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91 results for “African savannas”
Data from: Large mammal declines and the incipient loss of mammal-bird mutualisms in an African savanna ecosystem
Over the past half-century, large mammal populations have declined substantially throughout East Africa, mainly due to habitat loss and unsustainable direct exploitation. While it has been acknowledged that the loss of large mammals can have direct and cascading effects on community composition and ecosystem characteristics, limited quantitative work has been done on how declines of large herbivore populations impacts the abundance of mutualistic symbionts. Using a space-for-time observational approach, we quantified the large mammal community alongside the densities, host preferences and behaviors of mutualistic red-billed oxpeckers (Buphagus erythrorhynchus), and yellow-billed oxpeckers (Buphagus africanus) in northern Tanzania. At the landscape scale, mammal community composition was substantially less diverse in highly human-dominated areas when compared with more protected areas, with an observed complete loss of large wild mammal species in two study areas. Mirroring this trend, oxpecker densities were lowest in the least protected areas, and highest in fully protected areas. Using resource selection functions implemented via generalized linear models at different scales, we found that oxpeckers (1) were predominantly (67% of red-billed oxpeckers; 70% of yellow-billed oxpeckers) feeding on larger (between 500kg and 1500kg) ungulate host species within the mammal community, (2) usually preferred feeding on larger individuals (adults and males) within a specific host species population, and (3) preferred hosts that were more tolerant of their presence. In particular, cattle were especially intolerant of oxpecker presence and were relatively effective in displacing oxpeckers. We found little evidence that oxpecker feeding was parasitic across all host species; wound feeding was only observed on giraffe, comprising 6% and 4% of feeding behavior in red-billed and yellow-billed oxpeckers respectively. Thus, a loss of large-bodied and oxpecker tolerant host species is a likely explanation for declines of oxpecker populations in human dominated landscapes, which may have further cascading effects.
Data from: Demographic legacies of fire history in an African savanna
Fire is a key determinant of woody vegetation structure in savanna ecosystems, acting both independently and synergistically through interactions with herbivores. Fire influences biodiversity and ecological functioning, but quantifying its effects on woody structure is challenging at both species and community scales. Deeper insight into fire effects, and fire-herbivore interactions, can be gained through the examination of species-specific demographic and dynamic changes occurring across areas with different fire regimes in the presence of large herbivores. We used the Carnegie Airborne Observatory (an integrated LiDAR and imaging spectroscopy system) to map woody tree structure, species and dynamics over a four-year interval across two adjacent savanna landscapes with contrasting fire histories in Kruger National Park, South Africa. A history of higher fire frequency was associated with reduced woody canopy cover (17% vs. 23%) and an increased overall rate of treefall (27% vs. 18%). The landscape with a history of higher fire frequency displayed a shift in woody canopy height distribution from a unimodal curve to a bimodal pattern at the community scale, with large reductions in height classes < 7 m. Differences in tree height distributions and treefall rates across sites were underpinned by species-specific responses to fire frequency. Acacia nigrescens displayed the highest rates of treefall, most likely related to elephant activity, with losses exceeding 40% in the 6-9 m height classes. Synthesis. Our findings indicate that fire history imparts demographic legacies not only on vegetation structure, but also on current vegetation dynamics. Current treefall rates of certain tree species are exacerbated by a history of higher fire frequency. Species-specific and context-conscious investigations are critical for elucidating the driving mechanisms underlying broader community patterns.
Data from: Ants, fire and bark traits affect how African savanna trees recover following damage
Bark damage resulting from elephant feeding is common in African savanna trees with subsequent interactions with fire, insects and other pathogens often resulting in tree mortality. Yet, surprisingly little is known about how savanna trees respond to bark damage. We addressed this by investigating how the inner bark of marula (Sclerocarya birrea), a widespread tree species favoured by elephants, recovers after bark damage. We used a long-term fire experiment in the Kruger National Park to measure bark recovery with and without fire. At 24 months post-damage, mean wound closure was 98, 92, and 72% respectively in annual and biennial burns and fire exclusion treatments. Fire exclusion resulted in higher rates of ant colonisation of bark wounds, and such ant colonisation resulted in significantly lower bark recovery. We also investigated how ten common savanna tree species respond to bark damage and tested for relationships between bark damage, bark recovery and bark traits while accounting for phylogeny. We found phylogenetic signal in bark dry matter content, bark N and bark P, but not in bark thickness. Bark recovery and damage was highest in species which had thick moist inner bark and low wood densities (Anacardiaceae), intermediate in species which had moderate inner bark thickness and wood densities (Fabaceae) and lowest in species which had thin inner bark and high wood densities (Combretaceae). Elephants prefer species with thick, moist inner bark, traits that also appear to result in faster recovery rates.
Data from: Strong but opposing effects of associational resistance and susceptibility on defense phenotype in an African savanna plant
The susceptibility of plants to herbivores can be strongly influenced by the identity, morphology, and palatability of neighboring plants. While the defensive traits of neighbors often determine the mechanism and strength of associational resistance and susceptibility, the effect of neighbors on plant defense phenotype remains poorly understood. We used field surveys and a prickle‐removal experiment in a semi‐arid Kenyan savanna to evaluate the efficacy of physical defenses against large mammalian herbivores in a common understory plant, Solanum campylacanthum. We then quantified the respective effects of spinescent Acacia trees and short‐statured grasses on browsing damage and prickle density in S. campylacanthum. We paired measurements of prickle density beneath and outside tree canopies with long‐term herbivore‐exclusion experiments to evaluate whether associational resistance reduced defense investment by decreasing browsing damage. Likewise, we compared defense phenotype within and outside pre‐existing and experimentally created clearings to determine whether grass neighbors increased defense investment via associational susceptibility. Removing prickles increased the frequency of browsing by ~25%, and surveys of herbivory damage on defended leaves suggested that herbivores tended to avoid prickles. As predicted, associational resistance and susceptibility had opposing effects on plant phenotype: individuals growing beneath Acacia canopies (or, analogously, within large‐herbivore exclosures) had a significantly lower proportion of their leaves browsed and produced ca 70–80% fewer prickles than those outside refuges, whereas plants in grass‐dominated clearings were more heavily browsed and produced nearly twice as many prickles as plants outside clearings. Our results demonstrate that associational resistance and susceptibility have strong, but opposing, effects on plant defense phenotype, and that variable herbivore damage is a major source of intraspecific variation in defense phenotype in this system.
Data from: Climate and the landscape of fear in an African savanna
1.Herbivores frequently have to make tradeoffs between two basic needs: the need to acquire forage and the need to avoid predation. One manifestation of this tradeoff is the "landscape of fear" phenomenon – wherein herbivores avoid areas of high perceived predation risk even if forage is abundant or of high quality in those areas. Although this phenomenon is well-established among invertebrates, its applicability to terrestrial large herbivores remains debated, in part because experimental evidence is scarce. 2.This study was designed to (a) experimentally test the effects of tree density – a key landscape feature associated with predation risk for African ungulates – on herbivore habitat use, and (b) establish whether habitat use patterns could be explained by tradeoffs between foraging opportunities and predation risk-avoidance. 3.In a Kenyan savanna system, replicate plots dominated by the tree Acacia drepanolobium were cleared, thinned, or left intact. Ungulate responses were measured over four years, which included years of moderate rainfall as well as a severe drought. 4.Under average rainfall conditions, most herbivores (primarily plains zebra, Grant's gazelle, and hartebeest) favored sites with fewer trees and higher visibility – regardless of grass production – while elephants (too large to be vulnerable to predation) favored sites with many trees. During the drought, however, herbivores favored sites that had high grass biomass, but not high visibility. Thus, during the drought, herbivores sought areas where food was more abundant, despite probable higher risk of predation. 5.These results illustrate that the "landscape of fear", and the associated interactions between top-down and bottom-up effects, is not static, but rather shifts markedly under different conditions. Climate thus has the potential to alter the strength and spatial dynamics of behaviorally-mediated cascades in large herbivore systems.
Data, Metadata, R-codes and R data files for publication "Comparative ungulate diversity and biomass change with human use and drought: implications for community stability and protected area prioritization in African savannas" by Bartzke et al. in Ecology and Evolution
<p>These files contain data and metadata for modeling ungulate diversity and biomass in the Maasai Mara ecosystem in Kenya in the drought year of 1999 and a year with normal rainfall, 2002. The files also contain R codes and R data files.</p> <p>Metadata.pdf: Metadata for files "mc_333m.csv" and "mc_1km.csv"</p> <p>mc_333m.csv: A data file for 333-meter-by-333-meter sub-blocks.</p> <p>prepare_data.r: R code to impute missing vegetation records in 333-meter-by-333-meter subblocks and summarize the data over 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>krige_vegetation.RData: An R data file containing the imputed vegetation records.</p> <p>mc_1km.csv: A data file for 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>mc_1km.r: R code for modeling ungulate diversity and biomass; mc_1km_mod.RData: An R data file for loading the ungulate diversity and biomass models.</p> <p>mc_1km.RData: An R data file containing model predictions of ungulate diversity and biomass.</p> <p>mc_1km_plots.r: R code for plotting model predictions of ungulate diversity and biomass.</p> <p>MMNR_boundary.shp: A shapefile of the Maasai Mara National Reserve boundary in Kenya and associated files. These files are used for plotting the predictions of ungulate diversity and biomass.</p> <p>MMNR_border.zip: A shapefile and associated files for the Maasai Mara National Reserve border with Tanzania. These files are also used for plotting predictions of ungulate diversity and biomass.</p>
Distribution. Five disjunct regions: Guinean and Sahelian savannas in W Africa (Guinea and Liberia E to Niger and Nigeria); White Nile valley (Sudan, South Sudan, and NE DR Congo); around Lake Victoria (Uganda, Kenya, and Tanzania); a narrow strip along the E African coast between Mombasa and Dar es Salaam; and SW shore of Lake Tanganyika in NE Zambia, based on a reidentification in 2010 of two specimens in the London Natural History Museum collected in 1920. in Molossidae
Distribution. Five disjunct regions: Guinean and Sahelian savannas in W Africa (Guinea and Liberia E to Niger and Nigeria); White Nile valley (Sudan, South Sudan, and NE DR Congo); around Lake Victoria (Uganda, Kenya, and Tanzania); a narrow strip along the E African coast between Mombasa and Dar es Salaam; and SW shore of Lake Tanganyika in NE Zambia, based on a reidentification in 2010 of two specimens in the London Natural History Museum collected in 1920.
Distribution. Found in two disjunct regions: largest part in savanna belt of East Africa from Ethiopia, Djibouti, and Somalia S through Kenya, Tanzania (including Pemba and Unguja in Zanzibar Archipelago), extreme SE Zambia, and E Zimbabwe to C Mozambique, with W stretch to E Central African Republic and NE DR Congo; smallest part includes SW Republic of the Congo and extreme NW Angola. in Rhinonycteridae
Distribution. Found in two disjunct regions: largest part in savanna belt of East Africa from Ethiopia, Djibouti, and Somalia S through Kenya, Tanzania (including Pemba and Unguja in Zanzibar Archipelago), extreme SE Zambia, and E Zimbabwe to C Mozambique, with W stretch to E Central African Republic and NE DR Congo; smallest part includes SW Republic of the Congo and extreme NW Angola.
Subspecies and Distribution. L.v.victoriaeThomas,1893—Tanzania. L.v.angolensisThomas,1904—Angola. L.v.senegalensisRochebrune,1883—Senegal,TheGambia. L. v. whyte: Thomas, 1894 — Malawi. The African Savanna Hare is present from the Atlantic coast of NW Africa (Western Sahara S to Guinea), E across the Sahel to Sudan and the extreme W Ethiopia, S through E Africa (E DR Congo, Uganda, W Kenya, Rwanda, Burundi, and Tanzania) to most of Angola, Zambia, Malawi, NE Namibia, Botswana, Zimbabwe, Mozambique, E South Africa, Swaziland, and Lesotho; a small isolated population exists near Beni Abbas in the Sahara Desert in W Algeria. in Leporidae
Subspecies and Distribution. L.v.victoriaeThomas,1893—Tanzania. L.v.angolensisThomas,1904—Angola. L.v.senegalensisRochebrune,1883—Senegal,TheGambia. L. v. whyte: Thomas, 1894 — Malawi. The African Savanna Hare is present from the Atlantic coast of NW Africa (Western Sahara S to Guinea), E across the Sahel to Sudan and the extreme W Ethiopia, S through E Africa (E DR Congo, Uganda, W Kenya, Rwanda, Burundi, and Tanzania) to most of Angola, Zambia, Malawi, NE Namibia, Botswana, Zimbabwe, Mozambique, E South Africa, Swaziland, and Lesotho; a small isolated population exists near Beni Abbas in the Sahara Desert in W Algeria.
On following pages: 34. Burmese Hare (Lepus peguensis); 35. Ethiopian Hare (Lepus fagani); 36. Scrub Hare (Lepus Highland Hare (Lepus starcki); 40. Cape Hare (Lepus capensis); 41. Abyssinian Hare (Lepus habessinicus); 42. Tolai oiostolus); 46. Snowshoe Hare (Lepus americanus); 47. Black-tailed Jackrabbit (Lepus californicus); 48. Black Jackrabbit saxatilis); 37. African Savanna Hare (Lepus victoriae); 38. Yarkand Hare (Lepus yarkandensis); 39. Ethiopian Hare (Lepus tolai); 43. Desert Hare (Lepus tibetanus); 44. Yunnan Hare (Lepus comus); 45. Woolly Hare (Lepus (Lepus insularis). in Leporidae
On following pages: 34. Burmese Hare (Lepus peguensis); 35. Ethiopian Hare (Lepus fagani); 36. Scrub Hare (Lepus Highland Hare (Lepus starcki); 40. Cape Hare (Lepus capensis); 41. Abyssinian Hare (Lepus habessinicus); 42. Tolai oiostolus); 46. Snowshoe Hare (Lepus americanus); 47. Black-tailed Jackrabbit (Lepus californicus); 48. Black Jackrabbit saxatilis); 37. African Savanna Hare (Lepus victoriae); 38. Yarkand Hare (Lepus yarkandensis); 39. Ethiopian Hare (Lepus tolai); 43. Desert Hare (Lepus tibetanus); 44. Yunnan Hare (Lepus comus); 45. Woolly Hare (Lepus (Lepus insularis).
On following pages: 390. Greater Red White-toothed Shrew (Crocidura flavescens); 391. Lesser Red White-toothed (Crocidura nigricans); 394. Goliath White-toothed Shrew (Crocidura goliath); 395. Somali White-toothed Shrew (Crocidura Shrew (Crocidura viaria); 398. Savanna White-toothed Shrew (Crocidura fulvastra); 399. Ugandan White-toothed Shrew toothed Shrew (Crocidura cinderella); 402. loi White-toothed Shrew (Crocidura voi); 403. Yankari White-toothed Shrew toothed Shrew (Crocidura tarella); 406. Jackson's White-toothed Shrew (Crocidura jackson); 407. Kivu Long-haired White-toothed White-toothed Shrew (Crocidura dolichura): 410. Savanna White-toothed Swamp Shrew (Crocidura longipes); 411. Cameroon 413. Eisentraut's White-toothed Shrew (Crocidura eisentrauti); 414. Upemba White-toothed Shrew (Crocidura zimmeri (Crocidura caliginea); 417. Congo White-toothed Shrew (Crocidura congobelgica); 418. Latona's White-toothed Shrew (Crocidura ansellorum); 421. Pitman''s White-toothed Shrew (Crocidura pitmani); 422. Makwassie White-toothed Shrew Shrew (Crocidura hirta); 392. Heather White-toothed Shrew (Crocidura erica); 393. Blackish White-toothed Shrew somalica); 396. African Giant White-toothed Shrew (Crocidura olivieri); 397. Savanna Path White-toothed (Crocidura mutesae); 400. Small-footed White-toothed Shrew (Crocidura parvipes); 401. Cinderella White-(Crocidura yankariensis); 404. Kahuzi White-toothed Swamp Shrew (Crocidura stenocephala); 405. Tarella White-Shrew (Crocidura lanosa); 408. Dramatic White-toothed Shrew (Crocidura ludia); 409. Long-tailed White-toothed Shrew (Crocidura picea); 412. Manenguba White-toothed Shrew (Crocidura manengubae);); 415. Misotshi-Kabogo White-toothed Shrew (Crocidura Iwiroensis); 416. African Dusky White-toothed Shrew (Crocidura latona); 419. Polia's White-toothed Shrew (Crocidura polia); 420. Ansell's White-toothed Shrew (Crocidura maquassiensis); 423. Turbo White-toothed Shrew (Crocidura turba). in Soricidae
On following pages: 390. Greater Red White-toothed Shrew (Crocidura flavescens); 391. Lesser Red White-toothed (Crocidura nigricans); 394. Goliath White-toothed Shrew (Crocidura goliath); 395. Somali White-toothed Shrew (Crocidura Shrew (Crocidura viaria); 398. Savanna White-toothed Shrew (Crocidura fulvastra); 399. Ugandan White-toothed Shrew toothed Shrew (Crocidura cinderella); 402. loi White-toothed Shrew (Crocidura voi); 403. Yankari White-toothed Shrew toothed Shrew (Crocidura tarella); 406. Jackson's White-toothed Shrew (Crocidura jackson); 407. Kivu Long-haired White-toothed White-toothed Shrew (Crocidura dolichura): 410. Savanna White-toothed Swamp Shrew (Crocidura longipes); 411. Cameroon 413. Eisentraut's White-toothed Shrew (Crocidura eisentrauti); 414. Upemba White-toothed Shrew (Crocidura zimmeri (Crocidura caliginea); 417. Congo White-toothed Shrew (Crocidura congobelgica); 418. Latona's White-toothed Shrew (Crocidura ansellorum); 421. Pitman''s White-toothed Shrew (Crocidura pitmani); 422. Makwassie White-toothed Shrew Shrew (Crocidura hirta); 392. Heather White-toothed Shrew (Crocidura erica); 393. Blackish White-toothed Shrew somalica); 396. African Giant White-toothed Shrew (Crocidura olivieri); 397. Savanna Path White-toothed (Crocidura mutesae); 400. Small-footed White-toothed Shrew (Crocidura parvipes); 401. Cinderella White-(Crocidura yankariensis); 404. Kahuzi White-toothed Swamp Shrew (Crocidura stenocephala); 405. Tarella White-Shrew (Crocidura lanosa); 408. Dramatic White-toothed Shrew (Crocidura ludia); 409. Long-tailed White-toothed Shrew (Crocidura picea); 412. Manenguba White-toothed Shrew (Crocidura manengubae);); 415. Misotshi-Kabogo White-toothed Shrew (Crocidura Iwiroensis); 416. African Dusky White-toothed Shrew (Crocidura latona); 419. Polia's White-toothed Shrew (Crocidura polia); 420. Ansell's White-toothed Shrew (Crocidura maquassiensis); 423. Turbo White-toothed Shrew (Crocidura turba).
On following pages: 350. Tanzanian White-toothed Shrew (Crocidura tansaniana); 351. Usambara White-toothed Shrew (Crocidura usambarae); 352. Kivu White-toothed Shrew (Crocidura kivuana); 353. Niobe's White-toothed Shrew (Crocidura niobe): 354. Desert White-toothed Shrew (Crocidura smithii); 355. Sahelian Tiny White-toothed Shrew (Crocidura pasha); 356. Roosevelt's White-toothed Shrew (Crocidura roosevelt); 357. Long-footed White-toothed Shrew (Crocidura crenata): 358. Grasse's White-toothed Shrew (Crocidura grassei); 359. Bicolored African White-toothed Shrew (Crocidura fuscomurina); 360. Flat-headed White-toothed Shrew (Crocidura planiceps); 361. Crosse's White-toothed Shrew (Crocidura crossei): 362. Jouvenet's White-toothed Shrew (Crocidura jouvenetae); 363. Mauritanian White-toothed Shrew (Crocidura lusitania); 364. Naked-tailed White-toothed Shrew (Crocidura littoralis): 365. Gracile White-toothed Shrew (Crocidura maurisca); 366. Reddish-gray White-toothed Shrew (Crocidura cyanea); 367. Swamp White-toothed Shrew (Crocidura mariquensis), 368. Lesser Gray-brown White-toothed Shrew (Crocidura silacea): 369. Dent's White-toothed Shrew (Crocidura denti); 370. Hildegarde's White-toothed Shrew (Crocidura hildegardeae); 371. Mamfe White-toothed Shrew (Crocidura virgata), 372. Bates's White-toothed Shrew (Crocidura batesi): 373. Sao Tome White-toothed Shrew (Crocidura thomensis); 374. Fingui White-toothed Shrew (Crocidura fingui); 375. Fraser's White-toothed Shrew (Crocidura poensis); 376. Nigerian White-toothed Shrew (Crocidura nigeriae); 377. Hun White-toothed Shrew (Crocidura attila); 378. Fox's White-toothed Shrew (Crocidura foxi); 379. Buttikofer's White-toothed Shrew (Crocidura buettikoferi); 380. Therese's White-toothed Shrew (Crocidura theresae); 381. Large-headed White-toothed Shrew (Crocidura grandiceps); 382. Wimmer's White-toothed Shrew (Crocidura wimmeri); 383. African Black White-toothed Shrew (Crocidura nigrofusca); 384. Savanna Dwarf White-toothed Shrew (Crocidura nanilla); 385. Nimba White-toothed Shrew (Crocidura nimbae). in Soricidae
On following pages: 350. Tanzanian White-toothed Shrew (Crocidura tansaniana); 351. Usambara White-toothed Shrew (Crocidura usambarae); 352. Kivu White-toothed Shrew (Crocidura kivuana); 353. Niobe's White-toothed Shrew (Crocidura niobe): 354. Desert White-toothed Shrew (Crocidura smithii); 355. Sahelian Tiny White-toothed Shrew (Crocidura pasha); 356. Roosevelt's White-toothed Shrew (Crocidura roosevelt); 357. Long-footed White-toothed Shrew (Crocidura crenata): 358. Grasse's White-toothed Shrew (Crocidura grassei); 359. Bicolored African White-toothed Shrew (Crocidura fuscomurina); 360. Flat-headed White-toothed Shrew (Crocidura planiceps); 361. Crosse's White-toothed Shrew (Crocidura crossei): 362. Jouvenet's White-toothed Shrew (Crocidura jouvenetae); 363. Mauritanian White-toothed Shrew (Crocidura lusitania); 364. Naked-tailed White-toothed Shrew (Crocidura littoralis): 365. Gracile White-toothed Shrew (Crocidura maurisca); 366. Reddish-gray White-toothed Shrew (Crocidura cyanea); 367. Swamp White-toothed Shrew (Crocidura mariquensis), 368. Lesser Gray-brown White-toothed Shrew (Crocidura silacea): 369. Dent's White-toothed Shrew (Crocidura denti); 370. Hildegarde's White-toothed Shrew (Crocidura hildegardeae); 371. Mamfe White-toothed Shrew (Crocidura virgata), 372. Bates's White-toothed Shrew (Crocidura batesi): 373. Sao Tome White-toothed Shrew (Crocidura thomensis); 374. Fingui White-toothed Shrew (Crocidura fingui); 375. Fraser's White-toothed Shrew (Crocidura poensis); 376. Nigerian White-toothed Shrew (Crocidura nigeriae); 377. Hun White-toothed Shrew (Crocidura attila); 378. Fox's White-toothed Shrew (Crocidura foxi); 379. Buttikofer's White-toothed Shrew (Crocidura buettikoferi); 380. Therese's White-toothed Shrew (Crocidura theresae); 381. Large-headed White-toothed Shrew (Crocidura grandiceps); 382. Wimmer's White-toothed Shrew (Crocidura wimmeri); 383. African Black White-toothed Shrew (Crocidura nigrofusca); 384. Savanna Dwarf White-toothed Shrew (Crocidura nanilla); 385. Nimba White-toothed Shrew (Crocidura nimbae).
On following pages: 61. Giffard's Gerbil (Gerbilliscus giffardi); 62. Northern Savanna Gerbil (Gerbilliscus kempii); 63. Fringe-tailed Gerbil (Gerbilliscus robustus); 64. Phillips's Gerbil (Gerbilliscus phillipsi); 65. Black-tailed Gerbil (Gerbilliscus nigricaudus); 66. Southern Savanna Gerbil (Gerbilliscus validus); 67. Bushveld Gerbil (Gerbilliscus leucogaster); 68. East African Gerbil (Gerbilliscus vicinus); 69. Boehm's Gerbil (Gerbilliscus boehmi); 70. Gorongoza Gerbil (Gerbilliscus inclusus); 71. Setzer's Hairy-footed Gerbil (Gerbilliscus setzeri); 72. Dune Hairy-footed Gerbil (Gerbilliscus tytonis); 73. Brush-tailed Hairy-footed Gerbil (Gerbilliscus vallinus); 74. Paeba Hairyfooted Gerbil (Gerbilliscus paeba); 75. Highveld Gerbil (Gerbilliscus brantsii); 76. Cape Gerbil (Gerbilliscus afen; 77. Robbins's Tateril (Taterillus arenarius); 78. Senegal Tateril (Taterillus pygargus); 79. Petter's Tateril (Taterillus petteri); 80. Tranier's Tateril (Taterillus tranieri); 81. Gracile Tateril (Taterillus gracilis); 82. Congo Tateril (Taterillus congicus); 83. Lake Chad Tateril (Taterillus lacustris); 84. Emin's Tateril (Taterillus emini); 85. Cape Short-tailed Gerbil (Desmodillus auricularis). in Muridae
On following pages: 61. Giffard's Gerbil (Gerbilliscus giffardi); 62. Northern Savanna Gerbil (Gerbilliscus kempii); 63. Fringe-tailed Gerbil (Gerbilliscus robustus); 64. Phillips's Gerbil (Gerbilliscus phillipsi); 65. Black-tailed Gerbil (Gerbilliscus nigricaudus); 66. Southern Savanna Gerbil (Gerbilliscus validus); 67. Bushveld Gerbil (Gerbilliscus leucogaster); 68. East African Gerbil (Gerbilliscus vicinus); 69. Boehm's Gerbil (Gerbilliscus boehmi); 70. Gorongoza Gerbil (Gerbilliscus inclusus); 71. Setzer's Hairy-footed Gerbil (Gerbilliscus setzeri); 72. Dune Hairy-footed Gerbil (Gerbilliscus tytonis); 73. Brush-tailed Hairy-footed Gerbil (Gerbilliscus vallinus); 74. Paeba Hairyfooted Gerbil (Gerbilliscus paeba); 75. Highveld Gerbil (Gerbilliscus brantsii); 76. Cape Gerbil (Gerbilliscus afen; 77. Robbins's Tateril (Taterillus arenarius); 78. Senegal Tateril (Taterillus pygargus); 79. Petter's Tateril (Taterillus petteri); 80. Tranier's Tateril (Taterillus tranieri); 81. Gracile Tateril (Taterillus gracilis); 82. Congo Tateril (Taterillus congicus); 83. Lake Chad Tateril (Taterillus lacustris); 84. Emin's Tateril (Taterillus emini); 85. Cape Short-tailed Gerbil (Desmodillus auricularis).
Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final 'consensus' dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research.
Livestock as vectors of organic matter and nutrient loading in aquatic ecosystems in African savannas
<p>Populations of large wildlife have declined in many landscapes around the world, and have been replaced or displaced by livestock. The consequences of these changes on the transfer of organic matter (OM) and nutrients from terrestrial to aquatic ecosystems are not well understood. We used behavioural data, excretion and egestion rates and C: N: P stoichiometry of dung and urine of zebu cattle, to develop a metabolism-based estimate of loading rates of OM (dung), C, N and P into the Mara River, Kenya. We also directly measured the deposition of OM and urine by cattle into the river during watering. Per head, zebu cattle excrete and/or egest 25.6 g dry matter (DM, 99.6 g wet mass; metabolism) - 27.7 g DM (direct input) of OM, 16.0-21.8 g C, 5.9-9.6 g N, and 0.3-0.5 g P per day into the river. To replace loading rates OM of an individual hippopotamus by cattle, around 100 individuals will be needed, but much less for different elements. In parts of the investigated sub-catchments loading rates by cattle were equivalent to or higher than that of the hippopotamus. Changing these patterns of OM and nutrients transport and cycling are having significant effects on the structure and functioning of both terrestrial and aquatic ecosystems.</p>
High-intensity fires may have limited medium-term effectiveness for reversing woody plant encroachment in an African savanna
<p>1. Woody thickening or "bush encroachment" is a growing concern in savannas worldwide and can reportedly be reversed by applying high-intensity fires. Preliminary findings following experimental fires in 2010 and 2013 indicated that woody plant cover declined one year after high-intensity fires, but increased after low-intensity fires. However, the longer-term outcomes of high-intensity fires are largely unknown.</p> <p>2. To establish longer-term outcomes, we re-assessed sites subjected to Low, Medium and High-intensity fire treatments 10 years after the initial experimental fires. We compared woody vegetation structure in 2010 with that in 2020 using both ground surveys and airborne LiDAR.</p> <p>3. Ground surveys revealed increases in the number of stems and individual shrubs (< 10 m tall) over 10 years, and decreases in shrub height, with no significant differences between treatments. Large trees (≥ 10 m) declined by about 65% in number due to ongoing high mortality across treatments over 10 years.</p> <p>4. LiDAR surveys revealed significant but very small differences in woody plant height and cover between treatments after 10 years. Median height was around 2 m in all treatments, and 90<sup>th</sup> percentile tree height was moderately taller in the Low fire treatment. Mean canopy cover was about 55% in all treatments. The treatments, therefore, did not result in a meaningful reversal of woody encroachment with no discernible difference between the treatment sites after 10 years.</p> <p>5. <em>Synthesis and applications</em>. The application of high-intensity fires did not reverse woody encroachment in the longer term. In addition, the application of such treatments would be impractical at a large scale. Within a framework of Strategic Adaptive Management, the next logical step would be to attempt a different approach. In this case, it is intended to use early or late wet season burns to increase the mortality of shrubs when they are in a more vulnerable phenological state. This study illustrates the importance of ongoing long-term monitoring, review and adaptation for finding practical ways to achieve desired ecological outcomes.</p>
Data from: Postwar wildlife recovery in an African savanna: Evaluating patterns and drivers of species occupancy and richness
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Data from: Demographic legacies of fire history in an African savanna
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Data from: Strong but opposing effects of associational resistance and susceptibility on defense phenotype in an African savanna plant
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Data from: Woody encroachment over 70 years in South African savannas: overgrazing, global change or extinction aftershock?
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