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Effects of errors in range maps on estimates of historical species richness of mammals in Canadian national parks


Abstract and Figures

Aim Tests for faunal relaxation in reserves, particularly for mammals, have relied on comparisons of current species richness with estimates of species richness derived from historical range maps. However, any range map reflects the extent of occurrence of species and not necessarily the area of occupancy. Thus, estimates of historical species richness might be prone to error introduced by ‘false positives’, that is, a species might be considered to have been present in locations where it actually was not. The effect of such ‘false positives’ could bias statistical tests of faunal relaxation to type I error, and result in estimates of the extent of faunal relaxation in reserves greater than was actually the case. We evaluated the potential for errors in historical range maps to generate inflated estimates of historical species richness of mammals at sites that are reserves today.
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Effects of errors in range maps on estimates
of historical species richness of mammals in
Canadian national parks
Lucas D. Habib, Yolanda F. Wiersma* and Thomas D. Nudds Department of Zoology,
University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Aim Tests for faunal relaxation in reserves, particularly for mammals, have relied on
comparisons of current species richness with estimates of species richness derived from
historical range maps. However, any range map reflects the extent of occurrence of
species and not necessarily the area of occupancy. Thus, estimates of historical species
richness might be prone to error introduced by Ôfalse positivesÕ, that is, a species might
be considered to have been present in locations where it actually was not. The effect of
such Ôfalse positivesÕcould bias statistical tests of faunal relaxation to type I error, and
result in estimates of the extent of faunal relaxation in reserves greater than was
actually the case. We evaluated the potential for errors in historical range maps to
generate inflated estimates of historical species richness of mammals at sites that are
reserves today.
Location Canadian national parks in the Canadian portion of the Alleghenian–Illinoian
mammal province in south-eastern Canada (the maritime region and parts of southern
´bec, Ontario and Manitoba).
Methods The effect of varying levels of error in range maps on estimates of historical
species richness was tested using geographical information systems (GIS)-based statis-
tical sampling of simulated historical ranges. SpeciesÕareas of occupancy were simulated
to be only 25%, 75% and 95% of published historical species ranges. For each reserve,
estimates of historical species richness from these simulated species ranges were then
compared with similar, previously published estimates of richness based on published
historical species ranges.
Results Previous estimates of historical species richness for reserves were inversely and
linearly related to the degree of inaccuracy of species ranges. If species ranges were, on
average, 5% smaller than the accepted ranges, then estimates of historical species
richness agreed with previous estimates in c. 90% of cases. However, if historical
ranges were, on average, 25% smaller than those used in previous analyses, then
previous historical estimates of species richness may be overestimates in c. 40% of
Main conclusions Estimates of the extent of faunal relaxation in reserves that use
historical range maps to quantify past species richness appear to be sensitive to even
small errors in the degree to which range maps may overestimate Ôarea of occupancyÕ.
Canadian national parks, historical mammal distributions, faunal relaxation, range
*Correspondence: Department of Zoology, University of Guelph, Guelph, Ontario, N1G 2W1, Canada. E-mail:
Journal of Biogeography, 30, 375–380
2003 Blackwell Publishing Ltd
Terrestrial reserves have been shown to undergo faunal
relaxation consistent with the principles of island biogeog-
raphy. Small parks that have become isolated from their
surrounding habitat matrix have been shown to have lost
more of their historical complement of disturbance-sensitive
mammals than large parks that are surrounded by intact
habitat (Glenn & Nudds, 1989; Newmark, 1995; Gurd &
Nudds, 1999; Brashares et al., 2001; Gurd et al., 2001;
Wiersma & Nudds, 2001). Tests for extinctions of mammals
from Canadian reserves have used two approaches to esti-
mate historical baselines of species richness. Species richness
has been estimated from species–area curves generated from
historical range maps assumed to represent species ranges
prior to widespread European settlement (e.g. Glenn &
Nudds, 1989; Gurd & Nudds, 1999). Species richness has
also been directly sampled from these maps using geo-
graphical information systems (GIS) (e.g. Wiersma &
Nudds, 2001). Estimates of the numbers of species that
might have been present in reserves prior to insularization
were compared with current species richness (Glenn &
Nudds, 1989; Gurd & Nudds, 1999; Gurd et al., 2001;
Wiersma & Nudds, 2001) to estimate the extent of faunal
relaxation that each park had undergone.
Estimates of past species richness from historical range
maps may be prone to error because of inaccuracy in those
maps. Range maps are typically derived from a series of point
observations and delineate the Ôextent of occurrenceÕof a
species (van Jaarsveld et al., 1998). Different methods may be
used to extrapolate range boundaries (extent of occurrence)
from observations of species (areas of occupancy), giving very
different impressions of the Ôextent of occurrenceÕ. Authors of
original range maps, for instance, may have had different
confidence in the locations of observations on which they
based their maps, and delineated species historical ranges
For example, the mammal range maps in Banfield (1974)
used previously to test for faunal relaxation in Canadian parks
(Glenn & Nudds, 1989; Gurd & Nudds, 1999; Gurd et al.,
2001; Wiersma & Nudds, 2001) were based on observations
and collected specimens documented in records maintained in
museums. These kinds of data may reflect favoured transport
routes, hunting areas or collection localities (Lawes & Piper,
1998). Further, because historical records are typically fewer,
and historical estimates of species ranges thus cruder, the
discrepancy between the Ôextent of occurrenceÕof a species and
the actual Ôarea of occupancyÕ(van Jaarsveld et al., 1998) may
be even greater for historical data than it is for current distri-
bution data.
Thus, Robinson & Quinn (1992) cautioned that species
might be designated as historically present in an area that is
currently a reserve, when they actually were not (Fig. 1) and
the effect of such Ôfalse positivesÕhas implications for tests of
faunal relaxation. If a reserve was never as species rich as
assumed, then formal statistical tests for faunal relaxation
would be biased to type I error, that is, to detecting relax-
ation when it has not actually happened (Gurd & Nudds,
1999; Wiersma & Nudds, 2001). This criticism has intuitive
appeal, but we are unaware of any formal evaluation of how
poor the match between Ôextent of occurrenceÕand Ôarea of
occupancyÕwould have to be before we might consider sta-
tistical tests for faunal relaxation based on historical data to
be unreliable.
We evaluated the effect of potential inaccuracies in
historical ranges of mammals on estimates of historical
species richness for a subset of mammals (see below for
selection criteria) identified as historically present by
Wiersma & Nudds (2001) in Canadian national parks. For
each species, we simulated reductions in Ôarea of
occupancyÕwithin the historical ranges (extents of occur-
rence) by randomly placing ÔholesÕ(simulated unoccupied
areas) within each range. New estimates of historical spe-
cies richness for each park were derived using the
simulated ranges following methods of Wiersma & Nudds
(2001), wherein GIS was used to count directly the number
of species historically present in areas that are currently
national parks. The new estimates of historical species
richness were compared with Wiersma & NuddsÕ(2001)
(a) (b)
Figure 1 Two possible ways to connect the
same set of points to delineate an historical
species range with different implications for
assessment of historical presence of a species
in a reserve. (a) The reserve (stippled)
intersects with the Ôextent of occurrenceÕof
the range; the species is recorded as historic-
ally present in the reserve. (b) The reserve
intersects the Ôextent of occurrenceÕof the
range, but not the actual Ôarea of occupancyÕ
(shaded); the species was not actually present
in the reserve.
2003 Blackwell Publishing Ltd, Journal of Biogeography,30, 375–380
376 L. D. Habib et al.
previous estimates of historical species richness to test how
robust the estimates were to the simulated inaccuracies in
the historical ranges.
We used maps digitized from historical distributions of
native, terrestrial mammals in Canada depicted in Banfield
(1974) to generate simulated historical ranges in a GIS.
Analysis was restricted to disturbance-sensitive mammal
species (defined by Glenn & Nudds, 1989, sensu Humphreys
& Kitchener, 1982) in the Canadian portion of the Alle-
ghenian-Illinoian (AI) mammal province (Fig. 2) (Hagmeier,
1966), the region where faunal relaxation by mammals
consistent with theory has been previously and consistently
detected (Glenn & Nudds, 1989; Gurd & Nudds, 1999;
Gurd et al., 2001; Wiersma & Nudds, 2001). Parks com-
posed of archipelagoes (Georgian Bay Islands National Park,
St Lawrence Islands National Park, and Mingan Archipelago
National Park) were excluded to study the effects of
insularization on terrestrial isolates alone, leaving ten parks
for analysis (Fig. 2).
The six species deemed to be historically present across all
ten national parks by Wiersma & Nudds (2001) were used
in this analysis: Castor canadensis (beaver), Lynx canadensis
(lynx), Microsorex hoyi (pygmy shrew), Tamiasciurus hud-
sonicus (red squirrel), Glaucomys sabrinus (northern flying
squirrel), and Lontra canadensis (river otter). Five of these
six are not currently resident in at least one park; therefore,
if their historical presence in a park is an example of a Ôfalse
positiveÕ, then Wiersma & NuddsÕ(2001) conclusion that
these species were extirpated from the parks may be sensitive
to type I error.
The shape of the AI province was clipped from each
mammal range using ArcView
3.1 (Environmental Sys-
tems Research Institute, Redlands, CA, USA) and used as the
Ôbase rangesÕfrom which to simulate ranges with different
degrees of Ôarea of occupancyÕ. The base historical range of
each species was gridded with 10,000 km
cells using Arc-
Sample 3.03 extension (Quantitative Decisions,
Merion Station, PA, USA) (Fig. 2). This cell size was chosen
somewhat arbitrarily, but with the intent of compromising
between two potential sources of error. Overly large cells
might have reduced the resolution of the analysis to the point
that effects of a simulated small percentage reduction in Ôarea
of occupancyÕwould be too hard to detect, as it would
reduce the simulated range by only one or two cells. This
might result in false confidence in the use of historical range
maps. On the other hand, overly small cells might have
created an unrealistic level of patchiness in the simulated
Ôarea of occupancyÕ.
A list of cells that comprised the simulated historical ran-
ges, and the parks that intersected each cell, was generated in
Riding Mountain
La Mauricie
Cape Breton
Point Pelee
Bruce Peninsula Location
in Canada
500 km
500 0
Figure 2 The Canadian portion of the Alleghenian–Illinoian mammal province (after Hagmeier, 1966), showing the ten national parks used
in the analysis. Superimposed is the range grid for the mammal province used in the analysis, from which the ranges for each of the six species
used were clipped.
2003 Blackwell Publishing Ltd, Journal of Biogeography,30, 375–380
Errors in range maps and historical species richness 377
. This list was exported to SPSS 10.0 (SPSS Inc.,
Chicago, IL, USA) and cells were deleted at random to cre-
ate, for each species, 300 historical ranges – 100 each of 25,
75 and 95% Ôarea of occupancyÕof the historical ranges used
in past studies (Glenn & Nudds, 1989; Gurd & Nudds,
1999; Wiersma & Nudds, 2001) – for a total of 1800 iter-
ations. For each iteration, species were scored as present or
absent in each area that is now a national park based on
whether they intersected with the simulated Ôarea of occu-
pancyÕ. Thus, a simulated ÔgapÕin the interior of the Ôextent
of occurrenceÕrepresented an unoccupied area and a species
would not be considered historically present in a park that
coincided with that gap.
For each park, we calculated the proportion of 100 itera-
tions in which all six species were detected as present histor-
ically, and therefore in agreement with Wiersma & Nudds
(2001). We calculated the mean proportion of agreement
(N ¼10 parks) at each simulated percentage Ôarea of occu-
pancyÕ(N ¼3). The proportion of those simulated results that
agreed with the original estimates by Wiersma & Nudds
(2001) was fitted against the simulated area of occupancy by
linear least squares regression.
The numbers of species estimated to have been historically
present in parks varied with simulated variation in Ôarea of
occupancyÕof historical ranges (Fig. 3). If actual historical
ranges were on average only c. 5% smaller than those depicted
by Banfield (1974), then Wiersma & NuddsÕ(2001) estimates
of historical richness may be accurate in c. 90% of cases
(Fig. 3). That is, if historical range maps overestimated the
Ôextent of occurrenceÕby 5%, then there is a 10% chance that a
species detected at a specific location will be a false positive. If,
however, historical ranges were, on average, 25% smaller
than those used in previous analyses, then Wiersma & NuddsÕ
historical estimates of species richness may have been over-
estimated by one to four species in c. 40% of cases (Figs. 3, 4).
The estimate of the slope suggests that a 10% decrease in the
actual Ôarea of occupancyÕresults in a 13% decrease in ag-
reement with estimates of historical species richness compared
with estimates generated under the assumption that the Ôarea
of occupancyÕis also the Ôextent of occurrenceÕ(i.e. the his-
torical species range).
Figure 3 Data points [;sindicates Wiersma & Nudds (2001)
baseline value] and linear regression (y ¼95.6)12.9x, r
P¼0.0095) illustrating rate of change of the probability of all six
species being historically present as the Ôarea of occupancyÕis
reduced. Decreasing the Ôarea of occupancyÕof a historical range by
5% results in a 90% agreement with the baseline value which
assumed that Ôarea of occupancyÕ¼Ôextent of occurrenceÕ. Thus, if
historical range maps overestimate Ôarea of occupancyÕby 5%, then
estimates of faunal relaxation may be biased by Ôfalse positivesÕin
10% of cases. Note that standard error for 75% historical range
removed is too small for error bars to be displayed on the scale of
Iterations (%)Iterations (%)Iterations (%)
Figure 4 The number of species estimated to have been present
historically in a small park (Fundy National Park; solid bars) and a
large park (Riding Mountain National Park; striped bars) at three
simulated Ôareas of occupancyÕ: (a) 25%, (b) 75% and (c) 95% of
historical ranges.
2003 Blackwell Publishing Ltd, Journal of Biogeography,30, 375–380
378 L. D. Habib et al.
At the smallest simulated Ôareas of occupancyÕ(25%),
parks were estimated to have one to six fewer species than
Wiersma & Nudds (2001) reported, and this effect varied
among parks of different sizes. Typically, estimates of spe-
cies richness for larger parks were more robust to variation
in the Ôareas of occupancyÕthan were estimates for smaller
parks (Fig. 4). However, because most reserves are small
[e.g. see Fig. 3 in Gurd et al. (2001)], the problem of Ôfalse
positivesÕfor identification of faunal relaxation could be
particularly acute. These results suggest that studies which
sample historical range maps at a large grain size may be
more robust to errors in the Ôextent of occurrenceÕthan
studies which conduct sampling at a finer resolution or grain
size. In any case, future inventories of biodiversity could be
strategically directed towards sampling to resolve to what
degree Ôextent of occurrenceÕactually differs from Ôarea of
occupancyÕfor species ranges.
Further, accuracy of historical ranges may also vary by
species. Better historical records exist for game species than
for smaller mammals. For example, the beaver was heavily
trapped following European colonization, and therefore has
more point observations. Conversely, the pygmy shrew was
not only ignored by trappers, but was probably rarely sigh-
ted because of its small size. Historical range maps may also
be more accurate for generalist species that can live in a
variety of habitats. For example, the red squirrel is consid-
ered to occupy a variety of habitats; therefore, its historical
range may more nearly reflect its Ôarea of occupancyÕthan
the pygmy shrew, which is considered a habitat specialist,
suggesting a smaller Ôarea of occupancyÕthan indicated by its
historical range. In general, studies that use historical range
maps for abundant, charismatic, or commercially important
species should be more reliable than those using maps of
rare, shy or non-game species. In our analysis, most of the
six species used were generalists, and three of them (river
otter, beaver and lynx) were heavily trapped by Europeans.
Our analysis speaks of the question of the reliability of
estimates of faunal relaxation when historical range maps
overestimate the Ôareas of occupancyÕof species. It is also
possible that historical range maps may underestimate species
Ôareas of occupancyÕ. Thus, a species recorded as historically
absent from a current park would be a false negative, and
would be considered to have appeared in the park over the
interval. In this case, an increase in species richness would be
detected, when, in fact, the correct conclusion would be that
no decrease occurred. While this type of error would bear
directly on estimates of faunal turnover, it does not bear on
assessments of faunal relaxation. In that respect, error from
historical range maps that underestimate Ôareas of occupancyÕ
is conservative when the emphasis is on stemming losses of
species from reserves; a park manager is more concerned with
missing a local extinction than detecting a colonization that
didnot happen.
So far as we are aware, this is the first formal assessment
of the implications of Ôfalse positivesÕfor historical data
used to test for faunal relaxation in reserves, but it con-
curs with the intuitive logic of Robinson & Quinn (1992),
who expressed concern that variation in Ôarea of occu-
pancyÕaffects estimates of historical species ranges in
present-day reserves. Nevertheless, although it is generally
accepted that historical range information is often difficult
to obtain and of questionable accuracy, it is difficult to
evaluate how inaccurate historical ranges are likely to be,
and so, how significant this source of error might be for
evaluating the extent of faunal relaxation. In any case,
Aplet & Keeton (1999) argued that historical data ought
not to be simply dismissed or ignored because of the
difficulties it presents. Certainly, historical range maps are
often based on opportunistically collected data, such as
trapping records, and there is no single acceptable stand-
ard against which the maps can be judged (Williams,
1996). Often, they are the only data available with
which to work (van Jaarsveld et al., 1998). Thus, the
outcomes of analyses using historical range data are
somewhat conditional on the accuracy of the historical
data. Where the accuracy of the data is not known, there
is no option other than to accept the assumption that the
majority of the range maps used are accurate, at least
within the range of effects detectable in analyses, and
supply appropriate caveats. At large scales of spatial
analysis, perhaps more error in historical range maps
might be tolerated than at finer scales. Thus, we remain in
somewhat of a quandary, requiring appropriate historical
baseline data for species richness, but perhaps unsure of
the quality of the data. Gurd & Nudds (1999) and
Wiersma & Nudds (2001) may have reported inflated
rates of faunal relaxation, especially in the smallest parks;
however, from a conservation perspective, the effect of
overestimating historical species richness in present-
day parks is a conservative error. Our caveat is this: if
Banfield (1974) depicted historical ranges larger than they
actually were, then faunal relaxation in some Canadian
parks may not be as great as previous work suggested. It
will never be possible to know with certainty, but ex-
tremely large discrepancies between Ôextents of occurrenceÕ
and Ôareas of occupancyÕseem unlikely given that many of
the species that are presumed extirpated from parks are
large, charismatic, game species whose ranges were prob-
ably quite well known. Overall, we concur with others
(van Jaarsveld et al., 1998) who suggest that range maps
are a useful tool for large scale ecological and biogeo-
graphical analyses, so long as their limitations are
Thanks to W. Yang and A.M. Edwards for assisting with
writing the SPSS script to perform the iterations. This
research was supported by grants from the Natural Sci-
ences Engineering and Research Council of Canada and
Parks Canada to T.D. Nudds. Two anonymous reviewers
provided helpful comments on an earlier draft of the
2003 Blackwell Publishing Ltd, Journal of Biogeography,30, 375–380
Errors in range maps and historical species richness 379
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Lucas Habib recently completed B.Sc. at the University
of Guelph, Canada and is currently working as a
freelance biologist. He is interested in spatial issues
related to conservation.
Yolanda Wiersma is a Ph.D. student in the Department
of Zoology, University of Guelph, and a Fulbright
Visiting Scholar at Duke University. She is interested in
landscape ecology and biogeography applications to the
design and management of protected areas.
Thomas Nudds is a professor of Zoology at the
University of Guelph. His research interests include
community ecology, the evolutionary and applied
ecology of waterfowl, and conservation biology. He
recently served on the federally appointed Panel on the
Ecological Integrity of the National Parks.
2003 Blackwell Publishing Ltd, Journal of Biogeography,30, 375–380
380 L. D. Habib et al.
... The quality of range maps not only varies with the spatial and temporal dynamics of ranges ( Fortin et al., 2005), but also with the publisher, digitizer, scale and geographical region . They may also be more accurate for certain species, such as game and charismatic or generalist species ( Habib et al., 2003). As a consequence, when individual range maps of several species are summed to estimate species rich- ness, the resulting 'range overlap map' is likely either to underestimate local species richness, because species may have been undetected due to sparse sampling in certain areas of their 'true' range, or overestimate species richness, because species do not occur evenly throughout their range (Gaston, 2003). ...
... In the same vein, different methods are typically used to delineate an expert-drawn species range maps from point records, which might have different implications for assessing the presence of a species in a reserve, especially at range boundaries (e.g. see fig. 1 in Habib et al., 2003). For instance, as acknowledged by their producers ( Ridgely et al., 2007), most maps were digitized by eye or from scanned images of source maps occurring in field guides or species accounts. ...
We use data based on species checklists and geographical range maps for 3096 amphibian, 4100 bird and 1878 terrestrial mammal species to explore possible discrepancies in estimates of species richness for protected areas. We predict substantial differences between checklist and range map richness for smaller areas, where higher errors of commission from range maps are likely to occur. We also explore how discrepancies between species inventories and range maps may affect assessments of reserve prioritization and representation.
... Patterns of species richness are generally not homogeneous throughout the landscape and vary geographically (Currie and Paquin 1987;Currie 1991). Although species richness should be an objective metric of biodiversity, different mapping methods can result in dramatically different diversity spatial patterns (Graham and Hijmans 2006), which can lead to uncertainties in conservation decision-making and ecological interpretations (Habib et al. 2003;Hulbert and White 2005). Therefore, it is important to evaluate how different mapping methods affect both individual species distributions and patterns of species richness. ...
Mapping species distributions has become central for biodiversity research. Different mapping methods, however, may result in dramatically different spatial patterns. We used expert-drawn maps (EDMs), minimum convex polygons (MCPs), ecological niche models (ENMs), and consensus models, to compare patterns of species ranges and species richness in 12 species of Psittacidae in a poorly known Neotropical ecosystem, the Caatinga Dry Forest. We validated results by comparing the ability of each method to predict the number of Psittacidae species in 17 localities with well-studied avifaunas. Size ranges were highly correlated (from 0.7 to 0.9) among mapping methods, but presented critical spatial differences, which resulted in very different patterns of species richness. When confronted with real data, MCPs and the EDM/MCP consensus method, both correctly predicted the presence of ~ 90% of the species present in the studied areas. However, when taking commission errors into account, MCPs presented the lowest efficiency (56%) among all methods. All three consensus methods (ENM/ EDM, ENM/MCP, and EDM/MCP) performed better (> 74% efficiency) than any single method. We conclude that single mapping methods are prone to both higher omission and commission errors, and advocate for the use of consensus methods whenever species ranges will be used in macroecological studies.
... Discontinuous distributions are generally accepted in two-dimensional geographic range analyses (Gaston andBlackburn 2000, Hurlbert andWhite 2005). Whereas a species' distribution is indicated by the occupation of points, complex and perhaps fractal, and is compared to a slice of Swiss cheese due to the inherent porosity (Rapoport 1982), a species' range is simply the smallest convex polygon that covers the distribution and ignores the internal structure (Gaston and Blackburn 2000, Habib et al. 2003, Hurlbert and White 2005. Additionally, Laurie and Silander (2002) suggested the need for careful distinctions between range overlap and density in descriptions of species richness. ...
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Interpolation of species ranges has been a common approach to compensate for the unevenness or incompleteness in sampling effort in studies of geographic species richness gradients. However, potential biases introduced by this estimation method remain unclear. Here, we presented an explicit examination of the influences of one-dimensional interpolation on elevational species richness gradients, and discussed potential causes and processes of these influences. We conducted intensive surveys of birds along the elevational gradients of the Ailao Mountains, southwestern China, and compared richness patterns based on interpolation with raw data as well as estimated data from rarefaction and Chao1 non-parametric estimator; we also compared results of multiple linear regressions and hierarchical partitioning analyses explaining these four measures of richness. Actual evapotranspiration (AET) and the mid-domain effect (MDE) were highly correlated and separately provided a good potential explanation for the unimodal richness pattern in the Ailao Mountains, with modifying and suppressive effects of other variables such as area. Interpolation consistently and significantly increased the effects of AET/MDE, while it reduced contributions of area and human disturbance. Our results demonstrated that while compensating for biases in sampling effort, interpolation may also spuriously fill genuine distribution gaps, and tend to underestimate the effects of the non-monotonic or discontinuous influencing factors that are responsible for these gaps, and overestimate the effects of other factors actually suppressed by these factors. These influences were most strong for species with relatively medium elevational ranges. We conclude that at the regional scale, interpolation method is a potential source of bias in identifying and explaining species richness gradients and should be used with careful consideration. It may be advantageous to adopt other robust estimation methods besides interpolation to gain a more accurate assessment of species richness and a more objective understanding of their underlying mechanisms.
... illustrating species distributions in field guides), the scale and resolution to which they are typically drawn preclude their use as source of presence data for ENM analyses. That is, because range maps reflect the extent of occurrence and not necessarily the area of occupancy of species, they are prone to false positives, i.e. to include areas with unsuitable environmental conditions for the focal species [14]. A search for false positives in the datasets of Medone et al. [1] by extracting elevation values from a GIS file (30 00 resolution; [15]) allows flagging sites at elevations of 3000-3600 m for R. prolixus and of 4300-4650 m for T. infestans that are at odds with their own assertion that the elevation range of these species is 0-2600 m and 0-4100 m, respectively. ...
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Medone et al. [[1][1]] used ecological niche modelling (ENM) analyses to assess the climatic suitability expected by the year 2050 in Venezuela and Argentina for two vectors of Chagas disease, the kissing bugs Rhodnius prolixus and Triatoma infestans . Based on these analyses, and on epidemiological
... However, full details of how the "presence polygons" on the distribution maps were derived are not provided; it can only be assumed that these represent some kind of "broadbrush" approach that is akin to determining the "extent of occurrence" of a species (a convex polygon created by joining the outermost distribution records, as defined in IUCN, 2015). If so, it introduces the problem of the possible existence of "false positives"; here a species may be considered to occur everywhere within its "extent of occurrence" but no data or information exist to confirm this (Laliberte & Ripple, 2004;Habib et al., 2006). A further limitation of the Du Plessis study is that it uses "rock paintings" and "place names" as sources of distribution information; both of these are considered to be unreliable for this purpose (Skead, 2007(Skead, , 2011Boshoff & Kerley, 2013). ...
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Distribution data form the basis of the study of zoo-geography, which has applications in, inter alia, ecology and conservation. Written records were used to estimate the distribution patterns of some of the medium- to large-sized terrestrial mammals in central, southern and western South Africa, and neighbouring Lesotho, during the early historical period (late 1400 s to the 1920s). The sources of these records comprise mainly published or unpublished letters, journals, diaries or books written by literate pioneers – notably various missionaries, explorers, travellers, naturalists, military personnel, big game hunters and agro-pastoralists. The classification (according to record type) of the written records in key publications was standardised, and records overlooked by them are taken into account. Interpretation of the spatial patterns provided by the written records was aided by reference to supporting information, in the form of qualifying palaeontological, zoo-archaeological and museum records. Written records of acceptable quality are shown, together with supporting records (where applicable), on a series of species occurrence maps, which also depict the biomes that are represented in the study area. The information on these maps is interpreted, together with relevant information in the source texts and a map of the bioregions that constitute the biomes in question, to estimate distribution patterns that prevailed during the period under study. Data are presented for 27 genera, 36 species and 2 subspecies, comprising 7 carnivores and 30 herbivores. Despite the limitations associated with the use of written records, the information provided is considered to offer a realistic distribution pattern for most of the taxa covered. The use of supporting records is justified, since the majority of these corroborate the ranges derived from the written records. The present study enhances our knowledge of distribution patterns for these larger mammal species in a large part of the southern African sub-region during the early historical period. It also provides a first attempt to describe the sub-regional scale, historical, distribution patterns within the context of the broad biogeographical characteristics of the area in question. There is a need to extend the coverage achieved by this study to include the remaining approx. 30% of “South Africa”, i.e. the region incorporating South Africa, Lesotho and Swaziland, and also the area incorporated by the countries of Namibia, Botswana, Zimbabwe and Mozambique. This level of coverage will permit enhanced definition of historical distribution patterns for some larger mammals in the southern African sub-region. There is also a need to better understand the drivers, as well as the implications, of the observed changes in the distribution of the larger mammals since the start of the historical period.
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Systematic Conservation Planning (SCP) is an approach to protected areas planning that follows a step-by-step process. Recent reviews have examined the use of key "biogeographic-concepts", but an assessment of their use or effectiveness has not been done. We conducted a review of the literature on SCP to assess how the 6-step approach considers these concepts. Most of the 127 papers we reviewed varied in their application of SCP steps. Our findings suggest that protected areas plans are not effectively achieving conservation goals. Only six papers considered data uncertainty. Twenty papers used so-called "data free" conservation targets without clear rationales, and which have been shown to under-represent natural features. The median size of planning units applied (2500 ha) is too small to meet minimum area requirements for many species. We show how an examination of the variation in the ways that SCP is applied helps to identify best practices for achieving conservation effectiveness and efficiency. However, very few SCP efforts have been implemented, making it difficult to assess their effectiveness or efficiency in practice. Detailed examination of how SCP is implemented (perhaps focused on a specific region) can lead to a better understanding of how best to achieve large-scale conservation goals.
Aim Maps of species richness are the basis for applied research and conservation planning as well as for theoretical research investigating patterns of richness and the processes shaping these patterns. The method used to create a richness map could influence the results of such studies, but differences between these methods have been insufficiently evaluated. We investigate how different methods of mapping species ranges can influence patterns of richness, at three spatial resolutions. Location California, USA. Methods We created richness maps by overlaying individual species range maps for terrestrial amphibians and reptiles. The methods we used to create ranges included: point-to-grid maps, obtained by overlaying point observations of species occurrences with a grid and determining presence or absence for each cell; expert-drawn maps; and maps obtained through species distribution modelling. We also used a hybrid method that incorporated data from all three methods. We assessed the correlation and similarity of the spatial patterns of richness maps created with each of these four methods at three different resolutions. Results Richness maps created with different methods were more correlated at lower spatial resolutions than at higher resolutions. At all resolutions, point-to-grid richness maps estimated the lowest species richness and those derived from species distribution models the highest. Expert-drawn maps and hybrid maps showed intermediate levels of richness but had different spatial patterns of species richness from those derived with the other methods. Main conclusions Even in relatively well-studied areas such as California, different data sources can lead to rather dissimilar maps of species richness. Evaluating the strengths and weaknesses of different methods for creating a richness map can provide guidance for selecting the approach that is most appropriate for a given application and region.
Recent analyses of mammal species loss in protected areas around the world suggest that habitat loss and human population density outside of park boundaries may be better predictors of species loss and biodiversity patterns than absolute area of parks themselves. In North American parks, there hav e been conflicting studies about the relative impact of habitat versus human population density on the loss of mammals. These differences may be due to scale effects, as past studies in Canadian national parks have not examined the effect of spatial scale on species loss since the time of widespread European settlement. Here, we build on previous work and look at the effects of habitat area and human population density in buffer regions that are 10, 25, 50, and 100 km (6.2, 15.5, 31.1, and 62.1 mi) outside of the boundaries of 24 national parks in Canada on the loss of disturbance-sensitive mammals. We also examine whether the relative importance of predictors is correlated with species body size. As in previous work, we find that the amount of effective habitat area is a more significant predictor than human population density and that scale effects are not significant, at least for the scales and species examined.
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Patterns of local extinction of mammal populations in western North American parks were examined in relation to current biogeographic and population lifetime models. The analysis was based on species sighting records as of 1989. While western North American pants are obviously not true Isolates, patterns of mammal extinction in them are nonetheless consistent with two predictions of the land-bridge island hypothesis. First, the number of extinctions hers exceeded the number of colonizations since park establishment, and, second the rate of extinction is inversely related to park area. Factors influencing the lifetime of mammal populations were evaluated using a stepwise multivariate survival analysis procedure for censored data Survival time for mammal populations was positively related to estimated initial population size. After accounting for population size, species within the order Lagomorpha were particularly prone to extinction. Finally, after controlling for population size and taxon variation survival time was positively related to age of maturity, indicating that species with longer generation times-age of maturity and generation time are highly correlated in mammals-persist longer in absolute time.
The biological reserves in the wheatbelt of Western Australia form islands of native vegetation in a sea of arable farmland. Subdivision of the species in three vertebrate taxa (mammals, passerine birds and lizards) into species retained only in reserves (u species) and those also surviving outside the reserves in disturbed areas (d species) show conflicting requirements for reserve area. In each taxon u species are lost disproportionately with a reduction in area. The d species are favoured by more smaller reserves while u species, those most in need of conservation, are favoured by larger reserves.
Using range maps depicting mammal distributions in Canada prior to European settlement, species-area curves with confidence limits were constructed for each of six regions where species composition of mammals was faunistically homogeneous. The number of species in parks was compared to the number predicted to have been present in an area of equivalent size during the pre-settlement era. In the densely populated region comprising southern Ontario, southern Quebec, and the maritimes, and consistent with predictions from island biogeography theory, parks presently have fewer species that require undisturbed habitats than were predicted to have been present prior to settlement. The difference between the number of species observed and predicted is greater for small than large parks. In all other regions, most parks had the same or more species than predicted. It appears that greater numbers of species in some parks may be due either to alteration of habitats (e.g. fire suppression in prairie parks) or siting parks in species-rich areas.
In an earlier paper, numerical techniques were developed and used to analyze distribution patterns of the native terrestrial mammals of North America. An error in method is here corrected, indicating that 35 provinces, 13 superprovinces, four subregions, and one region may be recognized. The methods used are relatively objective, quantitative, and suited to computerization.
AimSome recent tests of faunal change in reserves have relied on, but been limited to, estimates of species richness from random samples of historic range maps. We evaluated a different, Geographic Information Systems (GIS)-based, approach to count species directly, as the latter method might facilitate rapid estimation of historic species richness as well as composition for samples of the same size, shape and exact location as present-day reserves.LocationNational parks throughout Canada.Methods Geographic Information System.ResultsThe GIS-based method tended to count, in exact locations of modern parks, fewer species (on average, seven disturbance intolerant and five disturbance tolerant) present historically than extrapolated from randomly sampled sites, but the differences were not greater than expected by chance. However, correlations between number of species lost and park size were weaker than reported previously, suggesting a greater potential for other factors to influence a change in species richness (and composition) than inferred earlier.Main conclusionsDirect counts of historic range maps using GIS tools provided a quick means to estimate site-specific historic species richness not statistically different from estimates produced by random sampling. As well, the GIS-based method could yield data about historic species composition for the specific location and size of modern reserves, which may be more ecologically meaningful in terms of assessing what factors may have contributed to the observed species losses.
AimGlenn & Nudds (1989) compared mammal species richness in Canadian parks to estimated species-area relationships prior to European settlement to test if parks presently contain their historical compliment of species. However, the data they used to estimate the presettlement species-area relationships were not commensurate with the scale of the parks and were not independent. This uncertainty reduces the utility of Glenn and Nudds analysis to detect which, if any, parks are experiencing mammal extirpations and ultimately to direct conservation efforts to enhance mammal conservation in Canada. We improved Glenn and Nudds methods and re-assesed the conservation status of mammals in Canadian parks.