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 Lawton Report implies a need for conservation planning at a landscape level Does not seem to have formed part of local authority policy Local authority policy takes a landscape view of green space for social factors (eg. distance to/accessibility of green space), but not for biodiversity. Ecological impact assessments tend to be at a site level. (Urban) Ecological literature and policy recommendations suggest that this will not suffice and that a landscape level approach is necessary.

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 Objective shows the focus of this talk: sensitivity analysis of model based on start occupancy pattern. This is to check the suitability of widely available (citizen science) data.

Incidence function model (IFM) Spatially realistic metapopulation model Low data requirements Urban landscapes are fragmented Spatially realistic: can be used on real landscapes Low data req: should be able to parameterise and run the model on widely available data

Incidence function model (IFM) Time (t) = 0 Demonstration of how IFM simulates patch occupancies. This slide shows initial patch occupancy: circles represent suitable habitat of varying sizes, everything else is uniformly unsuitable. Filled circles are occupied by focal species, empty circles are empty patches. This serves a purpose as a graphical illustration of the low data requirements of the IFM: presence/absensce, patch size, patch location.

Incidence function model (IFM) Time (t) = 0 t = 1 Move to time t=1, there is an extinction event. Extinction is simulated with a probability that is a function of the patch area.

Incidence function model (IFM) Time (t) = 0 t = 1 At time t=2 there are 2 colonisation events. These occur with a probability that is a function of patch connectivity; this in turn is a function of the distance between patches, the species dispersal and characteristics of neighbouring patches (occupied, area) t = 2

Incidence function model (IFM) Time (t) = 0 t = 1 At t=3 there are both colonisation and extinction events t = 2 t = 3

Incidence function model (IFM) Time (t) = 0 t = 1 I am interested in whether a species persists in the landscape over time, and so keep track of the proportion of occupied patches at each time step. t = 2 t = 3

Implications of data quality Mis-estimated patch areas Habitat patches not identified False absences in species dataset Moilanen, 2002 I am looking at the 3rd point on this slide as the quality of the landscape data (Land Cover Map 2007 and Ordnance Survey Mastermap) is assumed to be high, but the species data does not have full coverage, so there is lots of potential for false absences.

Study Site and Landscape Data Study site is Nottingham City Council administrative boundary with a 2km buffer (to allow for dispersal from outside). Nottingham chosen as it is a typical UK city in terms of green space. LCM 2007 views urban/suburban areas as homogenous, but wildlife will use urban gardens for feeding etc. I have added gardens on to the landscape from OS Mastermap.

Species Data Data from Nottinghamshire Birdwatchers Map shows surveyed grid squares (1998-2011) Data from 1998-2009 grouped into 3 survey windows There is not full coverage of the area from all records. When looking at individual years, this becomes patchier. Have grouped into 4 year survey windows to get better coverage. Surveys are at DINTY level (ie. 2km sq).

Patch Occupancy 1. 2. Survey data Interpolated data (kriging) Random at surveyed % Random at interpolated % 3. 4. Explanation of how I have created 4 different starting patch occupancies to compare model performance each. 1st is based on survey data - crude downscaling by assuming species is present in any patch within grid cell where species is present (red square represents grid with species present). 2nd is interpolated from survey data to get higher patch occupancy using kriging. 3rd and 4th are testing how sensitive the model is to spatial structure by randomly populating the same proportion of patches as in 1 and 2. Looking left to right shows model sensitivity to level of occupancy in the landscape, looking top to bottom is sensitivity to spatial structure.

Blackbird (Turdus merula) ← Effects of occupancy level → Patches Occupied ← Effects of spatial structure → Photo credit: Oystercatcher on flickr Blackbird: ubiquitous generalist. Should prove as a reality check for the sensitivity analysis as it should be fairly stable in the landscape. Model is mostly sensitive to the effects of patch occupancy for this species, wheras spatial structure has little effect. The predicted quick extinction of the species using the survey data is possibly due to the fact that this species is under-surveyed. The lack of effect of spatial structure is likely because there are many suitable patches available in the landscape. Time

Corn Bunting (Miliaria calandra) ← Effects of occupancy level → Patches Occupied ← Effects of spatial structure → Photo credit: Steve Riall on flickr Corn bunting: declining farmland specialist, will be useful when looking at impacts of environmental stewardship schemes. Model is sensitive to both the occupancy level and spatial structure, but it is spatial structure that it is most sensitive to. Time

Marsh Tit (Poecile palustris) ← Effects of occupancy level → Patches Occupied ← Effects of spatial structure → Photo credit: Steffen Hannert Marsh tit: declining woodland specialist, will be useful for looking at the impacts of local nature reserves/changes to woodland. Model is sensitive to spatial structure, occupancy level and the interaction between the two in different ways. This species has the shortest dispersal distance and least suitable habitat patches: think that its colonisation ability is overestimated due to these factors in the random placement at low occupancy level. Time

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 Rather than parameterising on the whole landscape, I have parameterised on samples of 4 km sq (ie. where there are 4 of the 2 km sqs that are continuous) where I have greater confidence of the absences (meaning that the squares have been surveyed within the 1st and 2nd survey window for these grid squares).

Results of subsetting Patches Occupied Time The model predicts similar trends when run using the parameters gained by fitting to each sample (for the blackbird and marsh tit this is better than for corn bunting). Based on actual trends for the 3 species, the model seems to systematically overpredict. This is most likely down to the crude downscaling method outlined earlier (method one on slide 15), as such further research into downscaling is necessary, or more monitoring at site rather than grid square level is required. Time

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 Point One: Fits in with suggestions from policy documentation, eg. Biodiversity 2020. For the subsetting approach to fitting IFM this suggests a need for monitoring at a patch rather than grid square level, but good news is that a smaller (representative) area can be covered. Point Two: Many site lists of species exist, but are not necessarily shared, centralised databases may complement the above Point Three: If the IFM is to be used for decision making it should be to compare the differences between different management scenarios (ie. developments, new nature reserves etc.) rather than to be used as a predictive tool.