Obtaining data and setting targets: methods and limitations

Bob Smith

Durrell Institute of Conservation & Ecology

• Problems with data quality (focussing on presence/absence data)

• Suggestions on data requirements at the fine-scale

• How to develop cost and threat datasets

• How to set targets for species and landcover types

A planning system will only be useful if its results are implemented and there are several ways to increase the likelihood of this, which include:• Conduct the analysis at a relevant spatial

scale

• Include data on relevant conservation features

• Use up-to-date information

• Set justifiable representation targets

• Include relevant socio-economic & political data

One common source of distributional data comes from atlas projects, which generally show the distribution of a range of species as presence/absence maps.

These type of data are commonly analysed in the scientific literature.

I will discuss the problems with using such datasets, both to identify specific limitations and illustrate broader issues.

This paper uses a dataset that shows the distribution of 3882 vertebrate species in 1957 1º grid squares in sub-Saharan Africa.

Each of these grid squares is approximately 105 km x 105 km and the presence or absence of each species in each grid square is recorded. They set a target of at least one representation for each species.

The authors then used a complementarity-based algorithm to identify the 50 sites that, when combined, would represent the largest number of species.

They also used the WWF ecoregion map to label each site according to the ecoregion that it falls within.

By failing to involve stakeholders they increase the chances of mis-naming areas or choosing unsuitable areas.

This is not montane grassland so reduces credibility of output

Problems of scale: Implementation

Problems of scale: Measuring representation

Data quality: distribution errors

Reddy & Davalos (2003). J. of Biogeography 30, 1719-1727

Data quality: sampling bias

There is no way of knowing whether protecting one representation of each species will be sufficient for their conservation or whether each population in each grid square is viable.

Basing an analysis on complementarity may maximise the efficiency of the final PA system but it might not necessarily protect viable populations when using presence/absence data.

Arbitrary targets

Beetle

Butterfly

Lizard

Toad

Tortoise

Beetle

Butterfly

Lizard

Toad

Tortoise

Beetle

Butterfly

Lizard

Toad

Tortoise

Beetle

Butterfly

Lizard

Toad

Tortoise

Beetle

Butterfly

Lizard

Toad

Tortoise

Beetle

Butterfly

Lizard

Toad

Tortoise

Beetle

Butterfly

Lizard

Toad

Tortoise

Beetle

Butterfly

Lizard

Toad

Tortoise

Beetle

Butterfly

Lizard

Toad

Tortoise

This phenomenon may have other serious implications

SouthAfrica

Lesotho

SWAZILAND

0 - 300

301 - 500

501 - 800

801 - 1000

1001 - 1300

1301 - 1500

1501 - 1800

Protected area8 PAs

3.8% of country

Natural = 59.5%

Degraded = 12.8%

Transformed = 27.7%

Transform ed

Degraded

Natural

No of record

s

No of species

Units sample

d

Range of

distrib.

No of spp

with 1 unit

range

Species richness range

Birds 18,255

476 101 1-101 23 108-307

Mammals

905 122 50 1-43 27 2-65

BirdsN = 101, rs = 0.09, P = 0.353

MammalsN = 50, rs = -0.05, P = 0.971

Proportion of natural vegetation

0.2 0.4 0.6 0.8 1.0

Bir

d s

pe

cie

s rich

ne

ss

15

20

25

30

35

40

45

Proportion of natural vegetation

0.2 0.4 0.6 0.8 1.0

Ma

mm

al s

pe

cie

s ric

hn

ess

0

2

4

6

8

10

Birds17 irreplaceable + 4 flexible

Mammals13 irreplaceable + 1 flexible

Flexible

I rreplaceable

Flexible

I rreplaceable

0.00

0.25

0.50

0.75

1.00

Not selected Selected

Pro

por

tion

of

natu

ral v

ege

tatio

n (

+1

SD

)

Outside PAs

Contains PA

0.00

0.25

0.50

0.75

1.00

Not selected Selected

Pro

port

ion

of

natu

ral v

ege

tatio

n (

+1

SD

)

Outside PAs

Contains PA

Birdsdf = 3, Χ2 = 0.677, P = 0.879

Mammalsdf = 3, Χ2 = 1.226, P = 0.747

No of record

s

No of species

Units sample

d

Range of

distrib.

No of spp

with 1 unit

range

Species richness range

Birds 18,255

476 101 1-101 23 108-307

Mammals

905 122 50 1-43 27 2-65

The selected planning units were still not significantly less transformed than other units.

One reason for this might be the number of species that were only recorded in one planning unit.

67 % transformed

28 mammal species recorded

Only record of Kuhl’s pipistrelle

Conclusions

• Swaziland species distribution data were not sensitive to levels of agricultural and urban transformation.

• This was partly driven by the number of species that were only recorded in one planning unit, which may have been exacerbated by under-sampling.

• Species list data should ideally only be used for coarse-scale planning exercises, whereas finer scale exercises should include data that relates to ecological viability.

The recorded data came from the Southern African Bird Atlas Project (SABAP), which describes the distribution of the region’s bird species in a series of ¼ degree grid squares. The data were collected by a series of expert volunteers. Each square was visited a number of times and a list of recorded species was compiled on each occasion.

Each record of each species in each grid square was then compiled and stored in a central database.

Other effects of sampling bias

Rec

ord

num

ber

Spe

cies

num

ber

Point 8 – The data is affected by sampling bias

The land-cover map had an overall accuracy of 86.9 % and a resolution of 30 m.

It contained 29 natural and 5 transformed land-cover types.

Recorded distribution

Cloud cisticola

Number of recorded squares = 6

Associated with woody & hygrophilous grassland

Recorded distribution

Cloud cisticola

Number of recorded squares = 6

Associated with woody & hygrophilous grassland

Recorded distribution

Modelled distribution

Recording success = 0.462

Cloud cisticola

Number of recorded squares = 6

Number of modelled squares = 13

Distinctive species were classified on the basis of their appearance and/or song.

Distinctive appearance:Plumage, bills or legs that contained red, yellow, pink or purple coloration.

Bills or tails that were more than 50 % of their body length.

Body length of > 80 cm

Distinctive song:

Described as “loud”, “characteristic”, “penetrating”, “far-carrying”, “raucous”, “strident”, “booming” or “piercing”.

n = 429

t = -2.825

p = 0.005

The relationship between recording success and distinctiveness was tested and there was a significant difference between distinct and nondescript species.

1 0 0 k m

A ll & d is tin c tiv e

D is tin c tiv e

A ll

Using the distinctive and complete data sets, only 7 of the 17 grid squares selected were the same.

This shows that it might not always be best to include all available data.

• This suggests that species distributions should be modelled to produce fine-scale data, rather than using raw presence/absence data.

• Vegetation/geology/soil etc maps can provide much more reliable data.

• Recent landcover data is also important.

• Point location data reduces flexibility in the system, helping to anchor larger PAs.

MOST PLANNING EXERCISES USE WHATEVER ADEQUATE DISTRIBUTION DATA ARE

AVAILABLE.

Conservation valueH igh

Low

Vegetation types

Forest types

Threatened tree species

Threatened vertebrate species

MARXAN acts to minimise the planning unit costs and these can be based on:

• Area

• Financial value

• Human population density

• Risk

• Opportunity costs

• Etc

Probability of being cleared related to elevation, slope,

geology and distance to agriculture

Risk of agricultural transformation

P ro te c te d A re a

A g r ic u ltu ra l la n d o r w a te r

0

1

T ra n sfo rm a tio np ro b a b il i ty

2 0 k m

The spatial distribution of bark stripping

Game ranching profitability in Maputaland

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140

PERPENDICULAR DISTANCE (m)

DE

TE

CT

ION

PR

OB

AB

ILIT

Y

Targets

• 40% original extent of threatened and endemic landcover types

• 20% original extent of other natural landcover types

• 25% of natural landcover in each communal area

Cost of each planning unit =

US$10,000 – Potential profitability from game ranching

Revenue '000s US$

Non-tribal areas 1967

Mathenjwa 409

Tembe 1852

Nyawo 670

Mngomezulu 285

Mashabane 256

Mabaso 699

Siqakatha 27

Zikhali-Mbila 87

Myeni-Ntsinde 67

Manukuza-Jobe 513

Myeni-Ngwenya 301

Mnqobokazi 238

Qwabe-Makhasa 32

Nibela 59

Mpukunyoni-Mkhwanazi 13

Total revenue 7476

Setting targets

Setting targets is a vital part of systematic conservation planning and the target values have a profound affect on the final conservation portfolio.

Setting targets

Original targets were often political, eg

10% of the planet

12% of original cover

Setting targets is a vital part of systematic conservation planning and the target values have a profound affect on the final conservation portfolio.

These have been criticised for a lack of biological relevance, with targets of 50% having been suggested.

Also problematic because they assume all elements are equal.

Instead, it might be preferable to set individual targets based on criteria such as:

• Endemism

• Red list status or other measures of threat

• Life-history characteristics

• Original extent

• Non-biodiversity value (eg watersheds)

• Genetic diversity

•Expert workshops

•Estimates of minimum viable populations

•Species/area curves for habitat targets

Setting targets

Setting targets is a vital part of systematic conservation planning and the target values have a profound affect on the final conservation portfolio.

0.00

0.25

0.50

0.75

1.00

0 50 100 150Area sampled

Pro

port

ion

of s

peci

esType A

Type B

Work developed by Desmet & Cowling (2004) has used species/area curves to estimate the amount of a landcover type that is needed to represent 90% of plant species.

MARXAN and clumps

Another feature of MARXAN is that it can include information on patch and population size of the conservation features. MARXAN uses the term “clump” to describe these characteristics and defines clumps using the following parameters:

1) Clump distance

2) Clump size

3) Clump target

Clump distance

This is the maximum distance between planning units, below which it is assumed that units belong to the same clump. These distances are measured from the centre of each planning unit.

Clump size

This is the minimum size for a viable clump. The size of each clump is measured by combining the amount of the conservation feature found in each of the associated planning units.

A possible priority setting exercise for CWRs in Europe

• Use existing atlas, landcover and PA data

• Use clump option in MARXAN to identify different populations

• Set targets based on minimum number of populations

A possible priority setting exercise for CWRs in Europe

Analyses:

• Identify populations in grid cells within no associated PAs

• Identify irreplaceable grid cells based on minimising the amount of unsuitable land found within the grid cells

• Carry out field work to check the presence of viable populations of CWRs in PAs in irreplaceable cells

Systematic conservation planning should be considered as a form of adaptive

management.

Outputs will change as more information is added and refined.

However, there is already sufficient data to produce a useful initial product and these outputs would be the most effective at identifying where CWRs should be conserved.