in intermediate level of biomass (unimodal explaining grazing intensity on small patch relationship, Table 2). Thus, there was a level (Appendix 1). The most parsimonious negative relationship between biomass and model explaining grazing in small patches species richness in explaining variations in included biomass, species richness and their grazing intensity at small spatial scale (Table interactions (Table 2). All the remaining 2).

Table 2: The most parsimonious model explaining the relationships between grazing intensity and plant community properties and physiographic variables within small patches for all species at the Kilombero wetland, Morogoro, Tanzania Effects Estimate SE t-value P-value Intercept –19.140 0.320 –5.976 < 0.001 Biomass 0.057 0.004 13.160 < 0.001 Biomass2 –0.001 0.0001 –7.853 < 0.001 Species richness 0.210 0.051 4.136 < 0.001 Biomass: species richness –0.006 0.001 –5.239 < 0.001

Grazing intensity in large patches patches (Appendix 2). Grazing intensity Both plant community properties and decreased significantly with species diversity, physiographic factors significantly explained whereas it increased with elevation and variations in grazing intensity in large patches distance to agricultural fields (Table 3). There when all the plant species were combined was no significant interaction among biomass, (Table 3). The most parsimonious model species richness, evenness, diversity, elevation explaining grazing in large patches included and distances to agricultural fields, house or species diversity, elevation and distance to kraal in explaining variations in grazing agricultural fields (model 5; Appendix 2). All intensity in large patches when all the plant other models (models 1-4), with ΔAICc species were considered (Table 3). between 2.78 and 10.25, explained less of the total variations in grazing intensity in large

Table 3: The most parsimonious model explaining the relationships between grazing intensity and plant community properties and physiographic variables within large patches for all species at the Kilombero wetland, Morogoro, Tanzania Effects Estimate SE t-value P-value Intercept 23.600 22.080 1.069 0.290 Elevation 64.730 25.650 2.524 0.015 Diversity –81.990 23.250 –3.527 < 0.001 Distance to agricultural field 52.240 22.060 2.368 0.021

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When only grazed plant species (22) were and the three-way interactions of biomass, analysed in large patches, plant community richness and evenness when grazed plant properties and physiographic variables were species were separately analysed (Table 4). included in the best two candidate models However, grazing intensity in large patches explaining grazing intensity (Appendix 3). was negatively related with the interaction of Plant species richness explained a marginal biomass and richness, as well as with richness significant variation in grazing intensity in and evenness when only grazed plant species large patches (Table 4). Grazing intensity was were considered (Table 4). positively related to plant biomass, elevation

Table 4: The most parsimonious model explaining the relationships between grazing intensity and plant community properties and physiographic variables within large patches, considering only grazed plant species at the Kilombero wetland, Morogoro, Tanzania Effects Estimate SE t-value P-value Intercept –102.45 77.76 –1.317 0.194 Biomass 443.04 191.77 2.310 0.025 Richness 330.27 170.49 1.937 0.059 Evenness 203.47 161.02 1.264 0.213 Elevation 65.86 27.12 2.429 0.019 Biomass: richness –935.94 367.70 –2.545 0.014 Richness: evenness –635.55 310.86 –2.045 0.046 Biomass: richness: evenness 1816.58 687.86 2.641 0.011

Discussion A positive relationship between grazing This study showed that cattle grazed intensity and plant species richness has also patches with high plant species richness and been documented for moose (Milligan and moderate biomass at a small spatial scale. On a Koricheva 2013) and sheep (Wang et al. larger scale, cattle grazed on patches with low 2010b). Since a single plant species will not diversity, at high elevations, and at longer meet animal’s optimal energy and nutritional distances from agricultural fields. Moreover, requirements, diet switching is important for cattle selected many palatable plant species large domestic herbivores, such as cattle as with high biomass at larger spatial scale, and pointed out by Westoby (1978) and Wiggins et richness had less influence on the selection of al. (2006). By foraging in patches with high these palatable species at large scale. species richness, cattle increase chances to Therefore, it appears that community properties secure palatable and nutrient-rich materials are important for cattle foraging decisions both (Westoby 1978). Moreover, a high species at small and large spatial scales. In addition to richness offers the opportunity for cattle to species diversity, physiographic variables were change species preferences and thereby also important for variations in the grazing stimulating intake of more food (Early and intensity at large spatial scale. Thus, the first Provenza 1998) which leads to higher energy prediction that plant community properties and protein gains (Belovsky 1997). In addition, would be important for grazing intensity at a experimental studies suggest that daily nutrient small spatial scale, whereas physiographic intake by large domestic herbivores (sheep) variables would be important in explaining increases chances not only for selecting the variations in grazing intensity at larger patch most palatable species in patches, but also from sizes, is not fully supported. complementary effects of specie richness (Wang et al. 2010b). In particular, taking a

289 Andrew - Effects of plant species diversity and biomass on grazing patch selection … mixed diet from several plant species (i.e. from small scales seems to be based on a trade-off a species-rich patch) facilitates intake of both between food quantity and quality (i.e. a varied low and different classes of plant secondary diet). metabolites (PSMs; although generally low in This study showed that grazing intensity grasses) (Cheeke 1995, Provenza et al. 2003, was positively related to distance to Marsh et al. 2006) in addition to the agricultural fields, i.e. grazing intensity instantaneous intake of balanced nutrients. increased away from agricultural fields. A Therefore, feeding in species-rich patches likely explanation for this is that herders often provides the animal with nutritional benefits release cattle away from the agricultural fields and reduces toxic effects of PSMs (Gordon and to avoid cattle feeding on crops (e.g. rice and Prins 2008). Thus, feeding in species-rich vegetables) under semi free-ranging system. patches saves energy that cattle would expend The progressive change in grazing intensity on searching and travelling, which is not the with elevation at a large spatial scale implies case when the herbivore feeds in species-poor that patches located at relatively high patches (WallisDeVries 1996, Milligan and elevations are more preferred by livestock than Koricheva 2013). lower floodplain landscapes. Although Grazing intensity related positively to plant differences in elevation are small, many biomass at a small spatial scale, and grazing nutrient- and energy-rich plant species, such as intensity reached maximum at an intermediate Echinochloa colona and Paspalum level of biomass where species richness is scrobiculatum cannot tolerate prolonged predicted to be highest in grasslands (Oba et al. inundation (Phillips et al. 2003, CABI 2020) 2001, Bhattarai et al. 2004). Moreover, grazing and these species therefore potentially occur intensity increased with biomass of grazed mostly on higher elevations. Moreover, it is species at a large spatial scale. Large bulk possible that vegetation patches at relatively feeders, such as cattle, can tolerate low nutrient high elevations are more accessible and content of plants but need a greater abundance available to cattle and thus often selected and of foliage to maintain energy demands (Bell grazed (Bailey et al. 1996, Bailey et al. 1998). 1970, Bell 1971, Jarman 1974, Belovsky The physiographic variables, such as 1997). Thus, at small spatial scale in the study distance to kraal sites, were not important in area cattle appear to prefer patches with explaining variations in grazing intensity at intermediate biomass and high species large spatial scale in the study area. This agrees richness, whereas at a larger scale they appear partly with a study that related grazing intensity to prefer individual species with high biomass. to droppings, distance from pastoral camp sites Moreover, using cafeteria and field and pika burrows by Dorji et al. (2013). They experiments, Wang et al. (2010b) demonstrated found that distance from camp sites predicted that species richness enhanced overall nutrient only a small variation (30%) in grazing intake of sheep and that the benefit of higher intensity, suggesting that other physiographic species richness came from the greater variables (e.g. elevation and distance to abundance of foliage consumed and not a agricultural fields) may be more important for higher quality of foliage. This suggests that foraging choices by herbivores at large spatial biomass is also important to sustain energy in scales. addition to species richness, for large domestic herbivores (Belovsky 1997, Gordon and Prins Conclusion 2008). Since grazing was at maximum at In general, few studies examine the combined moderate amounts of biomass, the third effects of plant community properties and prediction that cattle selected patches with high physiographic variables on grazing intensity by plant biomass at small spatial scale is not fully free-ranging large herbivores in plant species- supported. Instead, the selection of patches at rich ecosystems. This study therefore, adds to

290 Tanz. J. Sci. Vol. 47(1) 2021 our understanding of grazing ecology in such heterogeneous environments. Appl. Anim. systems, particularly the role of plant Behav. Sci. 45: 183-200. community properties and physiographic Bailey DW, Gross JE, Laca EA Rittenhouse variables in shaping grazing intensity of large LR, Coughenour MB, Swift DM, and Sims domestic herbivores. This study suggests that PL 1996 Mechanisms that result in large foraging by cattle is driven by different factors herbivore grazing distribution patterns. J. at different spatial scales. At small scale, cattle Range Manage. Arch. 49: 386-400. appear to select and graze small vegetation Bailey DW, Dumont B and WallisDeVries MF patches with high species richness and 1998 Utilization of heterogeneous intermediate level of biomass; presumably to grasslands by domestic herbivores: theory achieve nutritional balance and maximize to management. Ann. Zootech. 47: 321-333. energy gains (Westoby 1978, Belovsky 1997). Bates D 2005 Fitting linear mixed models in R. On a larger scale, cattle appear to select and R News. 5: 27-30. graze patches with relative low plant diversity, Bell RH 1970 The use of the herb layer by but with a higher abundance of palatable plant grazing ungulates in the Serengeti. In: species with high biomass. Moreover, at a large Watson A (Ed) Animal populations in scale cattle seem to graze mostly at high relation to their food resources (pp. 111– elevations and at longer distances from 123), Blackwell, Oxford. agricultural fields. These results have important Bell RH 1971 A grazing ecosystem in the implications for improvement of livestock Serengeti. Sci. Am. 225: 86-93. production and rangelands management Belovsky GE 1997 Optimal foraging and strategies. In particular, maintenance of plant community structure: the allometry of species richness and biomass is important to herbivore food selection and competition. improve rangelands conditions and domestic Evol. Ecol. 11: 641-672. herbivore production. Bhattarai KR, Vetaas OR and Grytnes JA 2004 Relationship between plant species richness Acknowledgements and biomass in an arid sub-alpine grassland Funding for the study was provided by of the central Himalayas, Nepal. Folia Norwegian Programme for Development, Geobot. 39: 57-71. Research and Education at Sokoine Bråthen KA and Hagberg O 2004 More Universities of Agriculture (NUFU-TZ efficient estimation of plant biomass. J. 2007/10229). Professors Ørjan Totland and Veg. Sci. 15: 653-660. Stein R. Moe provided extensive inputs that Burnham KP and Anderson DR 2002 Model helped to improve the draft manuscript. Lilian selection and multi-model inference: a J. Kishimbo and Bruce Amanya assisted in the practical information-theoretic approach. field work, while Godson Leliyo helped with 2nd ed, Springer, New York. the collection and identification of plants. Burnham KP, Anderson DR and Huyvaert KP 2011 AIC model selection and multimodel Conflict of interest: None. inference in behavioral ecology: some background, observations, and References comparisons. Behav. Ecol. Sociobiol. 65: Andrew SM, Moe SR, Totland Ø and Munishi 23-35. PKT 2012 Species composition and CABI 2020 Invasive species compendium. functional structure of herbaceous http://www.cabi.org/isc/?compid=5anddsid vegetation in a tropical wetland system. =20368andloadmodule=datasheetandpage= Biodiv. Conserv. 21: 2865-2885. 481andsite=144. Accessed 21 March 2020 Bailey DW 1995 Daily selection of feeding Cheeke P 1995 Endogenous toxins and areas by cattle in homogeneous and mycotoxins in forage grasses and their

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293 Andrew - Effects of plant species diversity and biomass on grazing patch selection …

Appendix 1: Comparison of the candidate models following information-theoretic model selection and multi-model procedure in explaining the relationship between grazing intensity and community properties and physiographic variables within small patches for all species at Kilombero wetland, Morogoro, Tanzania Model Candidate model(s) K AICc ΔAICc Wi Cum.Wt Code 5 Grazing intensity = Biomass + Species 6 2595.20 0.00 0.88 0.88 richness - Biomass2- Biomass: richness 4 Grazing intensity = Biomass + Species 9 2599.16 3.96 0.12 1.00 richness + Evenness + Biomass: richness + Biomass: evenness + Richness: evenness + Biomass: richness: evenness 3 Grazing intensity = Species richness + 7 2773.65 178.45 0.00 1.00 Evenness + Richness2 +Evenness2 + Species richness: evenness 2 Grazing intensity = Biomass + Species 7 3305.07 709.87 0.00 1.00 evenness + Biomass2 + Evenness2 + Biomass: evenness 1 Grazing intensity = Biomass + Diversity + 7 3312.44 717.24 0.00 1.00 Biomass2 + Diversity2 + Biomass:Diversity K: the number of parameters in the regression model, AICc: Akaike information criterion, Wi: Akaike weight, ΔAICc: change in Akaike information criterion, Cum.Wt: cumulative Akaike weight and “:” show interaction terms. Only a few candidate models are presented for illustration purposes.

Appendix 2: Comparison of the candidate models following information-theoretic model selection and multi-model procedure in explaining the relationship between grazing intensity and community properties and physiographic variables within large patches for all species at Kilombero wetland, Morogoro, Tanzania. Model Candidate model(s) K AICc ΔAIC Wi Cum.Wt Code c 5 Grazing intensity = Elevation + Distance to 5 599.39 0.00 0.69 0.69 agricultural field - Shannon diversity 4 Grazing intensity = Elevation + Distance to 6 602.17 2.78 0.17 0.87 agricultural field - Species richness - Evenness 3 Grazing intensity = Elevation + Distance to 5 603.18 3.79 0.10 0.97 agricultural field - Species evenness 2 Grazing intensity = Elevation + Distance to 5 606.19 6.80 0.02 1.00 agricultural field - Species richness 1 Grazing intensity = Biomass + Species 13 609.64 10.25 0.00 1.00 richness + Evenness + Elevation + Distance to agricultural field - Distance to house + Distance to kraal - Biomass: richness- Biomass: evenness - Richness: evenness + Biomass: richness: evenness K: the number of parameters in the regression model, AICc: Akaike information criterion, Wi: Akaike weight, ΔAICc: change in Akaike information criterion, Cum.Wt: cumulative Akaike weight and “:” show interaction terms. Only a few candidate models are presented for illustration purposes.

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Appendix 3: Comparison of the candidate models following information-theoretic model selection and multi-model procedure in explaining the relationship between grazing intensity and community properties and physiographic variables within large patches considering only grazed species at Kilombero wetland, Morogoro, Tanzania. Model Candidate model(s) K AICc ΔAICc Wi Cum.Wt Code 5 Grazing intensity = Biomass + Species 9 604.52 0.00 0.51 0.51 richness + Evenness + Elevation - Biomass: richness- Richness: evenness + Biomass: richness: evenness 4 Grazing intensity = Biomass + Species 13 606.16 1.64 0.22 0.73 richness + Evenness + Elevation - Distance to house + Distance to agricultural field + Distance to kraal - Biomass: richness- Biomass: evenness - Richness: evenness + Biomass: richness: evenness 3 Grazing intensity = Biomass + Elevation - 5 606.67 2.15 0.17 0.90 Distance to house 2 Grazing intensity = Elevation + Distance to 4 608.91 4.39 0.06 0.96 agricultural field 1 Grazing intensity = Elevation - Evenness - 5 609.45 4.93 0.04 1.00 Distance to house K: the number of parameters in the regression model, AICc: Akaike information criterion, Wi: Akaike weight, ΔAICc: change in Akaike information criterion, Cum.Wt: cumulative Akaike weight and “:” show interaction terms. Only a few candidate models are presented for illustration purposes.

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