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Relationship between mammals and human activity measures
Anthropogenic activities form a complex web of threats that is influenced by various socioeconomic and political factors (e.g. national policies, economic conditions and a host of other factors varying among nations; Macdonald 1991; James 1994; Kerr & Currie 1995; Development Bank South Africa 2000; McKinney 2001; O’Neill et al. 2001; Liu et al. 2003). Clear evidence for these separate components affecting taxon extinction have been highlighted, yet it is difficult to assign a single risk value to separate impacts of these measures (Kerr & Currie 1995; Chertow 2001; Ceballos & Ehrlich 2002). Furthermore using certain human threat predictors (as used in the current study) should not be taken to mean that other human impacts are insignificant, as many additional human activities are also extremely important at local scales e.g. agriculture, alien invasive species etc. (Macdonald 1991). The present study indicates an association/congruence between most of the anthropogenic variables with overall mammal richness (OMR), yet very little of the variation in OMR was explained by the anthropogenic variables. In addition, weak correlations were also evident between EMR, TMR and various anthropogenic variables. Other studies indicated significant, but weak relationships between human population density, human population growth, poverty, per capita income, urbanization and mammals per country (Ehrlich & Holden 1971; Kerr & Currrie 1995; Harcourt & Parks 2003, Cincotta et al. 2000; McKee et al 2003). For example, Balmford et al. (2001) found a marked congruence between high vertebrate richness and human population density across Africa, while Chown et al. (2003) found a strong significant positive relationship between South African bird richness and human population density at a quarter degree scale. However, none of the six anthropogenic variables considered in the present study showed similar relationships with any of the measures of mammal richness despite some significant degrees of correlation. Andrews & O’Brien (2000) reported a strong association between mammal and plant richness, with woody plant richness explaining between 70 – 77% of the mammal richness in southern Africa.
Strong evidence indicates that primary productivity, evapotranspiration, and annual precipitation are some factors driving plant richness patterns (see O’Brien et al. 1998; Rutherford & Westfall 1986; O’Brien et al. 2000; Hawkins et al. 2003). Mammal richness is most likely defined by plant richness, which in turn is characterized by water-energy dynamics (Andrews & O’Brien 2000; Hawkins et al. 2003). Similarly, it has been shown that human population density and subsequent human predictors respond positively to increases in net primary productivity, indicating a relationship between measures of human and vertebrate richness (Balmford et al. 2001; Chown et al. 2003; Hawkins et al. 2003; van Rensburg et al. 2004b). These findings seem to support our results that allude to similar responses by measures of both human density and mammal richness being concurrent with primary productivity (Chown et al. 2003), with both high mammal richness and human distribution prevalent in the southern and eastern parts of the country.
The results from the current study suggest human threats do not currently define any of the mammal richness measures, with landscape transformation possibly being too recent to exhibit any statistically noticeable effect (Chown et al. 2003) at a QDS scale. processes that define mammal richness as well as threats may be operating at a finer spatial scale, over a longer temporal scale or, other more important causal mechanisms may dominate the current patterns, e.g. climatic variables, topographic variables, β-diversity etc. (Bailey et al. 2002, Hawkins & Pausas 2004). In addition, further analyses are required to ascertain which of the current human impact measures included in the current study are proximate or ultimate threats. It has been shown that human density is clearly a proximate threat with agriculture, urbanization, land transformation, and roads denoted as ultimate threats (Thompson & Jones 1999). A clearer understanding to which threats are more relevant as an immediate threat will allow relevant actions to be implemented.
A serious dilemma with the current analyses are attributable to the different varying spatial scales of the data used. Most of the anthropogenic data were collected at municipality level and were consequently transformed to QDS scale with most measures calculated as weighted averages, which resulted in a possible loss of fine scale information. Taxon distribution data (in this case mammal data) are rarely representative and accurate, and in most cases old and out of date (Freitag & van Jaarsveld 1995; Lombard 1995; Maddock & Benn 2000; Maddock & Samways 2000). South African mammal distribution data (presence and extent of occurrence data) are not equally sampled, incomplete and uneven in coverage and most of all at the wrong scale quarter degree scale (QDS) (Rebelo 1994; Freitag & van Jaarsveld 1995; Lombard 1995). This scale is often too coarse to reflect finer scale topographical and vegetation differences and will most likely fail to pick up many of the finer interactions between human predictors and mammal richness measures (Rebelo & Tansley 1993; van Rensburg et al. 2004a).
Another major factor influencing statistical results and analysis is the marked differences between the temporal scales of all the databases. The mammal QDS data range from specimens collected in the early 1900’s up to present time (Freitag & van Jaarsveld 1995); with the additional distribution range maps also based on potential distribution ranges of species (Freitag & van Jaarsveld 1995). Conversely the anthropogenic variables used in the current study generally dates from 1994 to present. The discrepancy between the varying time lines of the data sources could be a plausible cause for the resulting poor statistical correlations and variation found within the current study.
Materials and Methods
The study is based on the extant mammals of South Africa and the taxonomic treatments of Wilson and Reeder (1993) and augmented by that of Taylor (2000) for the Order Chiroptera and conforms to that used by the recent regional Red List (Friedmann & Daly 2004). For taxa with taxonomic discrepancies between these authorities, taxon specialists working on the specific problematic groups were consulted (see Friedmann & Daly 2004). The final species list, excluding subspecies and subpopulations was matched with presence data obtained from distributional records (Freitag & van Jaarsveld 1995; Keith 2004). Several taxa were excluded from the current study because no relevant distribution data were available. All subsequent distribution data were generalised to a common resolution at the quarter degree square level (QDS) representing an area of 25 x 25 km or 625km2) (Freitag & van Jaarsveld 1995) prior to the computation of regional priority scores.
RPS Components Six different components were used to compute regional conservation priority scores for South African terrestrial mammals. These included the four components, described by Freitag & van Jaarsveld (1997) as well as two additional components. These components were groups into three subsets that were considered to represent measures of vulnerability, irreplaceability, and threat and were calculated as follows: Vulnerability components
CHAPTER 1: General Introduction
CHAPTER 2: Regional IUCN Red List assessments for South African terrestrial and marine mammals: An overview
CHAPTER 3: Incorporating measures of anthropogenic threat in regional conservation assessments: A case study based on South African mammals
CHAPTER 4: Conservation priority-setting at a regional scale: a case study based on South African terrestrial mammals
CHAPTER 5: Taxonomic and phylogenetic distinctiveness in regional conservation assessments: A case study based on extant South African Chiroptera and Carnivora
CHAPTER 6: The Orange List: a safety net for biodiversity in South Africa
CHAPTER 7: Revisiting Green Data Species Lists
CHAPTER 8: Conclusion and a synopsis of the conservation assessment of South African mammals
APPENDIX 1: Regional IUCN Red List assessments for South African terrestrial and marine mammals: An overview
APPENDIX 2: Incorporating measures of anthropogenic threat in regional conservation assessments: A case study based on South African mammals
APPENDIX 3: The Orange List: a safety net for biodiversity in South Africa
