ArticlePDF Available

Vegetation type conservation targets, status and level of protection in KwaZulu-Natal in 2016

Authors:
  • Independent Researcher

Abstract and Figures

Background: Systematic conservation planning aims to ensure representivity and persistence of biodiversity. Quantitative targets set to meet these aims provide a yardstick with which to measure the current conservation status of biodiversity features and measure the success of conservation actions. Objectives: The conservation targets and current ecosystem status of vegetation types and biomes occurring in KwaZulu-Natal (KZN) were assessed, and their level of formal protection was determined, to inform conservation planning initiatives in the province. Method: Land cover maps of the province were used to determine the amount of natural habitat remaining in KZN. This was intersected with the vegetation map and assessed relative to their conservation targets to determine the ecosystem status of each vegetation type in KZN. The proclaimed protected areas were used to determine the level of protection of each vegetation type. Results: In 17 years (1994–2011), 19.7% of natural habitat was lost to anthropogenic conversion of the landscape. The Indian Ocean Coastal Belt and Grassland biomes had the least remaining natural habitat, the highest rates of habitat loss and the least degree of formal protection. Conclusion: These findings inform conservation priorities in the province. Vegetation type targets need to be revised to ensure long-term persistence. Business-as-usual is no longer an option if we are to meet the legislative requirements and mandates to conserve the environment for current and future generations.
Content may be subject to copyright.
hp://www.abcjournal.org Open Access
Bothalia - African Biodiversity & Conservaon
ISSN: (Online) 2311-9284, (Print) 0006-8241
Page 1 of 10 Original Research
Read online:
Scan this QR
code with your
smart phone or
mobile device
to read online.
Author:
Debbie Jewi1,2
Aliaons:
1Ezemvelo KZN Wildlife,
Biodiversity Research and
Assessment, South Africa
2School of Animal, Plant and
Environmental Sciences,
University of the
Witwatersrand, South Africa
Corresponding author:
Debbie Jewi,
debbie.jewi@kznwildlife.com
Dates:
Received: 21 Aug. 2017
Accepted: 03 Mar. 2018
Published: 09 May 2018
How to cite this arcle:
Jewi, D., 2018, ‘Vegetaon
type conservaon targets,
status and level of protecon
in KwaZulu-Natal in 2016’,
Bothalia 48(1), a2294.
hps://doi.org/10.4102/
abc.v48i1.2294
Copyright:
© 2018. The Authors.
Licensee: AOSIS. This work
is licensed under the
Creave Commons
Aribuon License.
Introducon
Systematic conservation planning is used globally to identify priorities for biodiversity
conservation and inform policy and legislation to facilitate the long-term conservation of
biodiversity (Pressey et al. 2007). Conservation planning requires planning for whole landscapes,
ensuring both representivity and persistence of species, habitat types, ecosystems and the
processes that maintain and create diversity (Margules & Pressey 2000). A critical component
of the planning process is to set quantitative targets for biodiversity features or conservation
goals. Targets reflect the conservation value of existing protected areas, inform the selection of
additional areas to meet conservation goals (Margules & Pressey 2000), measure the success
of conservation actions (Desmet & Cowling 2004) and allow for accountability and defensibility
of conservation decisions.
In South Africa (SA), vegetation types are used as higher order biodiversity feature surrogates
for species and ecosystems (Lombard et al. 2003). This coarse-filter approach covers the entire
landscape and reduces the spatial and taxonomic bias associated with species data (Lombard
et al. 2003; Margules & Pressey 2000). Whilst vegetation types have been found to be good
surrogates for arthropods (Schaffers et al. 2008), they are not good surrogates for specialised
habitat or range-restricted species, rare or threatened species and vertebrates (Lombard et al.
2003). Using vegetation types in conservation planning is therefore complementary to species
data and may fill a gap where species data are scarce.
Plant communities or vegetation types underpin trophic structure and functioning (Jewitt et al.
2015a) and sequester nutrients in most ecosystems (Giam et al. 2010). These habitats support
essential ecological processes and provide ecosystem services, materials and food critical for
human well-being (Giam et al. 2010). However, habitat loss and land cover change are currently
the leading cause of biodiversity loss worldwide (Jetz, Wilcove & Dobson 2007; MEA 2005;
Vitousek 1994). Indeed, in KwaZulu-Natal (KZN), SA, 7.6% (721 733 ha) of natural habitat was
Background: Systematic conservation planning aims to ensure representivity and persistence
of biodiversity. Quantitative targets set to meet these aims provide a yardstick with which to
measure the current conservation status of biodiversity features and measure the success of
conservation actions.
Objectives: The conservation targets and current ecosystem status of vegetation types and
biomes occurring in KwaZulu-Natal (KZN) were assessed, and their level of formal protection
was determined, to inform conservation planning initiatives in the province.
Method: Land cover maps of the province were used to determine the amount of natural
habitat remaining in KZN. This was intersected with the vegetation map and assessed relative
to their conservation targets to determine the ecosystem status of each vegetation type in KZN.
The proclaimed protected areas were used to determine the level of protection of each
vegetation type.
Results: In 17 years (1994–2011), 19.7% of natural habitat was lost to anthropogenic conversion
of the landscape. The Indian Ocean Coastal Belt and Grassland biomes had the least remaining
natural habitat, the highest rates of habitat loss and the least degree of formal protection.
Conclusion: These findings inform conservation priorities in the province. Vegetation type
targets need to be revised to ensure long-term persistence. Business-as-usual is no longer an
option if we are to meet the legislative requirements and mandates to conserve the environment
for current and future generations.
Vegetaon type conservaon targets, status and level
of protecon in KwaZulu-Natal in 2016
Read online:
Scan this QR
code with your
smart phone or
mobile device
to read online.
Page 2 of 10 Original Research
hp://www.abcjournal.org Open Access
lost to anthropogenic conversion in only 6 years (Jewitt et al.
2015b). Hence, there is an urgent need to assess the impact of
habitat loss on vegetation types in KZN.
This article assesses the status of vegetation types and biomes
in KZN based on two standardised quantitative indicators
used in SA: ecosystem status (Driver et al. 2012) that compares
the amount of a vegetation type remaining in a natural state
to thresholds of conservation concern based on conservation
targets; and levels that assess how much of each vegetation
target is achieved in protected areas.
Research method and design
Study site
KwaZulu-Natal is a province on the east coast of SA. It has
high levels of biodiversity and forms part of the Maputaland–
Pondoland–Albany biodiversity hot spot with several
centres of endemism [Maputaland, Pondoland (Mucina et al.
2006b), Midlands and Drakensberg Alpine (Mucina et al.
2006a)]. The KZN vegetation map provides greater detail on
vegetation types and is mapped at a finer scale than the
national vegetation map of Mucina and Rutherford (2006)
and was used in this analysis. There are 101 vegetation types
and subtypes (EKZNW 2011a) in the province and five
biomes are recognised [Grassland, Savanna, Indian Ocean
Coastal Belt (IOCB), Forests and Wetlands (azonal)]. Their
historical extents are 4 583 855 ha, 3 259 341 ha, 891 092 ha,
202 879 ha and 393 628 ha, respectively (Figures 1 and 2a).
The forest coverage reflects a more current extent, as their
historical extents could not be accurately mapped. Zonal
and azonal groups are recognised within the forest biome
and wetlands are considered azonal. The provincial biome
classification includes wetlands as a biome, which differs
from the Mucina and Rutherford definition of a biome
(Rutherford, Mucina & Powrie 2006). Wetlands form a major
part of the landscape in KZN and have distinct floristic
communities and were therefore included as a biome in this
analysis.
Input data: Land cover
Five different land cover maps were used to determine the
extent of habitat conversion (non-natural categories) in
KZN. The 1994 (Fairbanks et al. 2000) and 2000 (Van den
Berg et al. 2008) land cover maps were national maps, whilst
the 2005 (EKZNW 2011b; GTI 2008), 2008 (EKZNW 2013a;
GTI 2010) and 2011 (EKZNW 2013b; EKZNW & GTI 2013)
land cover maps were provincial maps developed by
Ezemvelo KZN Wildlife. Based on a systematic land cover
change analysis for KZN (Jewitt et al. 2015b), which
demonstrated the extensive categorical swopping between
land cover categories, anthropogenic habitat conversion that
occurred in the province was accumulated, that is, a non-
natural category was not permitted to become a natural
category at some future point in time. This was done
specifically to identify primary natural vegetation occurring
in the province rather than secondary natural vegetation,
which does not harbour the same level of biodiversity as
primary natural habitat (Walters, Kotze & O’Connor 2006).
The land cover maps were projected, clipped to the 2008
vegetation extent to exclude the dynamic coastal rock and
sand category and clipped to the 2010 provincial boundary
(EKZNW 2010). Minor corrections were made to known
errors in the land cover maps. To determine the amount of
natural habitat remaining, two categories were created
across the five land cover maps, namely natural vegetation
and features (untransformed) and non-natural vegetation
(transformed or anthropogenic features such as the built
environment, cropped agriculture, timber plantations, dams
and mines). These were intersected with the vegetation types
and biomes to determine their degree of transformation or
habitat loss.
Input data: Conservaon targets for vegetaon
types
The conservation targets were a combination of the national
targets used in the national protected area expansion strategy
(Government of South Africa 2009), EKZNW vegetation
targets (Jewitt 2009), forest targets (Berliner 2005) and the
vegetation targets in Mucina and Rutherford (2006), using
the higher target where applicable. The conservation targets
for the non-forest vegetation types were determined using
the species-area method developed by Desmet and Cowling
(2004). The forest targets follow the method of Berliner
(2005) where a baseline of 15% was adjusted upwards
dependent on species diversity, rarity, patch fragmentation,
historic reduction and location within regions or centres of
endemism based on expert consultation.
FIGURE 1: (a) The amount of natural habitat remaining per me period in the
larger grassland and savanna biomes. (b) The amount of natural habitat
remaining per me period in the Indian Ocean Coastal Belt (IOCB), Wetland and
Forest biomes.
a
b
Wetlands
IOCB
Forests
5 000
4 500
4 000
3 500
3 000
2 500
2 000
1 500
1 000
500
0
Period
Area (ha × 103)
20001994 2005 2008 2011Original
extent
1 000
900
800
700
600
500
400
300
200
100
0
Period
Area (ha × 103)
20001994 2005 2008 2011Original
extent
Grasslands
Savanna
Page 3 of 10 Original Research
hp://www.abcjournal.org Open Access
FIGURE 2: (a) The biomes of KwaZulu-Natal (KZN), (b) the remaining natural habitat in KZN in 2011, (c) the ecosystem status of vegetaon types in 2011 and (d) the level
of protecon of vegetaon types (January 2016) with Protected Areas shown in red.
a
c d
b
Legend
Forest
Grassland
Indian ocean coastal belt
Savanna
Wetland
N
01020406080
Kilometers
N
N N
01020406080
Kilometers
01020406080
Kilometers 01020406080
Kilometers
Legend
Fully protected (>= biodiversity target)
Moderately protected
(>= 10% - biodiversity target)
Poorly protected (>= 1% – < 10%)
Nominally protected (> 0% – < 1%)
Not protected (0%)
Protected areas
Legend
Crically endangered remaining
natural habitat <= biodiversity target
Endangered remaining
natural habitat <= (biodiversity
target +15%)
Vulnerable remaining
natural habitat <= 60% of original
area of ecosystem
Least threatened remaining
natural habitat > 60% of original
area of ecosystem
Legend
Natural habitat remaining
Transformed
Page 4 of 10 Original Research
hp://www.abcjournal.org Open Access
Input data: Vegetaon map
The provincial vegetation map of KZN was used in this
analysis (EKZNW 2011a). It is mapped at a finer scale than
the national vegetation map (Mucina & Rutherford 2006).
The vegetation map was clipped with the provincial
boundary (EKZNW 2010).
Input data: Protected Areas map
The provincial Protected Areas from 2015 (EKZNW 2015)
and proclaimed Stewardship sites (National Environmental
Management: Protected Areas Act [NEM:PA] 57 of 2003) as at
January 2016 (EKZNW 2016) were used to determine the
level of protection for the vegetation types. The Department
of Environmental Affairs maintains a register of the country’s
conservation estate (the South African Protected Areas
Database [SAPAD]). The Protected Areas map used here
differs slightly from the SAPAD map as there is a lag period
between the provincial Protected Area proclamation and
updating of the Surveyor General cadastres and SAPAD at a
national level. Game farms and municipal reserves were not
included unless proclaimed under NEM:PA.
Analysis
The land cover, vegetation map, conservation targets and
protected areas map were used to calculate ecosystem
status and levels of protection as described in the National
Biodiversity Assessment (Driver et al. 2012). The remaining
natural habitat and conservation targets informed the
conservation or ecosystem status of the vegetation types.
Thresholds of concern are defined as follows: Critically
Endangered ( biodiversity target), Endangered (
biodiversity target + 15%), Vulnerable ( 60%) and Least
Threatened (> 60%). The threshold for Critically Endangered
is based on the vegetation type conservation target
described above. Below this threshold, the basic species
representation target cannot be achieved.
The level of protection represents the area of a vegetation
type within protected areas relative to the conservation
target. In SA, conservation targets are the target for the
amount of each vegetation type that should be represented
within public and private proclaimed protected areas. The
levels of protection thresholds of concern are defined as
follows: Fully Protected ( biodiversity target), Moderately
Protected ( 10% biodiversity target), Poorly Protected
( 1% – < 10%), Nominally Protected (0% – < 1%) and Not
Protected (0%).
Notes on the analysis
Habitat patches smaller than 4 ha were removed with the
exclusion of naturally fragmented vegetation types such as
forests and wetlands, as well as Drakensberg–Amathole
Afromontane Fynbos, Drakensberg Afroalpine Heathland,
Basotho Montane Shrubland and Lebombo Summit Sourveld.
Small patches were considered unable to support the natural
processes that create and maintain biodiversity, for example
fire. In addition, these small patches have an increased
vulnerability to stochastic events, suffer from edge effects
and increased disturbances (Doherty, Kearns & Barnett 2000),
limiting their long-term persistence.
Results
Between 1994 and 2011 (17 years), 19.7% of natural habitat
was converted to non-natural land classes, representing an
average annual loss of 1.2% (109 906 ha per annum) and a
decline from 73.3% to 53.6% remaining natural. Of the 53.5%
remaining natural in 2011 (Figure 2b), 7.35% was considered
degraded (in terms of aerial cover as detected from satellite
imagery). These degraded areas do not support the full
complement of biodiversity features.
The degree of habitat loss varied across vegetation types and
biomes, as did the conservation targets that ranged between
19% and 31.3% for non-forest targets and 61.6% and 100%
for forest targets. The resulting conservation status of the
vegetation types are: 21 (20.8%) vegetation types are
Critically Endangered, 14 (13.9%) are Endangered, 17
(16.8%) are Vulnerable and 49 (48.5%) Least Threatened
(Table 1, Figure 2c).
The IOCB had the least remaining natural vegetation
(24.9%) as of 2011, followed by grasslands (50.3%), wetlands
(58.7%), savannas (63.7%) and forests (73.9%) (Figure 1).
Similarly, the average annual rates of habitat loss in the
biomes between 1994 and 2011 were 2.9%, 1.7%, 1.3%, 1.3%
and 0.9% in the IOCB, grasslands, wetlands, savannas and
forests, respectively.
At a landscape scale, 9.1% of the terrestrial landscape is
protected. The degree of protection (Figure 2d) within the
biomes (Table 2) varies significantly, with only 6.8% of
grasslands protected, 8.2% of the IOCB protected, 9% of the
savannas protected, 24.6% of wetlands protected and 40.2%
of forests protected.
Compared to the national listed threatened ecosystems,
this analysis identifies additional vegetation types that
TABLE 1: The number of KwaZulu-Natal vegetaon types summarised by their conservaon status per biome.
Biome Crically Endangered Endangered Vulnerable Least Threatened
Forests 11 50 7
Wetlands 4 2 515
Savanna 2 1 4 12
Indian Ocean Coastal Belt (IOCB) 2 2 1 1
Grassland 2 4 7 14
Total 21 14 17 49
Page 5 of 10 Original Research
hp://www.abcjournal.org Open Access
TABLE 2: The number of KwaZulu-Natal vegetaon types summarised by their protecon status per biome.
Biome Fully Protected Moderately Protected Poorly Protected Nominally Protected Not Protected
Forests 3 17 2 1 0
Wetlands 13 4 7 0 2
Savanna 4 4 3 53
Indian Ocean Coastal Belt (IOCB) 2 2 1 1 0
Grassland 6 1 11 3 6
Total 28 28 24 10 11
TABLE 3: KwaZulu-Natal (KZN) vegetaon type conservaon targets, extents, ecosystem status and level of protecon based on 2011 accumulated transformaon stascs
and protected area (PA) proclamaon as at January 2016.
Code KZN vegetaon-type name KZN biome Conservaon
target (%)
Original extent
(ha)
Remaining
natural (ha)
Remaining natural
less fragments (ha)
Ecosystem
status
Total PA
(ha)
Level of
protecon
1 Drakensberg-Amathole Afromontane Fynbos Grassland27§1427 1425 1425 LT 1020 FP
2 Amersfoort Highveld Clay Grassland Grassland 27§13 253 8493 8412 LT 0 N
3 Drakensberg Afroalpine Heathland Grassland 27§6410 6354 6354 LT 5522 FP
4 Drakensberg Foothill Moist Grassland Grassland 23§360 071 223 583 221 516 LT 29 285 PP
5Basotho Montane Shrubland Grassland 28§2760 2483 2483 LT 0 N
6 Dry Coast Hinterland Grassland Savanna 25276 406 125 199 122 677 V 1950 NP
7 East Griqualand Grassland Grassland 23§134 232 67 256 66 360 V 366 NP
8 Eastern Free State Sandy Grassland Grassland 24§4119 3758 3729 LT 0 N
10 Income Sandy Grassland Grassland 23§437 810 198 948 194 765 V 0 N
11 Ithala Quartzite Sourveld Grassland 27§82 024 67 675 67 261 LT 11 159 MP
12 KaNgwane Montane Grassland Grassland 24§8265 2352 2228 E0 N
13 KwaZulu-Natal Sandstone Sourveld Grassland 25§179 668 19 954 17 978 CE 194 NP
14 Lebombo Summit Sourveld Grassland 24§11 763 3260 3260 E172 PP
15 Lesotho Highland Basalt Grassland Grassland 27§1134 1120 1103 LT 898 FP
16 Low Escarpment Moist Grassland Grassland 23§134 083 117 759 117 463 LT 3547 PP
17 Mabela Sandy Grassland Grassland 23§440 25 12 CE 0 N
18 Maputaland Wooded Grassland IOCB 25§107 929 39 643 39 172 E19 109 MP
19 Maputaland Coastal Belt IOCB 25§221 194 78 535 76 799 E37 176 MP
20 Midlands Mistbelt Grassland Grassland 23§547 445 130 599 126 355 E13 697 PP
21 Moist Coast Hinterland Grassland Grassland 25437 556 157 573 153 031 E873 NP
22 Mooi River Highland Grassland Grassland 23§266 938 144 071 142 047 V 13 719 PP
24 Northern Drakensberg Highland Grassland Grassland 27§70 706 69 096 69 044 LT 38 473 FP
25 Northern KwaZulu-Natal Moist Grassland Grassland 24§696 920 391 958 387 698 V 10 854 PP
26 Northern Zululand Mistbelt Grassland Grassland 23§52 896 22 594 22 251 V 931 PP
27 Paulpietersburg Moist Grassland Grassland 24§284 058 120 957 118 688 V 8420 PP
28 Pondoland-Ugu Sandstone Coastal Sourveld IOCB 30.3§§ 37 245 7165 6773 CE 2247 PP
29 KwaZulu-Natal Coastal Belt Grassland IOCB 25§411 500 45 543 40 613 CE 3890 NP
30 Southern Drakensberg Highland Grassland Grassland 27§89 808 88 501 88 471 LT 57 719 FP
31 Southern KwaZulu-Natal Moist Grassland Grassland 23§231 823 96 778 94 713 V 9800 PP
32 uKhahlamba Basalt Grassland Grassland 27§120 155 119 924 119 905 LT 106 550 FP
Table 3 connues on the next page →
are listed as Critically Endangered (e.g. Zululand Coastal
Thornveld, Alluvial wetlands and Lowveld Riverine Forest)
(Table 3). Similarly, a far greater proportion of vegetation
types are listed as Vulnerable.
Discussion
We present the targets, remaining natural habitat,
conservation and protection status of vegetation types and
biomes in KZN. Only 46.2% of the province remains in a
natural state once degraded areas are removed. This figure
is conservative considering the extensive alien invasive
plants that occur in KZN biomes (Van Wilgen et al. 2012).
Currently, alien invasive plants are not detected and
mapped on the land cover maps because of the scale and
resolution at which the land covers are mapped. Further, it
is not always possible to detect secondary vegetation, for
example from abandoned agricultural fields, on satellite
imagery. A further 7% of the landscape that is mapped as
natural vegetation on the land cover maps is estimated to
Page 6 of 10 Original Research
hp://www.abcjournal.org Open Access
TABLE 3 (Connues...): KwaZulu-Natal (KZN) vegetaon type conservaon targets, extents, ecosystem status and level of protecon based on 2011 accumulated
transformaon stascs and protected area (PA) proclamaon as at January 2016.
Code KZN vegetaon-type name KZN biome Conservaon
target (%)
Original extent
(ha)
Remaining
natural (ha)
Remaining natural
less fragments (ha)
Ecosystem
status
Total PA
(ha)
Level of
protecon
33 Wakkerstroom Montane Grassland Grassland 27§131 688 113 395 113 070 LT 4123 PP
34 Delagoa Lowveld Savanna 19§8770 1084 1069 CE 0 N
35 Eastern Valley Bushveld Savanna 25§313 748 211 707 210 176 LT 906 NP
36 Granite Lowveld Savanna 19§3656 1228 1188 E0 N
37 KwaZulu-Natal Highland Thornveld Grassland 23§500 487 307 803 303 496 LT 9073 PP
38 KwaZulu-Natal Hinterland Thornveld Savanna 25§152 542 99 029 97 918 LT 740 NP
39 Makani Clay Thicket Savanna 19§32 327 26 671 26 415 LT 12 760 FP
40.1 Maputaland Pallid Sandy Bushveld Savanna 25§§ 61 429 46 460 46 074 LT 9815 MP
40.2 Muzi Palm Veld and Wooded Grassland Savanna 2552 931 41 211 40 744 LT 3535 PP
41 KwaZulu-Natal Coastal Belt Thornveld Savanna 25111 926 49 582 48 218 V 611 NP
42 Northern Zululand Sourveld Savanna 19§470 422 306 996 304 135 LT 34 585 PP
44 Southern Lebombo Bushveld Savanna 24§116 567 97 350 96 830 LT 11 972 MP
45 Swaziland Sour Bushveld Savanna 19§50 517 42 378 42 161 LT 12 009 FP
47 Tembe Sandy Bushveld Savanna 19§110 678 85 880 85 139 LT 17 707 MP
48 Thukela Thornveld Savanna 25§215 907 163 740 162 188 LT 6580 PP
49 Thukela Valley Bushveld Savanna 25§268 482 191 381 189 374 LT 1255 NP
50 Western Maputaland Clay Bushveld Savanna 19§152 693 57 032 54 458 V 31 248 FP
51 Western Maputaland Sandy Bushveld Savanna 19§15 132 9895 9664 LT 2819 MP
52 Zululand Coastal Thornveld Savanna 19§67 137 11 181 10 630 CE 0 N
53 Zululand Lowveld Savanna 19§665 917 375 813 372 083 V 135 475 FP
55 Subtropical Coastal Lagoons: Estuary Azonal
Wetland
24§40 090 39 188 39 188 LT 35 224 FP
57 Drakensberg Montane Forests Forest 63.5†† 6393 6077 6077 LT 3665 MP
59 Eastern Mistbelt Forests Forest 66.5†† 44 474 29 933 29 933 E8127 MP
60.1 Eastern Scarp Forests: Ngome-Nkandla Scarp
Forest
Forest 61.6†† 8593 3785 3785 CE 2911 MP
60.2 Eastern Scarp Forests: Northern Coastal
Scarp Forest
Forest 61.6†† 5632 4408 4408 LT 3693 FP
60.3 Eastern Scarp Forests: Northern Zululand
Lebombo Scarp Forest
Forest 61.6†† 7656 6785 6785 LT 3418 MP
60.4 Eastern Scarp Forests: Southern Coastal
Scarp Forest
Forest 61.6†† 11 378 8804 8804 LT 570 PP
61 Pondoland Scarp Forests Forest 61.6†† 4889 3998 3998 LT 2015 MP
62.1 KwaZulu-Natal Coastal Forests: Dukuduku
Moist Coastal Lowlands Forest
Forest 71.7†† 8478 5781 5781 CE 7283 FP
62.2 KwaZulu-Natal Coastal Forests: Maputaland
Dry Coastal Lowlands Forest
Forest 71.7†† 2406 2053 2053 E1440 MP
62.3 KwaZulu-Natal Coastal Forests: Maputaland
Mesic Coastal Lowlands Forest
Forest 71.7†† 8962 7218 7218 E5814 MP
62.4 KwaZulu-Natal Coastal Forests: Maputaland
Moist Coastal Lowlands Forest
Forest 71.7†† 13 655 10 833 10 833 E8491 MP
62.5 KwaZulu-Natal Coastal Forests: Southern
Mesic Coastal Lowlands Forest
Forest 71.7†† 10 705 5925 5925 CE 1415 MP
62.6 KwaZulu-Natal Coastal Forests: Southern
Moist Coastal Lowlands Forest
Forest 71.7†† 3174 1600 1600 CE 280 PP
63.1 KwaZulu-Natal Dune Forests: East Coast Dune
Forest
Forest 69.2†† 2497 1313 1313 CE 451 MP
63.2 KwaZulu-Natal Dune Forests: Maputaland
Dune Forest
Forest 69.2†† 16 390 13 051 13 051 E10 898 MP
64.1 Licua Sand Forests: Eastern Sand Forest Forest 69†† 25 478 23 461 23 461 LT 10 143 MP
64.2 Licua Sand Forests: Western Sand Forest Forest 69†† 909 903 903 LT 870 FP
65 Lowveld Riverine Forests Azonal Forest 100†† 10 039 6134 6134 CE 4592 MP
66.1 Swamp Forests: Barringtonia Swamp Forest Azonal Forest 100†† 94 47 47 CE 47 MP
66.2 Swamp Forests: Ficus trichopoda Swamp
Forest
Azonal Forest 100†† 7722 5156 5156 CE 3570 MP
66.3 Swamp Forests: Raphia Swamp Forest Azonal Forest 100†† 370 172 172 CE 68 MP
Table 3 connues on the next page →
Page 7 of 10 Original Research
hp://www.abcjournal.org Open Access
be historical agricultural fields (circa 1960/1970), which are
depauperate in their species complement especially in
terms of specialised species and geophytic plants (Jewitt
et al. 2017). Hence, estimates of natural habitat remaining
are conservative. It is therefore essential that high diversity,
primary natural vegetation sites are identified and secured
TABLE 3 (Connues...): KwaZulu-Natal (KZN) vegetaon type conservaon targets, extents, ecosystem status and level of protecon based on 2011 accumulated
transformaon stascs and protected area (PA) proclamaon as at January 2016.
Code KZN vegetaon-type name KZN biome Conservaon
target (%)
Original extent
(ha)
Remaining
natural (ha)
Remaining natural
less fragments (ha)
Ecosystem
status
Total PA
(ha)
Level of
protecon
66.4 Swamp Forests: Voacanga thouarsii Swamp
Forest
Azonal Forest 100†† 462 36 36 CE 2 NP
67 Mangrove Forests Azonal Forest 100†† 2522 2382 2382 CE 1798 MP
68 Subtropical Seashore Vegetaon IOCB 20§52 42 23 V 23 FP
69 Subtropical Dune Thicket IOCB 20§1245 1195 1188 LT 1083 FP
70.1 Freshwater Wetlands: Drakensberg Wetlands Azonal
Wetland
24§5759 4256 4256 LT 2405 FP
70.2 Freshwater Wetlands: Lesotho Mires Azonal
Wetland
24§1 1 1 LT 1FP
72.1 Freshwater Wetlands: Eastern Temperate
Wetlands
Azonal
Wetland
24§44 743 24 702 24 702 V 502 PP
72.2 Freshwater Wetlands: Eastern Temperate
Wetlands: Lakes & Pans
Azonal
Wetland
24§41 35 35 LT 10 FP
75.1 Alluvial Wetlands: Subtropical Alluvial
Vegetaon
Azonal
Wetland
31§17 088 5805 5805 E1478 PP
75.3 Alluvial Wetlands: Subtropical Alluvial
Vegetaon: Lowveld Floodplain Grasslands
Azonal
Wetland
31§22 957 6078 6078 CE 3038 MP
75.4 Alluvial Wetlands: Subtropical Alluvial
Vegetaon: Lowveld Floodplain Grasslands:
Tall Reed Wetland
Azonal
Wetland
31§2535 1424 1424 V 753 MP
75.5 Alluvial Wetlands: Subtropical Alluvial
Vegetaon: Lowveld Floodplain Grasslands:
Short Grass/Sedge Wetland
Azonal
Wetland
31§7612 2087 2087 CE 434 PP
76.1 Freshwater Wetlands: Subtropical
Freshwater Wetlands
Azonal
Wetland
24§13 949 6260 6260 V 2129 MP
76.2 Freshwater Wetlands: Subtropical
Freshwater Wetlands: Tall Grassland/Sedge/
Reed Wetlands
Azonal
Wetland
24§14 809 14 442 14 442 LT 11 203 FP
76.3 Freshwater Wetlands: Subtropical
Freshwater Wetlands: Short Grass/Sedge
Wetlands
Azonal
Wetland
24§47 001 38 525 38 525 LT 15 182 FP
76.4 Freshwater Wetlands: Subtropical
Freshwater Wetlands: Short Grass/Sedge
Wetlands: Dune Slack
Azonal
Wetland
24§275 144 144 V 112 FP
76.5 Freshwater Wetlands: Subtropical
Freshwater Wetlands: Short Grass/Sedge
Wetlands: Coastal Plain Depression
Azonal
Wetland
24§782 649 649 LT 57 PP
76.7 Freshwater Wetlands: Subtropical
Freshwater Wetlands: Coastal Lakes & Pans
Azonal
Wetland
24§7595 7097 7097 LT 6166 FP
76.8 Freshwater Wetlands: Subtropical
Freshwater Wetlands: Coastal Lakes & Pans:
Endorheic
Azonal
Wetland
24§6999 6977 6977 LT 6247 FP
76.9 Freshwater Wetlands: Subtropical
Freshwater Wetlands: Coastal Lakes & Pans:
Lacustrine
Azonal
Wetland
24§1 0 CE 0 N
77.1 Inland Saline Wetlands: Subtropical Salt Pans Azonal
Wetland
24§2556 2277 2277 LT 1553 FP
77.2 Inland Saline Wetlands: Subtropical Salt
Pans: Floodplain Pans (Open)
Azonal
Wetland
24§2086 1731 1731 LT 1198 FP
77.3 Inland Saline Wetlands: Subtropical Salt
Pans: Rain fed (Endorheic) Pans (Closed)
Azonal
Wetland
24§538 328 328 LT 0 NP
78.1 Alluvial Wetlands: Temperate Alluvial
Vegetaon
Azonal
Wetland
24§§ 147 288 62 161 62 161 V 5604 PP
78.2 Alluvial Wetlands: Temperate Alluvial
Vegetaon: Midland Alluvial Woodland &
Thicket
Azonal
Wetland
24§§ 207 42 42 CE 18 PP
78.3 Alluvial Wetlands: Temperate Alluvial
Vegetaon: Midland Floodplain Grasslands
Azonal
Wetland
24§§ 1780 1228 1228 LT 274 MP
79.1 Marine Saline Wetlands Azonal
Wetland
24§§ 1761 427 427 E22 PP
79.2 Marine Saline Wetlands: Saline Reed & Sedge
Beds
Azonal
Wetland
24§§ 964 944 944 LT 942 FP
79.3 Marine Saline Wetlands: Saline Grassland &
Mud Flats
Azonal
Wetland
24§§ 4212 2912 2912 LT 2366 FP
, this vegetaon type has Fynbos anies but for the purposes of stascal reporng has been included in the Grassland biome.
Conservaon targets were based on ††, Berliner (2005); §, Government of South Africa (2009); , Mucina and Rutherford (2006); §§, Jewi (2009).
Ecosystem status abbreviaons are: CE, Crically Endangered: E, Endangered; V, Vulnerable; LT, Least Threatened.
Level of protecon abbreviaons are: N, Not Protected; NP, Nominally Protected; PP, Poorly Protected; MP, Moderately Protected; FP, Fully Protected.
Page 8 of 10 Original Research
hp://www.abcjournal.org Open Access
via Protected Area expansion and Stewardship
programmes. These sites need to be appropriately managed
to maintain their biodiversity value. High livestock stocking
rates, unsustainable indigenous resource harvesting and
alien invasive plant species are contributing to the
degradation of intact ecosystems and are a major concern
for the future.
The vegetation types occurring along the coast and the
midlands have the largest loss of natural habitat and are
thus the most threatened vegetation types in the province.
The IOCB and grassland biomes have the least amount of
natural habitat remaining and have the highest annual
rates of habitat loss. They also have the least amount of
formal protection. These vegetation types and biomes
require urgent conservation action. To ensure representivity,
each vegetation type should be adequately protected and
have the target amount of habitat formally protected. The
current distribution of the Protected Area network is
biased. Future Protected Areas should be created in
vegetation types without any protection or which are
nominally or poorly protected. The Drakensberg, Zululand
and Maputaland areas have a better Protected Area network
than north-western and south-eastern KZN. Rates of
habitat loss in the forest biome were the lowest but this
may reflect the more recent mapping extent of forests rather
than their actual habitat loss.
The indices reported here may help to inform land use
planning and Protected Area expansion by spatially
depicting vegetation types under greatest threat or requiring
Protected Area expansion. These maps may be used in
provincial conservation plans, spatial development
frameworks, Protected Area expansion strategies and other
land use planning initiatives. Whilst Protected Areas have
increased in extent since 1994, the rate of habitat loss is
continuing unsustainably, limiting the options to expand
the Protected Area network and increasing the threat status
of vegetation types. The rates of habitat loss have slowed
over successive time periods, but this could be related to the
sluggish economy (Jewitt et al. 2015b) or other factors and
could potentially increase in future.
Jewitt et al. (2015b) identified the dominant drivers of
transformation, or loss of natural habitat, as cultivated
agriculture, timber plantations, the built environment,
mining and dams. These represent the key sectors that
should be engaged with to guide appropriate land use
change. Rouget et al. (2003) recommend considering future
land use changes to identify future threats and enable the
search for alternative options. For instance, the Carbon Tax
Policy, scheduled to come into effect in 2017, may have a
significant effect on industries such as agriculture (Agri SA
Commodity Chamber 2017). This could have the advantage
of encouraging farmers to take up sustainable land
management practices or it could drive significant land
use changes in the agricultural landscape to remain
economically viable.
South Africa has good environmental legislation (e.g. the
Constitution of the Republic of South Africa and the National
Environmental Management Act 107 of 1998) and is also a
signatory to many different global conventions such as the
Convention on Biological Diversity (CBD). These demand
the conservation of landscapes, ecosystems and species for
current and future generations. The intentions of the
legislation and conventions are good, yet the loss of natural
habitat and species declines continue, resulting in the high
number of threatened ecosystems. A third of the vegetation
types in the province are Endangered or Critically
Endangered. The National List of Ecosystems that are
Threatened and in need of protection (Act No. 1002 of 2011)
was established to protect threatened ecosystems. This
analysis demonstrates that several ecosystems have since
attained a worse conservation status (based only on Criteria
A1 or loss of habitat). This analysis identifies 8.5% of KZN as
Critically Endangered compared to zero in the Threatened
Ecosystem legislation. Similarly, 15.5% is listed as
Endangered compared to 5% in the legislation. However, the
legislation only became effective in 2011, meaning that
future land cover maps will allow an assessment of the
efficacy of the Threatened Ecosystem legislation. If current
legislation, or perhaps the lack of implementation thereof, is
not sufficient to protect ecosystems and species, a new model
for conservation and sustainability must urgently be found.
Indeed, the calls for acknowledging and implementing what
is ultimately required to sustain life on the Earth are
increasing (Noss et al. 2012). It is recognised that humanity
is pushing ecosystems beyond their capacity to support life
and time is running out to change the current failing
trajectory (Ripple et al. 2017).
Targets
The targets used here may differ from national targets.
Differences may arise because of the phytosociological data
available at the time of the analysis, the differences between
calculated targets and extrapolated targets and the finer
scale of the provincial vegetation map compared to the
national vegetation map. Similarly, the conservation status
may differ because of revised vegetation boundaries at the
time of the analysis, dates of land cover maps used and
vegetation types that may extend beyond the boundary of
KZN compared to KZN endemic vegetation types. Processes
are in place to include finer scale mapping initiatives into the
national vegetation map, facilitating a hierarchical level of
mapping from broad scale to fine scale (Dayaram et al. 2017).
The targets provide an estimation of the area required to
represent a single occurrence of 75% of the plant species
occurring within the vegetation type (Desmet 2004). The
targets do not consider ecological processes. Hence, the targets
are conservative and will not ensure adequate representivity
or persistence of all species, but they represent an important
first step in securing representative habitats in the province.
Recent conservation plans based on composite sets of
biodiversity targets aimed at achieving biodiversity
persistence require 60%–65% of the area (Noss et al. 1999).
Page 9 of 10 Original Research
hp://www.abcjournal.org Open Access
It is well known that larger areas conserve more species
(Desmet & Cowling 2004) and are essential for ecological
resilience. The probability of species extinctions is less in larger
areas (Cumming 2011). Given climate change predictions,
larger areas that are more resilient to environmental
perturbations are critical. Noss et al. (2012) suggested that 50%
of landscapes should be managed in a conservation-friendly
manner so that species, populations and communities are
conserved into the future. Similarly, Soulé and Sanjayan (1998)
estimated that 50% of the landscape is required to maintain
functional integrity and ensure biological persistence. Flather
and Bevers (2002) found that there was a rapid decline in the
probability of landscapes supporting viable populations once
less than 50% of habitat remained. Plant pollination is
significantly negatively impacted once 50% of the habitat is
lost (Traveset et al. 2018). It is recommended that the current
vegetation type targets, both provincial and national, should
be revised to accommodate ecological and evolutionary
processes, ensure essential ecosystem services are provided,
maintain landscape connectivity and provide resilience to
climate change impacts and other threats to maintain
viable populations and ensure long-term persistence. It is
recommended that the targets should be closer to 50% (Locke
2013) – significantly higher than the current targets.
KwaZulu-Natal has less than the recommended target
amount of natural habitat remaining. As the province’s
ecological infrastructure is lost, an increasing proportion of
species extinctions can be expected. The long-term social
cost of losing this infrastructure is likely far greater than
the short-term cost of preventing further loss of natural
habitat in the landscape.
Conclusion
The evaluation of the conservation and protection status
of vegetation types in KZN informs conservation priorities
in the province. The rapid rate of habitat loss is creating
an urgency to protect the remaining natural habitat,
especially because the remaining primary, intact vegetation
is below the recommended target of 50%. Restoration efforts
are required in the Critically Endangered and Endangered
vegetation types. Awareness campaigns are required amongst
all stakeholders, highlighting the rapid loss of natural
habitat and the legislative need to protect the environment.
This would be enhanced by demonstrating the value and
benefits of the natural environment to society. Agreements
need to be secured amongst all government sectors to halt
further conversion of primary habitat and rather intensify
development on existing non-natural land. Business-as-
usual is no longer an option if we are to meet the legislative
requirements and mandates to conserve the environment
for current and future generations.
Acknowledgements
The author wishes to thank Ed Witkowski for reviewing the
draft manuscript and two anonymous reviewers and the editor
for their comments that helped improve the manuscript.
Compeng interests
The author declares that she has no financial or personal
relationships that may have influenced her in writing this
article.
References
Agri SA Commodity Chamber, 2017, ‘Implicaons of a carbon tax and oset system for
agriculture in South Africa’, A presentaon by Agri SA’s Commodity Chamber to
the Department of Environmental Aairs, The Department of Agriculture, Forestry
and Fisheries and Naonal Treasury, viewed 31 July 2017, from hp://www.
greenagri.org.za/blog/implications-of-a-carbon-tax-and-offset-system-for-
agriculture-in-south-africa/
Berliner, D., 2005, Systemac conservaon planning for the forest biome of South
Africa, Department of Water Aairs and Forestry, Pretoria, South Africa.
Cumming, G.S., 2011, ‘Spaal resilience: Integrang landscape ecology, resilience, and
sustainability’, Landscape Ecology 26, 899–909. hps://doi.org/10.1007/s10980-
011-9623-1
Dayaram, A., Powrie, L., Rebelo, T. & Skowno, A., 2017, ‘Vegetaon map of South
Africa, Lesotho and Swaziland 2009 and 2012: A descripon of changes from
2006’, Bothalia 47(1), a2223. hps://doi.org/10.4102/abc.v47i1.2223
Desmet, P., 2004, ‘Appendix B – Developing species representaon targets for South
African vegetaon types’, in M. Rouget, B. Reyers, Z. Jonas, P. Desmet, A. Driver, K.
Maze, et al. (eds.), South African Naonal Spaal Biodiversity Assessment 2004
technical report Vol 1 terrestrial component, pp. 94–110, South African Naonal
Biodiversity Instute, Pretoria.
Desmet, P. & Cowling, R.M., 2004, ‘Using the species-area relaonship to set baseline
targets for conservaon’, Ecology and Society 9(2), 11, viewed 28 January 2017,
from hp://www.ecologyandsociety.org/vol19/iss2/art11
Doherty, M., Kearns, A ., Barne, G., Sarre, A., Hochuli, D., Gibb, H. et al., 2000, The
interacon between habitat condions, ecosystem processes and terrestrial
biodiversity – A review, Australia: State of the Environment Second Technical
Paper Series (Biodiversity), Series 2, Department of the Environment and Heritage,
Canberra.
Driver, A., Sink, K.J., Nel, J.L., Holness, S., Van Niekerk, L., Daniels, F. et al., 2012,
Naonal biodiversity assessment 2011: An assessment of South Africa’s
biodiversity and ecosystems, Synthesis Report, South African Naonal Biodiversity
Instute and Department of Environmental Aairs, Pretoria.
Ezemvelo KZN Wildlife (EKZNW), 2010, KwaZulu-Natal provincial boundary.
(kznbnd10_w31.zip), GIS coverage, Conservaon Planning Division, Ezemvelo KZN
Wildlife, Pietermaritzburg.
Ezemvelo KZN Wildlife (EKZNW), 2011a, KwaZulu-Natal provincial pre-transformaon
vegetaon type map – 2011 (kznveg05v2_011_inhouse_w31.zip), GIS coverage,
Conservaon Planning Division, Ezemvelo KZN Wildlife, Pietermaritzburg.
Ezemvelo KZN Wildlife (EKZNW), 2011b, KwaZulu-Natal land cover 2005 v3.1, (clp_
KZN_2005_LC_v3_1_grid_w31.zip), GIS coverage, Biodiversity Conservaon
Planning Division, Ezemvelo KZN Wildlife, Pietermaritzburg.
Ezemvelo KZN Wildlife (EKZNW), 2013a, KwaZulu-Natal land cover 2008 v2 (clp_
KZN_2008_LC_v2_grid_w31.zip), GIS coverage, Biodiversity Research and
Assessment, Ezemvelo KZN Wildlife, Pietermaritzburg.
Ezemvelo KZN Wildlife (EKZNW), 2013b, KwaZulu-Natal land cover 2011 v1 (clp_
KZN_2011_LC_v1_grid_w31.zip), GIS coverage, Biodiversity Research and
Assessment, Ezemvelo KZN Wildlife, Pietermaritzburg.
Ezemvelo KZN Wildlife (EKZNW), 2015, EKZNW Protected Area boundaries 2015
(Ekznw_pabnd_2015_wdd_new.zip), GIS coverage, Biodiversity Spaal Planning
and Informaon, Ezemvelo KZN Wildlife, Pietermaritzburg.
Ezemvelo KZN Wildlife (EKZNW), 2016, EKZNW Stewardship boundaries 2016
(stewardship_wll_jan2016_dra.zip), GIS coverage, Biodiversity Spaal Planning
and Informaon, Ezemvelo KZN Wildlife, Pietermaritzburg.
Ezemvelo KZN Wildlife (EKZNW) & GeoTerraImage (GTI), 2013, 2011 KZN Province
land-cover mapping (from SPOT5 satellite imagery circa 2011): Data users report
and metadata (version 1d), unpublished report, Ezemvelo KZN Wildlife,
Pietermaritzburg.
Fairbanks, D.H.K., Thompson, M.W., Vink, D.E., Newby, T.S., Van den Berg, H.M. &
Everard, D.A., 2000, ‘The South African land-cover characteriscs database: A
synopsis of the landscape’, South African Journal of Science 96, 69–82.
Flather, C.H. & Bevers, M., 2002, ‘Patchy reacon-diusion and populaon abundance:
Relave importance of habitat amount and arrangement’, The American
Naturalist 159, 40–56.
GeoTerraImage (GTI), 2008, KZN Province land-cover mapping (from SPOT2/4 satellite
imagery circa 2005–06): Data users report and metadata (version 2), Unpublished
report, Ezemvelo KZN Wildlife, Pietermaritzburg.
GeoTerraImage (GTI), 2010, 2008 KZN Province land-cover mapping (from SPOT5
satellite imagery circa 2008): Data users report and metadata (version 1),
unpublished report, Ezemvelo KZN Wildlife, Pietermaritzburg.
Giam, X., Bradshaw, C.J.A., Tan, H.T.W. & Sodhi, N.S., 2010, ‘Future habitat loss and the
conservaon of plant biodiversity’, Biological Conservaon 143, 1594–1602.
hps://doi.org/10.1016/j.biocon.2010.04.019
Government of South Africa, 2009, Naonal Protected Area Expansion Strategy
for South Africa 2008. Priories for expanding the protected area network
for ecological sustainability and climate change adaptaon, Government of
South Africa, Pretoria, South Africa.
Page 10 of 10 Original Research
hp://www.abcjournal.org Open Access
Jetz, W., Wilcove, D.S. & Dobson, A.P., 2007, ‘Projected impacts of climate and land-
use change on the global biodiversity of birds’, PLoS Biology 5, 1211–1219,
hps://doi.org/10.1371/journal.pbio.0050157
Jewi, D., 2009, Conservaon targets for KZN vegetaon types in KwaZulu-
Natal, unpublished report, Biodiversity Division, Ezemvelo KZN Wildlife,
Pietermaritzburg.
Jewi, D., Erasmus, B.F.N., Goodman, P.S., O’Connor, T.G., Hargrove, W.W., Maddalena,
D.M. et al., 2015a, ‘Climate-induced change of environmentally dened orisc
domains: A conservaon based vulnerability framework’, Applied Geography 63,
33–42. hps://doi.org/10.1016/j.apgeog.2015.06.004
Jewi, D., Goodman, P.S., Erasmus, B.F.N., O’Connor, T.G. & Witkowski, E.T.F., 2015b,
‘Systemac land-cover change in KwaZulu-Natal, South Africa: Implicaons for
biodiversity’, South African Journal of Science 111(9/10), Art. #2015-0019, 1–9.
hps://doi.org/10.17159/sajs.2015/20150019
Jewi, D., Goodman, P.S., Erasmus, B.F.N., O’Connor, T.G. & Witkowski, E.T.F., 2017,
‘Planning for the maintenance of orisc diversity in the face of land cover and
climate change’, Environmental Management 59, 792–806. hps://doi.
org/10.1007/s00267-017-0829-0
Locke, H., 2013, ‘Nature needs half: A necessary and hopeful new agenda for
Protected Areas’, Parks 19, 9–18. hps://doi.org/10.2305/IUCN.CH.2013.
PARKS-19-2.HL.en
Lombard, A.T., Cowling, R.M., Pressey, R.L. & Rebelo, A.G., 2003, ‘Eecveness of land
classes as surrogates for species in conservaon planning for the Cape Florisc
Region’, Biological Conservaon 112, 45–62. hps://doi.org/10.1016/S0006-
3207(02)00422-6
Margules, C.R. & Pressey, R.L., 2000, ‘Systemac conservaon planning’, Nature 405,
243–253. hps://doi.org/10.1038/35012251
Millenium Ecosystem Assessment (MEA), 2005, Ecosystems and human well-being:
Biodiversity synthesis, World Resources Instute, Washington, DC.
Mucina, L., Hoare, D.B., Löer, M.C., Du Preez, P.J., Rutherford, M.C., Sco-Shaw,
C.R. et al., 2006a, ‘Grassland biome’, in L. Mucina & M.C. Rutherford (eds.), The
vegetaon of South Africa, Lesotho and Swaziland, pp. 348–437, SANBI,
Pretoria.
Mucina, L. & Rutherford, M.C., 2006, The vegetaon of South Africa, Lesotho and
Swaziland, South African Naonal Biodiversity Instute, Pretoria.
Mucina, L., Sco-Shaw, C.R., Rutherford, M.C., Camp, K.G.T., Mahews, W.S., Powrie,
L.W. et al., 2006b, ‘Indian Ocean coastal belt’, in L. Mucina & M.C. Rutherford
(eds.), The vegetaon of South Africa, Lesotho and Swaziland, pp. 568–583,
SANBI, Pretoria.
Noss, R.F., Dobson, A.P., Baldwin, R., Beier, P., Davis, C.R., Dellasala, D.A. et al., 2012,
‘Bolder thinking for conservaon’, Conservaon Biology 26, 1–4. hps://doi.
org/10.1111/j.1523-1739.2011.01738.x
Noss, R.F., Striholt, J.R., Vance-Borland, K. & Frost, P., 1999, ‘A conservaon plan for
the Klamath-Siskiyou ecoregion’, Natural Areas Journal 19, 392–411.
Pressey, R.L., Cabeza, M., Was, M.E., Cowling, R.M. & Wilson, K.A., 2007,
‘Conservaon planning in a changing world’, Trends in Ecology and Evoluon 22,
583–592. hps://doi.org/10.1016/j.tree.2007.10.001
Ripple, W.J., Wolf, C., Newsome, T.M., Gale, M., Alamgir, M., Crist, E. et al., 2017,
‘World sciensts’ warning to humanity: A second noce’, BioScience 67, 1026–
1028. hps://doi.org/10.1093/biosci/bix125
Rouget, M., Richardson, D.M., Cowling, R.M., Lloyd, J.W. & Lombard, A.T., 2003,
‘Current paerns of habitat transformaon and future threats to biodiversity in
terrestrial ecosystems of the Cape Florisc Region, South Africa’, Biological
Conservaon 112, 63–85. hps://doi.org/10.1016/S0006-3207(02)00395-6
Rutherford, M.C., Mucina, L. & Powrie, L.W., 2006, ‘Biomes and bioregions of Southern
Africa’, in L. Mucina & M.C. Rutherford (eds.), The vegetaon of South Africa,
Lesotho and Swaziland, pp. 31–51, SANBI, Pretoria.
Schaers, A.P., Raemakers, I.P., Sýkora, K.V. & Ter Braak, C.J.F., 2008, ‘Arthropod
assemblages are best predicted by plant species composion’, Ecology 89, 782–
794. hps://doi.org/10.1890/07-0361.1
Soulé, M.E. & Sanjayan, M.A., 1998, ‘Conservaon targets: Do they help?’, Science
279, 2060–2061. hps://doi.org/10.1126/science.279.5359.2060
Traveset, A., Castro-Urgal, R., Rotlàn-Puig, X. & Lázaro, A., 2018, ‘Eects of habitat loss
on the plant-ower visitor network structure of a dune community’, Oikos 127,
45–55. hps://doi.org/10.1111/oik.04154
Van den Berg, E.C., Plarre, C., Van den Berg, H.M. & Thompson, M.W., 2008, The South
African naonal land-cover 2000, unpublished report, Report no. GW/A/2008/86,
Agricultural Research Council – Instute for Soil, Climate and Water, Pretoria.
Van Wilgen, B.W., Forsyth, G.C., Le Maitre, D.C., Wannenburgh, A., Kotzé, J.D.F., Van
den Berg, E. et al., 2012, ‘An assessment of the eecveness of a large, naonal
scale invasive alien plant control strategy in South Africa’, Biological Conservaon
148, 28–38. hps://doi.org/10.1016/j.biocon.2011.12.035
Vitousek, P.M., 1994, ‘Beyond global warming: Ecology and global change’, Ecology 75,
1861–1876, hps://doi.org/10.2307/1941591
Walters, D.J.J., Kotze, D.C. & O’Connor, T.G., 2006, ‘Impact of land use on vegetaon
composion, diversity, and selected soil properes of wetlands in the southern
Drakensberg mountains, South Africa’, Wetlands Ecology and Management 14,
329–348. hps://doi.org/10.1007/s11273-005-4990-5
... We have seen Graphis handelii only in historical specimens from KwaZulu-Natal, where it was found in both coastal and inland forest vegetation. We lack recent collections from the Indian Ocean Coastal Belt vegetation of this province, a threatened vegetation type (Jewitt 2018), so the current status of this species in South Africa is uncertain. ...
... We include it here because this genus has not otherwise been reported from South Africa. In South Africa, Platythecium is probably restricted to the vegetation of the Indian Ocean Coastal Belt, which is a threatened vegetation type (Jewitt 2018) that lacks recent lichen sampling. Remarks. ...
... More than one third of the species we treat in this paper are known from South Africa only from pre-1950s collections. Their current status in the country is uncertain given high levels of forest habitat loss, especially in the Indian Ocean Coastal Belt (Mucina & Geldenhuys 2006;Jewitt 2018). ...
Article
Additions and corrections are provided for the South African species of Graphidaceae tribe Graphideae with hyaline ascospores. Allographa oldayana I. Medeiros sp. nov. is described as new to science based on morphological, chemical and molecular data. The new species is characterized by lirellae with striate labia and a complete thalline margin, a completely carbonized excipulum, large, muriform ascospores, and the presence of hirtifructic acid. Allographa consanguinea (Müll. Arg.) Lücking, A. leptospora (Vain.) Lücking & Kalb, Diorygma aff. minisporum Kalb et al ., Graphis crebra Vain., Gr. dupaxana Vain., Gr. furcata Fée, Gr. handelii Zahlbr., Gr. longula Kremp., Gr. pinicola Zahlbr., Gr. proserpens Vain, Gr. subhiascens (Müll. Arg.) Lücking and Platythecium sp. are reported as new records for South Africa. Allographa striatula (Ach.) Lücking & Kalb, Graphis analoga Nyl. and Gr. scripta (L.) Ach. are shown to be misapplied names that should be removed from the South African checklist. The new combination Mangoldia bylii (Vain.) I. Medeiros comb. nov. (bas. Graphis bylii Vain) is made; this represents an earlier name for M. atronitens (A. W. Archer) Lücking et al . Taxonomic notes are provided for Graphis bylii var. lividula Vain. and Gr. denudans Vain., species that are known only from their South African holotypes. Phylogenetic analyses that include new DNA sequence data from the nrLSU, mtSSU and RPB 2 loci confirm the generic placements of several species for which molecular data were lacking: Allographa consanguinea , Glyphis atrofusca (Müll. Arg.) Lücking, Graphis crebra and Gr . subhiascens .
... The Critically Endangered S. inornatus appears to favour forest-grassland ecotone habitat and did not seem to avoid any of the soil types and soil textures present at the sampling sites (this study; Alexander and Marais 2007). The skink's remaining intact habitat is small and declining in size in its extremely limited geographic distribution, due to the almost total transformation of the Critically Endangered KwaZulu-Natal Coastal Belt Grassland that borders the skink's coastal forest habitat (Jewitt 2018). Only 11% of the total original area of this grassland remained in 2016, and with land transformation proceeding apace in the province of KwaZulu-Natal, far less will remain untransformed this decade (Jewitt 2018;Jewitt et al. 2015). ...
... The skink's remaining intact habitat is small and declining in size in its extremely limited geographic distribution, due to the almost total transformation of the Critically Endangered KwaZulu-Natal Coastal Belt Grassland that borders the skink's coastal forest habitat (Jewitt 2018). Only 11% of the total original area of this grassland remained in 2016, and with land transformation proceeding apace in the province of KwaZulu-Natal, far less will remain untransformed this decade (Jewitt 2018;Jewitt et al. 2015). Another threat is the invasion of alien plant species at the forest-grassland ecotone and the wooding up of coastal grasslands due to suppression of fire, the lack of large browsing herbivores in urban protected areas, and the increased carbon dioxide levels in the atmosphere (O'Connor et al. 2014;Stevens et al. 2016). ...
... Education and societal ownership will be key aspects of the success of this approach. • The amount of Sand Forest identified for conservation within the livelihood support management objective should be sufficient to reach the 69% of remaining vegetation conservation target (Jewitt, 2016). • There are a few discrepancies in the results of the (b) formal conservation management objective and the aim of the objective: o Sand Forest that is considered as a high priority for conservation within this management objective is already significantly conserved, meaning the remaining high conservation priority areas may not be enough to reach the conservation target (69% of remaining vegetation (Jewitt, 2016)). ...
... • The amount of Sand Forest identified for conservation within the livelihood support management objective should be sufficient to reach the 69% of remaining vegetation conservation target (Jewitt, 2016). • There are a few discrepancies in the results of the (b) formal conservation management objective and the aim of the objective: o Sand Forest that is considered as a high priority for conservation within this management objective is already significantly conserved, meaning the remaining high conservation priority areas may not be enough to reach the conservation target (69% of remaining vegetation (Jewitt, 2016)). o The aim of the formal conservation management objective is to conserve in areas where there is lower levels of poverty and unemployment, thus preventing the conservation of resources that are embedded in livelihoods, especially if conservation is more formal and not community based. ...
Article
Divergences between community livelihoods and conservation efforts often result in changes to the access and use of natural capital in affected areas, negatively affecting the respective livelihoods. New tools and interdisciplinary approaches are more frequently required for solving these conflicts. In this paper, the socio-ecological systems (SES) perspective along with Bayesian Belief Networks (BBNs) have been used to model the interdependencies that exist within the livelihood-conservation nexus to determine if this method can improve synergy between community livelihood requirements and conservation targets in an era of unsustainable decline of natural resources, using the livelihood-conservation nexus of informal Sand Forest harvesting in Northern KwaZulu-Natal as a case study. Results suggest BBNs have great potential for use in socio-ecological and resource management studies concerning the livelihood-conservation nexus. The results of the sensitivity analysis of the BBN showed that employment and income are the greatest socio-economic drivers in terms of Sand Forest harvesting, while preference (i.e. behaviour) for energy sources and building materials has the greatest overall influence on Sand Forest usage. The BBN scenario comparison further demonstrated the influence of preference (i.e. behaviour) on the SES. Finally, the conservation effectiveness assessment using the BBN showed the significant contribution of accessibility and availability of Sand Forest (i.e. conservation) to the system. Information provided by the BBN allowed for suitable areas for conservation to be identified given conservation targets and the utilisation needs of the communities (i.e. greater synergy), through the mapping of two livelihood management approaches (i.e. community conservation and formal conservation). The results of mapping the different management objectives have shown that community conservation is more suitable for moving the system towards synergy and should be considered for a Sand Forest resource management approach to compliment any conservation planning.
... The Long-toed Tree Frog (Leptopelis xenodactylus) is endemic to grasslands within the temperate region of the KwaZulu-Natal (KZN) Midlands. Some of these grassland types are threatened by land transformation (Jewitt 2018). This area falls within the Maputaland-Pondoland-Albany biodiversity hotspot (Mittermeier et al. 2005). ...
Article
Full-text available
Leptopelis xenodactylus is a little-known, Endangered species of frog that is thought to be endemic to the KwaZulu-Natal Province of South Africa. In an effort to determine the distribution of this species more accurately, a working species distribution model was created for use in searching for more populations over a period of three breeding seasons. Twenty-one more wetlands containing the frog were discovered and a second species distribution model was created for use in spatial planning applications. Leptopelis xenodactylus occurs primarily in temperate, alluvial hummock wetlands in U-shaped valleys at mid-altitudes in southwestern KwaZulu-Natal. The extent of occurrence and area of occupancy of L. xenodactylus were recalculated including the new records and have increased by 9% and 429%, respectively. The known localities for L. xenodactylus were analysed in relation to the predictions of two downscaled climate change models and a vulnerability framework. Climate change was found to be a potentially significant threat to L. xenodactylus according to the downscaled HadMC2 model and the vulnerability framework, potentially affecting up to 80.5% of the geographic range, but not according to the downscaled GFDL2.1 model and the vulnerability framework which indicated that up to 22% of the geographic range might be affected. The better understanding of the distribution and habitat of L. xenodactylus and of the potential combined impact of climate change and land transformation on the species gained through this study will assist in improving its conservation management.
... We conducted our study in Pietermaritzburg (29 • 37 ′ 04 ′′ S, 30 • 23 ′ 57 ′′ E), South Africa (Fig. 1). The city's natural landscape mostly consists of vegetation characteristic of grassland landscapes with a few areas of vegetation characteristic of savanna landscapes and thicket patches (Mucina and Rutherford, 2006;Jewitt, 2018). The region experiences warm-to-hot summer temperatures with frequent rainfall, and dry winters with high diurnal temperature variation (Nel, 2009;Thabethe and Downs 2018). ...
Article
Full-text available
Ground-nesting bird species are typically threatened when their natural habitats are removed or altered through urbanisation. Despite this, some species, like the Spotted Thick-knee (Burhinus capensis), persist in urban mosaic landscapes. Our study was undertaken to collect novel information on Spotted Thick-knee persistence in an urban mosaic landscape by investigating facets of its nesting ecology in Pietermaritzburg, South Africa. We collected Spotted Thick-knee nesting and nest site data between July 2019 and December 2020. We conducted direct observations at 33 nest sites, and additional remote monitoring with camera traps at eight nest sites. Spotted Thick-knee breeding pairs showed select habitat and nest-site preferences (greater use of shrub-like species for nest-site placement, more grass cover, shorter grass, and flatter slopes at nest sites) compared with random sites. Successful nesting outcomes were significantly greater than failed nesting outcomes. Incubation activity was significantly longer during the day, and incubation activity had a significant adverse relationship with disturbance in human-modified habitats. Land use and human activity influenced nest-site selection and survival of nests. Spotted Thick-knee used residential gardens and recreational areas as nest sites in the urban mosaic, although nests were more successful in residential gardens. They used shrub-like vegetation as nest-cover structures, possibly because of added protection from extreme weather or less visual detection. Risks associated with nesting in human-modified habitats included increased threats from domestic animals and incubation activity costs because of disturbance around nest sites. There are relatively few studies on ground-nesting birds in urban areas, so further research is needed to determine how our results compare with other species.
... Sand forest is under pressure from human exploitation for firewood, timber, and charcoal production (Izidine et al. 2008;Gaugris & Van Rooyen 2009;Tokura et al. 2020), with natural herbivores, such as elephant, impacting vegetation structure and other components of biodiversity (Van Rensburg et al. 1999 Aside from sand forest, the Maputaland vegetation comprises a mosaic of structurally contrasting woodlands, grasslands, wetlands, swamps, and forests, contributing to the considerable heterogeneity and biodiversity of the region (Gaugris & Van Rooyen 2010). Although large proportions of sand forest are formally conserved in South Africa, most other vegetation units are proportionately less protected (Jewitt 2018). ...
Article
Ground-dwelling spider assemblages were sampled by pitfall trapping in four contrasting biotopes in the Ndumo Game Reserve, South Africa, situated in the Maputaland-Pondoland-Albany biodiversity hotspot. Over two years (2006 and 2007) in two seasons (mid-summer and winter, 10 days each) 1261 spiders were collected, representing 31 families and 121 species. Twenty-five taxa were recorded from Ndumo for the first time. Spider activity densities and species richness were highest in the deciduous broadleaf woodland (BW, n = 538, S = 106), followed by Albizia adianthifolia-Vachellia tortilis woodland (AW, n = 358, S = 70), sand forest (SF, n = 188, S = 74), and Mahemane thicket (MT, n = 177, S = 53). The four most abundant species were Asemesthes ceresicola Tucker, 1923 (Gnaphosidae, 27.8%), Arctosa sp. (Lycosidae, 8.4%), Pardosa crassipalpis Purcell, 1903 (Lycosidae, 7.4%), and Stenaelurillus guttiger (Simon, 1901) (Salticidae, 5.2%). Species richness and activity densities were strongly seasonal, with sharp decreases in winter. Conservation assessments could not be carried out on a sizable proportion of the species collected, as they represent new taxa or were only represented by immatures (30.6%), but of the remainder the majority had a conservation status of Least Concern (64.5%), with very few being Data Deficient (4.1%) and a single vulnerable species being collected, Massagris natalensis Wesoowska & Haddad, 2009. However, among the new taxa not assessed there may be several Maputaland endemics.
Article
Full-text available
The appropriate management of the habitat of the endangered and endemic White-spotted Ketsi Blue butterfly, Lepidochrysops ketsi leucomacula Henning & Henning, 1994, is necessary for its survival. Much of the life history and ecology of this butterfly are unknown. The oviposition plant was found to be Selago tarachodes Hilliard and the plants that were observed to be visited by L. k. leucomacula for nectar all had pink flowers. Monitoring of two populations of L. k. leucomacula in protected areas commenced in March 2022. Surveillance and walked transects were the monitoring methods used. Observed threats to this endangered butterfly in protected areas were uncontrolled grazing by domestic livestock and mowing of its grassland habitat. Fencing of its grassland habitat may be a way to control these threats.
Article
Full-text available
In this study, we documented the diversity of bird species in the Eastern Cape coastal nature reserves (i.e., Hluleka, Dwesa, Silaka and Mkhambati nature reserves), and determined the potential role of each bird species in habitat maintenance using two functional traits (i.e., body mass and feeding mode) as the function's proxy. We applied the timed species count approach during bird observations, coupled with drive‐by surveys to maximise spatial coverage of each nature reserve over four years. To evaluate functional diversity, bird species were classified based on functional traits such as the adult body, and their potential ecological role derived from their feeding mode and habitat associations. Over 864 h, we accumulated 818 bird records containing 178 different bird species that were classified into 58 families with 32 species occurring in all nature reserves. Shannon–Wiener Diversity Indices showed very high overall species diversity across the nature reserves ( H > 3.5) with no differences detected across sites. Although no significant correlations between vegetation changes measured through Normalised Difference vegetation Index (NDVI) in each nature reserve and the number of bird records, forest bird species were dominant (42.1%; N = 178) throughout years of observation and diversity remained high ( H > 3.5). Bird species abundance only increased significantly across all nature reserves during 2018–2019. All four nature reserves had a similar distribution of bird functional traits with both high functional richness (FRic = 1), and divergence (FDiv = 0.8) and moderate evenness (FEve = 0.4). Multiple Correspondence Analysis (MCA) demonstrated a positive correlation between bird sizes and functions with large birds mainly associated with predators and carrion. Small birds and medium birds had a similar composition of species in terms of functionality being seed dispersers across the nature reserves. A significant effect that insectivores and carrions displayed in MCA plots, suggest the availability of indirect pollination services. Despite extreme drought conditions across the country in 2019, NDVI levels remained largely consistent over time in these four reserves; and thus, they offer important refuge for birds during extreme climatic conditions such as drought.
Article
Full-text available
Twenty-five years ago, the Union of Concerned Scientists and more than 1700 independent scientists, including the majority of living Nobel laureates in the sciences, penned the 1992 “World Scientists’ Warning to Humanity” (see supplemental file S1). These concerned professionals called on humankind to curtail environmental destruction and cautioned that “a great change in our stewardship of the Earth and the life on it is required, if vast human misery is to be avoided.” In their manifesto, they showed that humans were on a collision course with the natural world. They expressed concern about current, impending, or potential damage on planet Earth involving ozone depletion, freshwater availability, marine life depletion, ocean dead zones, forest loss, biodiversity destruction, climate change, and continued human population growth. They proclaimed that fundamental changes were urgently needed to avoid the consequences our present course would bring. The authors of the 1992 declaration feared that humanity was pushing Earth's ecosystems beyond their capacities to support the web of life. They described how we are fast approaching many of the limits of what the ­biosphere can tolerate ­without ­substantial and irreversible harm. The scientists pleaded that we stabilize the human population, describing how our large numbers—swelled by another 2 billion people since 1992, a 35 percent increase—exert stresses on Earth that can overwhelm other efforts to realize a sustainable future (Crist et al. 2017). They implored that we cut greenhouse gas (GHG) emissions and phase out fossil fuels, reduce deforestation, and reverse the trend of collapsing biodiversity.
Article
Full-text available
Pollination is a valuable ecosystem service, and plant–pollinator interactions in particular are known to play a crucial role in conservation and ecosystem functioning. These mutualisms, like other ecological interactions, are currently threatened by different drivers of global change, mainly habitat loss, fragmentation, or modification of its quality. Most studies so far have focused on the impact of such disturbances on particular species interactions and we thus need more empirical evidence on the responses at a community-level. Here we evaluated how habitat loss influenced the pattern of interactions between plants and their flower visitors in a coastal dune marshland community. Using data from four years (2008–2011), we assessed the effect of a large disturbance in the area (occurring in 2010) that represented the loss of more than 50% of the vegetation cover. We found a considerable decrease in species richness and abundance of flower visitors, which resulted in a lower number of interactions after the disturbance. Not all functional groups, however, responded similarly. Contrary to the expected from previous findings, bees and wasps were less negatively influenced than beetles, flies and ants, possibly due to their higher movement capacity. Species interactions in the community were more specialized after habitat loss, resulting in a lower level of network nestedness and a higher modularity. At a species level, the number of flower visitors per plant decreased after the disturbance, and plants were visited by less abundant flower visitors. Our findings lead us to predict that the overall plant–flower visitor network became less robust and resilient to future perturbations. However, the fact that each functional group responds distinctly to disturbances makes it more difficult to foresee the final consequences on community composition and ecosystem functioning.
Article
Full-text available
Background: The variety of applications in which the Vegetation Map of South Africa, Lesotho and Swaziland (VEGMAP) is used requires the map to be continually updated and refined to reflect the latest available information. The VEGMAP has been updated twice, in 2009 and 2012, since its first release in 2006. Objectives: The first objective is to report on the motivations for changes in the 2009 and 2012 versions. The second objective is to describe new vegetation types and subtypes included in these versions. Method: Changes to the VEGMAP are implemented after a peer-review process that is managed by the National Vegetation Map Committee. Accepted changes are then incorporated into the VEGMAP using GIS software. Results: Seventy-one of the 449 vegetation types were affected by updates. Changes included the addition of new vegetation types and subtypes, modifications to the boundaries of types present in the 2006 VEGMAP and changes to the names of vegetation types. Conclusion: The updates have affected a small portion of the map but have reflected a progressive refinement in quality. Regions that are still mapped at a coarse scale, especially those earmarked for land-use development, should be prioritised for improved map accuracy and classification through a more proactive approach towards vegetation mapping, using guidelines that are under development.
Article
Full-text available
Habitat loss and climate change are primary drivers of global biodiversity loss. Species will need to track changing environmental conditions through fragmented and transformed landscapes such as KwaZulu-Natal, South Africa. Landscape connectivity is an important tool for maintaining resilience to global change. We develop a coarse-grained connectivity map between protected areas to aid decision-making for implementing corridors to maintain floristic diversity in the face of global change. The spatial location of corridors was prioritised using a biological underpinning of floristic composition that incorporated high beta diversity regions, important plant areas, climate refugia, and aligned to major climatic gradients driving floristic pattern. We used Linkage Mapper to develop the connectivity network. The resistance layer was based on land-cover categories with natural areas discounted according to their contribution towards meeting the biological objectives. Three corridor maps were developed; a conservative option for meeting minimum corridor requirements, an optimal option for meeting a target amount of 50% of the landscape and an option including linkages in highly transformed areas. The importance of various protected areas and critical linkages in maintaining landscape connectivity are discussed, disconnected protected areas and pinch points identified where the loss of small areas could compromise landscape connectivity. This framework is suggested as a way to conserve floristic diversity into the future and is recommended as an approach for other global connectivity initiatives. A lack of implementation of corridors will lead to further habitat loss and fragmentation, resulting in further risk to plant diversity.
Article
Full-text available
This is an extract of this Grassland Biome chapter from the pre-publication PDF of the book Mucina, L., & Rutherford, M.C. (eds). Reprint 2011. The Vegetation of South Africa, Lesotho and Swaziland. Strelitzia 19. South African Biodiversity Institute, Pretoria. ISBN: 978-1919976-21-1
Article
Full-text available
The high biodiversity and physical heterogeneity of the Klamath-Siskiyou Ecoregion of the Pacific Northwest (USA) suggest the need for an ambitious and multifaceted approach to conservation research and planning. We developed a process of reserve selection and design that proceeds along three parallel tracks: (1) protection of special elements, such as rare species hotspots, old-growth forests, and key watersheds; (2) representation of physical and vegetative habitat types; and (3) maintenance of viable populations of focal species (e.g., fisher, Martes pennanti Erxleben). Each of these complementary research tracks identified important conservation opportunities in the region. In combination they provide a basis for a reserve design and management plan that meets conservation goals better than the existing management situation established by the federal Northwest Forest Plan. Our proposed Phase I reserve design begins with protection of roadless areas on public lands that score high under the criteria of our three-track approach. A relatively small area of additional public and private land is necessary to provide habitat contiguity among roadless areas and capture remaining biological hotspots. This design would place approximately 34% of the region into the strictest category of protected areas, compared to 13% under current management, and would place an additional 19% of the region into moderate protection. A second, proposed phase of conservation would include protection of additional private lands to meet representation objectives. Also included in Phase II would be protection of linkages to other regions, necessary for long-term persistence of wide-ranging animals such as large carnivores (e.g., gray wolf, Canis lupus L.), which are being considered for reintroduction to the region. When implemented, Phase II would bring approximately 60-65% of the region into strict and moderate protection. Linkage design and ecological management (e.g., fire regimes) are among the critical topics for further research.
Article
Viewpoint article World Scientists’ Warning to Humanity: A Second Notice WILLIAM J. RIPPLE, CHRISTOPHER WOLF, THOMAS M. NEWSOME, MAURO GALETTI, MOHAMMED ALAMGIR, EILEEN CRIST, MAHMOUD I. MAHMOUD, WILLIAM F. LAURANCE, and 15,364 scientist signatories from 184 countries. http://scientistswarning.forestry.oregonstate.edu/signatories
Article
Land-cover change and habitat loss are widely recognised as the major drivers of biodiversity loss in the world. Land-cover maps derived from satellite imagery provide useful tools for monitoring land-use and land-cover change. KwaZulu-Natal, a populous yet biodiversity-rich province in South Africa, is one of the first provinces to produce a set of three directly comparable land-cover maps (2005, 2008 and 2011). These maps were used to investigate systematic land-cover changes occurring in the province with a focus on biodiversity conservation. The Intensity Analysis framework was used for the analysis as this quantitative hierarchical method addresses shortcomings of other established land-cover change analyses. In only 6 years (2005-2011), a massive 7.6% of the natural habitat of the province was lost to anthropogenic transformation of the landscape. The major drivers of habitat loss were agriculture, timber plantations, the built environment, dams and mines. Categorical swapping formed a significant part of landscape change, including a return from anthropogenic categories to secondary vegetation, which we suggest should be tracked in analyses. Longer-term rates of habitat loss were determined using additional land-cover maps (1994, 2000). An average of 1.2% of the natural landscape has been transformed per annum since 1994. Apart from the direct loss of natural habitat, the anthropogenically transformed land covers all pose additional negative impacts for biodiversity remaining in these or surrounding areas. A target of no more than 50% of habitat loss should be adopted to adequately conserve biodiversity in the province. Our analysis provides the first provincial assessment of the rate of loss of natural habitat and may be used to fulfil incomplete criteria used in the identification of Threatened Terrestrial Ecosystems, and to report on the Convention on Biological Diversity targets on rates of natural habitat loss.
Article
Conservation targets should be based on what is necessary to protect nature in all its expressions. When in 1988 the Brundtland report called for tripling the world’s protected area estate (which was then at 3 to 4 per cent of the land area) there was a strong belief that sustainable development would ensure the proper care for nature on the rest of the unprotected earth. This has proven wrong. We therefore must materially shift our protected areas target to protect at least half of the world, land and water, in an interconnected way to conform with what conservation biologists have learned about the needs of nature. Instead we have set goals that are politically determined, with arbitrary percentages that rest on an unarticulated hope that such non-scientific goals are a good first step towards some undefined better future outcome. This has been a destructive form of self-censorship. It is time for conservationists to reset the debate based on scientific findings and assert nature’s needs fearlessly.