hanski’s incidence function model for urban biodiversity planning

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Hanski’s incidence function model for urban biodiversity planning Laura Graham, ESRC funded PhD candidate Supervisors: Prof. Roy Haines-Young Dr. Richard Field

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Hanski’s incidence function model for urban biodiversity planning. Laura Graham, ESRC funded PhD candidate. Supervisors: Prof. Roy Haines-Young Dr. Richard Field. Background. Importance of urban ecosystems to both human well-being and biodiversity prevalent in conservation policy - PowerPoint PPT Presentation

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Page 1: Hanski’s incidence function model for urban biodiversity planning

Hanski’s incidence function model for urban biodiversity

planning

Laura Graham, ESRC funded PhD candidate

Supervisors: Prof. Roy Haines-Young Dr. Richard Field

Page 2: Hanski’s incidence function model for urban biodiversity planning

Background

Importance of urban ecosystems to both human well-being and biodiversity prevalent in conservation policy

Lawton Report implies a need for conservation planning at a landscape level

Does not seem to have formed part of local authority policy

Page 3: Hanski’s incidence function model for urban biodiversity planning

Research Aim

To investigate the applicability of the IFM to urban biodiversity planning at landscape scale

by testing suitability of available data through sensitivity analysis

To use the IFM to explore policy questions of both local and national relevance

Page 4: Hanski’s incidence function model for urban biodiversity planning

Spatially realistic metapopulation model

Low data requirements

Urban landscapes are fragmented

Incidence function model (IFM)

Page 5: Hanski’s incidence function model for urban biodiversity planning

Incidence function model (IFM)

Time (t) = 0

Page 6: Hanski’s incidence function model for urban biodiversity planning

Incidence function model (IFM)

Time (t) = 0 t = 1

Page 7: Hanski’s incidence function model for urban biodiversity planning

Incidence function model (IFM)

Time (t) = 0

t = 2

t = 1

Page 8: Hanski’s incidence function model for urban biodiversity planning

Incidence function model (IFM)

Time (t) = 0

t = 2

t = 1

t = 3

Page 9: Hanski’s incidence function model for urban biodiversity planning

Incidence function model (IFM)

Time (t) = 0

t = 2

t = 1

t = 3

Page 10: Hanski’s incidence function model for urban biodiversity planning

Implications of data quality

Mis-estimated patch areas

Habitat patches not identified

False absences in species datasetMoilanen, 2002

Page 11: Hanski’s incidence function model for urban biodiversity planning

Study Site and Landscape Data

Page 12: Hanski’s incidence function model for urban biodiversity planning

Data from Nottinghamshire Birdwatchers

Map shows surveyed grid squares (1998-2011)

Data from 1998-2009 grouped into 3 survey windows

Species Data

Page 13: Hanski’s incidence function model for urban biodiversity planning

Patch Occupancy

1. Survey data

2. Interpolated data (kriging)

3. Random at surveyed %

4. Random at interpolated %

1.

4.

2.

3.

Page 14: Hanski’s incidence function model for urban biodiversity planning

Blackbird (Turdus merula)

Time

Patc

hes O

ccup

ied

← Effects of occupancy level →

← Effects of spatial structure →

Photo credit: Oystercatcher on

flickr

Page 15: Hanski’s incidence function model for urban biodiversity planning

Corn Bunting (Miliaria calandra)

Time

Patc

hes O

ccup

ied

← Effects of occupancy level →

← Effects of spatial structure →

Photo credit: Steve Riall on

flickr

Page 16: Hanski’s incidence function model for urban biodiversity planning

Marsh Tit (Poecile palustris)

Time

Patc

hes O

ccup

ied

← Effects of occupancy level →

← Effects of spatial structure →

Photo credit: Steffen Hannert

Page 17: Hanski’s incidence function model for urban biodiversity planning

Parameterise on subset of survey data

Parameterise on subsets comprising grid squares surveyed in both 1st and 2nd survey window

Run model for each set of parameters

Page 18: Hanski’s incidence function model for urban biodiversity planning

Results of subsetting

Patc

hes O

ccup

ied

Time

Page 19: Hanski’s incidence function model for urban biodiversity planning

Conservation policy implications

If IFM has potential to be used to for urban biodiversity planning:

Need for intensive monitoring and surveying

Need for more joined up and centralised databases

Compare relative effects of varying management scenarios