Appendix 3. Technical report:

Developing models to estimate the occurrence in the English countryside of Great Crested

Newts, a protected species under the Habitats Directive [WC1108]

Applications and case studies

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Summary

We demonstrate the usefulness of SDMs and connectivity analysis by outlining five possible applications of the models at different scales. Using a small-scale pond network, we show how connectivity analysis can be used to identify important linkages that may need to be preserved within development mitigation. Using a combination of SDMs, connectivity analysis and favourable reference values (FRV), we then assess the impacts of a large hypothetical development at the local and National Character Area scale. Within a wider landscape, we then show how SDMs can be used to identify clusters of newt ‘hot spots’ within different NCAs. Generalized Linear Models that are built using presence/absence data such as eDNA, have the advantage of being able to provide standard error estimates of the reliability of prediction in different areas: this can be used for targeting further survey efforts. Using a real pipeline mitigation study, we then demonstrate how SDMs, connectivity analyses and FRVs can be used to assess the potential impact of the pipeline. To achieve this we develop a new index called ‘Risk of harm’ , that relates the likely impact of the development on newts by taking account of pond occupancy, dispersal of newts and the distance of the pond in relation to the development. The ‘Risk of harm’ index can be implemented as a separate layer within the SDM. Ultimately, the metrics generated by the model can be used to populate the four components of FCS, and assess the conservation status of the newts before, during and after development. For the pipeline mitigation study, the models predicted that current conservation status (CCS) would be maintained during and after development.

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3.1. Introduction

We present a series of case studies where the modelling approach described in Appendix 1 great can be applied to a variety of applications. We also use an example case study to illustrate and describe connectivity analysis using Linkage Mapper and Connefor. The case studies comprise a combination of real and hypothetical scenarios, and aim to demonstrate how SDMs, connectivity analyses and FRV concepts can be integrated into evidence-based approaches for resolving issues concerning great crested newts. Following the steps described in the case studies should allow practitioners with access to relevant data and software to run models for their own applications.

3.2. Applying connectivity modelling to a pond network

Here we illustrate how SDMs and network theory-based connectivity analysis can be combined to (1) overcome some of the shortcomings of SDMs at the local scale (Appendix 1); and (2) provide some information on managing great crested newt at the metapopulation level. The scenario uses a network of ponds distributed over 4 km2 , the SDM model we built for Kent (section 2.1, Appendix 1) and some additional information on land cover for the area (road network).

Our case study is the pond network in Figure 1. We assume no information on great crested newt or pond status in this specific area, but since the area is in Kent we do have access to a relatively reliable GLM SDM (section 2.1) and a range of landscape and environmental data (sections 1.1, 1.2). Since we have no access to pond-based variables in this case, we will use an alternative approach, and will view the ponds present in the area as a network and based on the occupancy values we derive from the SDM (Figs. 2-3), gain more information on the predicted status of great crested newt that could help inform a variety of applications.

Figure 1. The pond network in the case study area.

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Figure 2. The GLM SDM for Kent (Appendix 1) zoomed-in on the case study area.

Figure 3. The occupancy probability for the 12 ponds in the area. Most of the ponds with medium to low values corresponds to ponds within or close to the urban area.

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Most network-based approaches to connectivity hinge on two core ecological processes (with relevant species-specific attributes): dispersal within a landscape and total area or quality of each patch. In our case the latter is assumed to refer to the quality of each pond as derived from the SDM. Thus the occupancy value for each pond may be an indicator of how good the pond is for great crested newt.

Dispersal within a landscape is more complex to model, and usually involves knowledge of how far the species can move as well as information on how easy it moves through different types of habitat. We know that on average, great crested newt disperse around 400 m (Joly et al. 2001) while rare long range dispersal has been recorded up to 1000 m (Griffiths 2004; Karlsson et al. 2007). Regarding movement, it is generally assumed that species move easier, faster and further in suitable habitat, thus quite often the inverse of the SDM layer is used as a resistance layer (Fletcher et al. 2014; French et al. 2014). This resistance layer is refined using spatial information on roads and rivers, both of which are known barriers to great crested newt dispersal (ibid, Fig. 4). It is further assumed that during dispersal great crested newt would choose paths that are “easier to traverse”, i.e. would accumulate less resistance. Thus using the resistance layer and efficient algorithms (available in many GIS software and landscape ecology tools) least-cost paths can be calculated (Adriaensen et al. 2003), namely dispersal paths that represent the “path of minimum resistance (or friction)” between two ponds.

After determining the least-cost paths and using information on pond suitability (since pond occupancy ≈ patch quality) we can address three questions at the metapopulation level: (1) How does each pond contribute to the metapopulation? (2) How does each least-cost path (hereafter LCP) contribute to the metapopulation? (3) What is the functional connectivity at the metapopulation level?

Figure 4. The inverted SDM – with the addition of road barriers – can be seen as a resistance-to-movement layer. High values (light colour) indicate high resistance to movement; low values (dark colour) indicate low resistance. Ponds shown as blue dots.

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To address these questions we use Conefor, a graph theory software package (www.conefor.org) for modelling the importance of habitat patches (in our case ponds) and LCPs for landscape connectivity. Graph theory is a useful tool for assessing connectivity and the importance of individual patches in a landscape for a variety of organisms and habitats (Urban et al. 2009; Saura and Rubio 2010), including amphibians (Decout et al. 2012). Conefor is among the standard software applications used for modelling connectivity in landscape ecology and has been used both in scientific studies, policy development and national level indicator assessments (see the applications page on the software’s webpage, http://www.conefor.org/applications.html). It provides a variety of metrics for assessing node and LCP importance, as well as overall connectivity metrics concerning entire clusters of ponds, metapopulations or landscapes.

Connectivity analyses can be a complex exercise involving a suite of software and GIS-based techniques and a significant amount of expert knowledge decisions. Because of that, we will provide a discussion and illustration of the various metrics that can be calculated through a brief outline of the methodology in terms of the steps needed using ArcGIS (Basic Licence), Linkage Mapper, Conefor Inputs (or QGIS) and Conefor:

- We create the resistance layer of the area under study by inverting the occupancy (or suitability) layer1 and adding possible barriers to dispersal such as roads and rivers. The area of extent would ideally not be smaller than a potential metapopulation of newts (e.g. >5 ponds with a more-than-medium probability of occupancy) and depending on computing power could include 1000s of ponds (using a powerful seven core, 32 Gb RAM desktop running the LCP analysis for c. 600 ponds took 14 hours; for 100 ponds it took less than one hour).

- We select the ponds that have a probability of being suitable (depending on the model a threshold can be chosen) and use Linkage Mapper2 to create the LCP between the ponds. Linkage Mapper can be used to identify ponds closer than 1000 m (1000 m is the maximum dispersal distance of great crested newt; e.g. Griffiths, 2004).

1 Using the approach we describe below, a suitability layer is necessary. Thus, even if great crested newt occupancy modelling has been done at the pond level, a separate SDM model has to be created for use as a resistance layer. Alternatively, another approach for building a resistance layer is to assign resistance values to land cover types using knowledge about GCN movement during dispersal.2 A Linkage Mapper tutorial is available here http://www.circuitscape.org/linkagemapper.

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- Linkage Mapper produces a host of output files, and for this exercise we are interested in the LCPs (Fig. 5) file, which lists the LCPs along with their Euclidean distance (m), LCP distance (m) and Cost-Weighted Distance (CWD: the sum of all resistance layer cell values the path crosses). Using LCP distance we can further reduce the number of paths (if LCP distance is > 1000 m, i.e. more than the maximum dispersal distance of great crested newt). Using Cost Weighted Distance we can actually “simulate” barriers to movement for example by setting the resistance value of a land cover feature very high compared to the other cells and land covers (e.g. 1,000,000 while the maximum in Fig. 5 is 870), and then deleting all LCPs that cross the 1,000,000 CWD value.

Figure 5. LCPs for the pond network in the case study area. For the pond at the far left of the figure, while less than 1000 m from the closest pond, we did not retain the LCP connecting it to the network because it was > 1000 m (1200 m). LCP created using the freely available add-on software to ArcGIS Linkage Mapper (McRae and Kavanagh 2011; http:// www. circuitscape. org/ linkagemapper ).

- Next, we export the LCP file’s attribute table to a Conefor-ready format (that is Conefor’s “distance file”, see tutorials that are bundled within the Conefor download zipped file). Then, we export the Conefor’s “node file”, which is the pond’s attribute table along with each pond’s occupancy probability value in the Conefor-ready format.

- We then calculate LCP and pond importance attributes for our pond network. Saura and Rubio (2010) suggest the use of probabilistic indices of connectivity instead of the threshold based one3 . We choose the same probabilistic index (Probability of Connectivity, hereafter PC) for all types of analysis (pond, link, overall network) setting the distance value at 1000 m and the probability of dispersal at this distance to 0.05 (Griffiths, 2004). The results of pond and link importance based on the PC index can be seen in Fig. 6. For ponds, PC is a function

3 Threshold indices of connectivity use a cut-off distance in their calculation, related to the dispersal of the species. Probabilistic indices allow the user to set two values: one for distance, and for the percentage of successful dispersal at that distance.

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of its position within the network and its suitability for great crested newt (in our case

occupancy probability).

Figure 6. Pond and LCP importance using the PC index for the network of ponds in the case study area. dPC is the probability of connectivity index.

Connectivity analysis therefore provides significantly additional information to the initial SDM (Fig. 6). It provides a picture of the pond network of the area, as well as information on the metapopulation structure for great crested newts. It identifies potentially important dispersal corridors in the form of LCPs, as well as a pond importance hierarchy based both on the position of each pond in the network and its suitability for great crested newts. This output could inform decision-making processes for a variety of applications. For example:

- The circled pond in Fig. 6 could effectively be destroyed without affecting the great crested newt population in the area, since it is not part of the metapopulation. Even if it does indeed have a great crested newt population, it is highly unlikely that this population would be able to persist (Griffiths and Williams 2000; 2001).

- Unimportant LCP could possibly be severed (by a road for example) without affecting the great crested newt metapopulation.

- The hierarchy of ponds based on PC could be used to prioritise pond conservation, either by diverting effort from unimportant ponds, or by focusing effort on those ponds (e.g. to increase their suitability by habitat works).

However, connectivity analysis comes with a very important qualification: the approach has not been validated on the ground. Thus, while it is arguably the best approach available, there is no way of knowing if, for example, great crested newts actually use the LCPs as modelled with this methodology, or if ponds with high PC values do indeed provide important nodes for the network (e.g. acting as sources for the metapopulation). That said, identifying dispersal corridors for great

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crested newts empirically is itself fraught with difficulties and assumptions. We therefore strongly suggest a field survey validation exercise is carried out for a modelled case study such as this.

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3.3. Determining the impacts and mitigation measures of a development using Favourable Conservation Values, species distribution models and connectivity analysis

Here we apply the practical and conceptual tools described in Appendices 1 and 2 to estimate the impacts of a large development project and propose possible ways to mitigate them. We accomplish this linkage using an anonymised scenario that uses real data. Thus, while the all the analyses and maps presented are based on real data (pond location, real SDM, land cover, and development boundaries) the precise area and type of the development along with the National Character Area will remain anonymous. We combine the GLM SDMs and connectivity analysis with the rationale developed for the FRV and FCS part of project.

Possible indices of current conservation status used:

- % change in Area of Occupancy (Habitat-related FRV)- % change in the number of occupied ponds (Habitat-related FRV)- Landscape connectivity indices as indices of future prospects after a mitigation (Future

prospects-related FRV)- Effects on significant populations – clusters of ponds high probabilities of occupancy

(Population-related FRV)

The mitigation scenarios presented that relate to the NCA scale should not be interpreted as suggestions, but as exercises to test the validity of our approach. We group them into two types:

- An equilibrium approach - An approach that uses past benchmarks as FRVs for large-scale projects

3.3.1 The scenario

A developer is about to ask for planning for a new airport somewhere in England, in an area that is known to have ponds. Due to the size of the area to be developed (3 km2), it is expected that a significant number of ponds will be affected. The local council wants to know several things at two scales: the scale of the particular development (extending 1 km around the development area) and the relevant National Character Area (NCA) scale:

1. How many suitable ponds will be affected?2. Considering the size of the project, could it have an impact on the current conservation

status of great crested newt?4

3. What would the impact be on the local area, i.e. on a 1 km buffer around the development?

4. How can the impact be mitigated, considering that for an airport the mitigation cannot be made on-site?

3.3.2 The approach used

4 The National Character Area scale was discussed with Natural England as most relevant for this study.

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The development site has not been surveyed for newts before, so there are no data on pond occupancy. However, the council does have access to an Ordnance Survey pond dataset and to reliable, representative great crested newt eDNA records5 and publically available land cover data, it decides to use the SDM approach. An SDM was built using GLMs and the eDNA dataset mentioned above, and the probability of occupancy for every 25 m cell of the area was mapped. Using this map, the council attempted to answer questions 1-4 as follows:

How many suitable ponds will be affected?

Based on the SDM and the pond dataset for the NCA, the council’s GIS specialists extracted each pond’s probability of occupancy (Fig. 7). Then, using the thresholds generated by the models, ponds were categorised as occupied and unoccupied. In cases where confidence intervals overlapped the threshold6, the pond was categorised as occupied, in accordance with the council’s view of minimising possible risk to great crested newt. Based on the results of this exercise, it turns out that 11 occupied ponds will be destroyed on site.

Figure 2. Map of the NCA and the ponds that it contains, the latter coloured according to occupancy probability as predicted by the SDM.

Impact on the current conservation status of great crested newt at the scale of the relevant National Character Area (NCA)

Considering the size of the development, the council used three indices of conservation status:

- The predicted % drop in the total number of occupied ponds at the NCA scale - The predicted % drop in the Area of Occupancy (AoO), calculated as the sum total of all cell

values in the NCA divided by the total area of the NCA

5 A reliable and representative great crested newt datasets should have at least the following properties: (a) provide an indication of the number of false absences; (b) include the area in its variable space, meaning that predicting over the development area should not be an extrapolation (checked using a MESS layer- see Appendix 2), (c) depending on GCN occupancy, a relatively large amount of eDNA samples (>100).6 For example: if the threshold for declaring a pond occupied is 0.41 and a pond according to the model had a probability of occupancy of 0.45 with a lower confidence interval was 0.33, the pond was categorised as occupied.

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- The possible destruction of a highly significant area for great crested newt at the NCA scale

Using the same methodology described in section 3.1 to predict the probability of occupancy for each pond, the council determined that the 11 ponds that would be destroyed if the project were to get planning permission and go through without any mitigation are 1.5% of the total number of occupied ponds in the NCA (out of total of 691).

To assess the % drop in AoO the council took the following approach. Based on the SDM map, it calculated the total AoO as the sum total of all cell values (MacKenzie et al. 20067), and divided that number by the count of cells in the NCA. To calculate the drop, it excluded from the total AoO number the cells that represent the area to be developed, resulting in a drop of 0.9% drop in AoO.

To develop a spatial understanding of the clusters of highly suitable aggregations of ponds, the council’s GIS specialist used a method called Getis-Ord Hot-Spot Analysis, following an approach by Rissler and Smith (2010). This approach is implemented in ArcGIS’s Spatial Statistics Tools maps and provides confidence intervals for where high and low occupancy probability values cluster. The council can visually interpret these maps (Fig. 8) and discern if the proposed development would impact on a cluster of ponds with high probability of occupancy. Seven clusters were identified at the 90% confidence interval, and one of these is in the area proposed for development (not shown to maintain anonymity).

Figure 8. Hot Spot Analysis of the probability of occupancy for all the ponds in the NCA. Blue colours represent cold spots, i.e. clusters of low probability of occupancy, while reddish areas represent clusters of ponds with high occupancy probability. Clusters of ponds with a high probability of occupancy are circled.

What would the impact be on the local area?

7 Thresholding the map and summing all cell values are two different approaches that do not always give the same values for AoO; Guillera-Arroita et al. (2015) propose summing.

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To assess the impact on the local area, the council calculated the effect of the development on the connectivity of the assemblage of ponds in a 1 km buffer around the development, 1 km being the maximum dispersal distance recorded for great crested newt. The approach taken utilised graph-theoretic approaches to assessing connectivity, as these have been repeatedly used in both scientific and practical settings (Urban et al. 2009; Saura and Rubio 2010), including in cases for which amphibians are concerned (Decout et al. 2012; Clauzel et al., 2014; Fletcher et al. 2014; French et al. 2014). A resistance-to-great crested newt-movement layer was built by inverting the SDM (Fletcher et al. 2014; French et al. 2014) and adding barriers to great crested newt dispersal (roads, rivers, development8) and finally clipped to the 1 km buffer around the development. Then, using specialised software (Linkage Mapper with Circuitscape and Conefor – see previous case study), indices of connectivity were calculated:

- Probability of Connectivity (PC) that increases as landscape connectivity increases- Integral Index of Connectivity (IIC) that also increases as landscape connectivity increases

The indices’ probability of occupancy-weighted forms PC (EC) and IIC (EC) were also calculated. These indices are derived from a combination of graph theory and ecology, and assess the extent to which a landscape is connected. If left unmitigated, the development would have a significant impact on great crested newt population connectivity in the landscape (Table 1).

How could the project be mitigated, considering the mitigation cannot be made on-site?

Considering the significant losses in the number of ponds, AoO, high value clusters and connectivity, the council wants to make sure that the developer has a sound mitigation plan. During the meetings of planners and councillors with the ecology team assembled by the developer, the following plan was devised to identify possible areas for compensation and the creation on new ponds.

The new ponds would have to be as close as possible to the ponds scheduled to be destroyed by the airport development. However, the council thought that as a last resort - and after all options had been ruled out - they would consider compensating the development to another area within the NCA. To identify potential areas for mitigation that would be both suitable for great crested newt and not already saturated with occupied ponds:

- they selected areas that would be suitable based on the SDM and according to the threshold as they did before with the ponds;

- and from these areas they selected the areas that had less than four ponds per km2 (Fig. 9).

For mitigating in the local area (i.e. the 1 km buffer around the development), the council suggested adding as many ponds as possible within the areas that were identified as suitable and containing < 4 ponds per km2. To assess the positive impact this addition would have on the connectivity of the great crested newt population, they ran another connectivity scenario that included seven new ponds – a mitigation scenario (Fig. 10). The addition of ponds does increase the overall connectivity of the landscape for the great crested newt, although it does result in some overall losses (Table 1).

8 In building the resistance layer, we followed the approach by Fletcher et al. (2014).

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Figure 9. Map of the NCA showing areas with less than four ponds per km2.

Figure10. Zoomed-in map of the area of development. The site has at least three areas around it within the buffer with < 4 ponds per km2. Development area kept out of the map to retain anonymity.

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Table 1. Connectivity indices for the different scenarios regarding the airport development: before the development i.e. the current state; unmitigated development, and after new ponds have been created.

Scenario

Integral Index

of

Connectivity

Integral Index of

Connectivity

(probability of

occupancy

weighted)

Probability of

Connectivity

Probability of Connectivity

(probability of occupancy

weighted)

Current state 37.2 6.09 9.8 3.13

Unmitigated

development5.87 2.42 4.53 2.12

Development

with

mitigation

16.54 4.06 6.9 2.62

Considering how to mitigate at the NCA scale, the council decided that the airport developer would have to add some new ponds elsewhere too. To decide on the number the council did not use the ‘no-net-loss’ rule, as it did with the connectivity metric, but considered as a benchmark the time around which the Habitat’s Directive was signed, the early 1990s. For reasons discussed in Appendix 2, this may be an unrealistic benchmark that in reality would need to be adjusted, but for illustrative purposes we retain it for this example.

Considering a 4% reduction in the number of ponds per decade, and a FRV based on an early 1990s, benchmark, the council calculated that 46 new ponds needed to be created to achieve FCS (Table 2).

Table 2. Calculations for the two types of mitigation plans at the NCA scale.

2015

Equilibrium no-net loss

approach

1990

Past benchmark approach

Total number of ponds 1545 1678

Total number of suitable ponds 686 (44.7%) 736*

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Ponds to cover the loss of the 11

ponds

11 – 7 offset locally = 4 736 – 686 – 11 (lost) + 7 (new)

= 46

* Calculated as using the same current suitable/unsuitable percentage.

To account for loss of 0.9% AoO and one great crested newt population (as identified by the Hot-Spot Analysis), it was deemed appropriate to group the 46 ponds in two assemblages, and create new ponds in suitable areas that are not already saturated with ponds9. Regarding such risks as pond failure, climate change impacts, inadequate pond management or disease, it was considered that pond creation may need to consider an alternative ratio to that provided by 1:1 compensation.

9 Such large mitigation schemes are not very common. However, considering that the hypothetical scenario refers to a development as large as an airport, we could compare with an equally large development, the London Gateway port in Essex. According to Thomson Ecology who were the consultants in charge: ‘We have also completed many great crested newt mitigation projects including the largest great crested newt translocation, at London Gateway Port, that has ever been completed in the UK. This included the construction of 40 ponds and the creation of 35 ha of terrestrial habitat, with some 15,000 plants and trees planted as scrub.’ (http://www.thomsonecology.com/great-crested-newt-mitigation-guide).

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3.4. Targeting eDNA surveys based on uncertainty mapping

An advantage of using presence/absence models over models that use only presences (or presences/pseudo-absence, as in Maxent), especially in a regression framework, is the ability to have standard errors and thus confidence for the prediction maps – the SDMs (Marley et al. 2014). In our modelling framework, GLMs (versus Maxent, ensemble models and SVM) not only give standard errors and confidence intervals for the beta values in the models, but also for the predictions. Thus standard error (SE) maps can be obtained, as well as low and upper confidence interval maps for each GLM SDM (Figs 11-13).

Figure 11. Map of prediction of standard errors for the Kent GLM. Each 25 m x 25 m cell of the map represents a prediction from the model, with its own standard error and upper and lower confidence intervals.

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Figure 12. Map of low confidence interval for the Kent GLM SDM. Notice that areas of high probability of occupancy are less green and that areas with low probability of occupancy are redder compared with the occupancy map (Fig 13 below). Low confidence interval does not mean low occupancy probability; for example a cell with a predicted value of occupancy 0.8 could have a 95% confidence interval [0.76 – 0.83]. That means that we can be confident that the real value of the parameter (according to the model) lies within this interval.

Figure 13. Occupancy probability map of Kent.

A potential application of the SE map could be the spatial targeting of further eDNA surveys to improve the accuracy of the models. Suppose a county council is interested in improving its existing great crested newt SDM to make it more reliable and accurate for use in strategic planning or zoning

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across the county. Using an exsiting SDM and extracting the SE map from it, it could target areas with high SE for more eDNA surveys, thereby reducing the total area to be sampled.

In combination with a MESS map (see Appendix 1), a council (or any organisation interested in targeting great crested newt surveys) could significantly reduce the sampling effort required for the production of reliable SDMs.

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3.5. Favourable Reference Values using models – assessing current

conservation status at the NCA level

This part of the report is a suggestion on how to measure some components of Current Conservation Status (CCS) using model-derived Reference Values (RV). The core issues and methodologies have been identified and are laid out below using examples at different scales. We envisage a CCS assessment based on four core RVs: population, range, habitat and future prospects (Appendix 2).

3.5.1 Range

Metric: Extent of Occurence (EoO). EoO is a commonly used metric “of the overall geographic spread of the localities at which a species occurs” (Gaston and Fuller 2009). It can be calculated as the area that is included within a convex polygon drawn around the outer (marginal) localities a species has been recorded. As an example, we have drawn such a polygon around the presence records for the Low Weald NCA in Kent (see Fig. 14). The EoO of great crested newt for the Low Weald NCA as % of the total NCA is 97%.

Figure 14. Map depicting the convex polygon drawn around the marginal localities great crested newt has been recorded in Low Weald. The convex polygon was clipped to the NCA area to allow for the calculation of its area within the NCA.

3.5.2 Habitat

Metric: Area of Occupancy (AoO). AoO is the area that lies within those outermost limits (EoO), i.e. the total area over which it actually occurs. It is by definition smaller than the EoO10 and is a commonly used index of species range. While there is a variety of ways of assessing AoO, using SDMs it can be assessed fairly quickly as the total sum of cell values of the SDM map (values range

10 When AoO is defined using SDMs that only take into account bioclimatic variables or when it is developed at a coarse scale it tends to be confounded with the EoO because it does not take into account biotic interactions, landscape configuration, fragmentation etc. The GLM model we developed for Kent is possibly a good predictor of AoO, since it is developed at a fine scale while also taking into account landscape configuration (distance to woodland, arable land, grassland; amount of various land covers at 250 m radius around each cell).

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from 0 – 1; Table 3). Using regression based SDMs, confidence intervals (upper and lower limits) can also applied to the AoO number.

Regarding the GLM for Kent, for all NCAs apart from the Low Weald, the prediction rests on a significant extrapolation, and this is what creates this low variation in the area of occupancy. For the Maxent models for Romney Marsh and the Low Weald 98% and 74% may be unrealistically large.

Table 3. Areas of occupancy for each of the NCA that fall within Kent. GLM SDM using eDNA presence/absence data is compared with a Maxent model using presence-only data.

NCA AoO (GLM) (% total NCA area) AoO* (ARC, Maxent) (% total NCA area)

Greater Thames

Estuary 52 40

North Kent Plain 43 25

North Downs 38 14

Wealden Greensand 45 37

Low Weald 55 (compare with EoO, 97%) 74

High Weald 51 68

Romney Marshes 51 98

* It is becoming increasingly recognised that AoO cannot be assessed using Maxent due to fact that it is by default a pseudo-absence method. Even when used with real absences, its raw output needs to be transformed to produce real estimates of AoO.

3.5.3. Number of populations

Metric 1: visual interpretation of a Getis-Ord Hot-Spot Analysis (GOHSA). GOHSA is able to quantify clustering of high and low occupancy (or suitability) values based both on Euclidean distance and the occupancy probability from an SDM (Fig. 15). As a method it has been used extensively to quantify spatial clustering for a variety of applications: from amphibians, to crime incidents, to graffiti removal and many others. It is a measure that can be assessed visually and provide an indication about the number of great crested newt distinct clusters in a county or NCA. However, it does require some interpretation and a relatively good knowledge of the great crested newt populations of the area of interest. For example, for areas with many highly suitable ponds (circled in Fig. 15), the metric is not able to provide a clustering because the many highly suitable ponds are very close together. Thus a simple count of the clusters as provided by GOHSA may not be sufficient and a visual interpretation in combination with the other metric proposed is warranted. A caveat of the method is that it would not be able to identify clusters in highly unsuitable areas. Nevertheless, an approach such as the one developed in case study 3.6 (see Number of populations metric and Fig. 22) below could be an alternative.

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Figure 15. GOHSA map of clustering of high and low pond occupancy values for great crested newt from a GLM great crested newt. Some of the ponds in the NW belong to a different NCA, but they are not enough to warrant its inclusion.

Metric 2: Number of suitable ponds. Metric: % of suitable ponds per NCA as these are derived from the SDM either using a threshold or by avoiding a threshold and adding all the pond occupancy (or suitability) values and comparing with the total number of ponds. We convert this number to a percentage to allow for comparison between scales and locations. This number can differ from the AoO because of the spatial clustering of ponds. For example, in the North Kent Plains, while AoO is 45%, the percentage of suitable ponds is 55% based on the GLM model, and 25% versus 31% respectively for the Maxent model.

3.5.4 Future prospects

Future prospects is possibly the most problematical RV to model. It will possibly have to take into account all the above metrics, as well as expert judgement. We present options that are relevant for different scales.

Indirect metric 1: Land use change. A trend in land changes that affect great crested newt could be ascertained from already existing and continuously updated maps of land cover change. For

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example, we know that great crested newt favours areas with a certain amount of woodland, good quality grassland and possibly hedgerows. In most areas of England there are detailed maps available going back at least to the 1990s (the Land Cover Map 1990), while other areas have digitized maps that go back to the 1960s, such as Kent. A land cover change analysis is simple enough to be conducted by any GIS expert and could provide some information on future prospects if trends can be discerned.

Indirect metric 2: connectivity. Another metric for assessing future prospects could be landscape connectivity. There is a variety of methodologies available and in our case we suggest graph-based approaches (see case study 3.3, Table 1 and case study 3.6 Fig. 22) that have been used before for amphibians and great crested newt (Decout et al. 2012; Clauzel et al., 2014; Fletcher et al. 2014; French et al. 2014). Plus, the localised habitat and relatively small dispersal distance of great crested newt make it suitable for such an analysis.

We propose two metrics, Probability of Connectivity (PC) and the Integral Index of Connectivity (IIC) that both increase as landscape connectivity increases that can also be standardised for pond quality or probability of occupancy if such data exist (e.g. from an SDM; Saura and Rubio 2010).

Of course, the assessment of future prospects for any species has to take into account several additional sources of uncertainty such climate change, risks of disease, management quality (at the local scale). Possibly, expert knowledge and judgement could be used to reduce levels of uncertainty, especially if combined with tools designed to incorporate such knowledge and information into evidence-based decision making, such as Bayesian Belief Networks (Newton et al. 2007).

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3.6. Hull Gas Pipeline mitigation

In this case study we investigate alternative approaches to great crested newt survey and mitigation using a combination of predictive modelling, a risk-based approach and FCS principles. We set out how a real great crested newt project was addressed using a conventional approach to good practice in survey and mitigation. We then examine how the techniques developed in this contract could potentially be used to address the conflict differently. Note that this does not indicate any criticism of the original work; we have simply chosen this case because it illustrates key points in mitigation casework.

3.6.1. Summary of the case study: Easington to Paull High Pressure Gas Pipeline

Background. The case study involves the construction of a new high pressure gas pipeline near Hull in the East Riding of Yorkshire. Surveys were undertaken to assess the potential impacts on great crested newts in 2007 and 2009. Using these results as a basis, mitigation works were carried out under a Natural England licence in 2010. Ecological advice for the project was undertaken by Milner Ecology Ltd, who kindly provided the information to the current project team. Details of the pipeline project are provided as Supplementary Information, and a summary follows in this report.

We selected this case study because it demonstrates several key features of survey and mitigation projects which could see improved outcomes and efficiencies using predictive modelling. Notably, the project had the potential to impact on many ponds possibly supporting great crested newts but without recent survey data, but the impacts were likely to have only minimal long-term impacts on newt conservation status. The project used conventional survey and mitigation procedures to a high standard, and we have good data on the actual outcomes, thereby allowing a reasonable comparison with newly developed approaches.

The construction. The project entailed the construction of a new 1220 mm high pressure gas pipeline from Easington to Paull, east of Hull. The total length of the pipeline was 24 km, and a working width of 43 m was required along the route. The habitats impacted by construction were reinstated shortly afterwards.

Understanding of great crested newt status in construction area. Little information on great crested newts was available for the works area prior to project initiation. The pipeline route passed within 500 m of 52 waterbodies, and given that great crested newts are known to occur in the region there was a need to assess presence. In summary, habitat assessments were undertaken at 49 waterbodies, and detailed great crested newt surveys undertaken at 14 ponds. Great crested newt presence was confirmed in six ponds, with predicted population sizes largely small.

Impact assessment and mitigation undertaken. Impacts were predicted to be low largely because of the type of terrestrial habitat along the route, the fact that no ponds would be directly affected, works duration was short, and there would be immediate reinstatement of habitat. Exclusion fencing was erected in areas where newts were considered likely to enter the working area. Pitfall traps were installed to capture newts in these areas over a 30 day period, along with searching by torch on five nights and destructive searching of key habitats such as hedgerows. Twenty-two great crested newts were captured in the working area, and 11 outside it.

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3.6.2. Possible alternative approaches to survey and mitigation

Background. Using the methods developed under the current project, we have assessed how a more strategic approach to survey and mitigation could be applied to the Easington to Paull pipeline case study. We have used a combination of species distribution modelling, risk assessment and FCS principles. It is important to note that the methods explained below are illustrative and that there are other approaches, yet we hope this demonstrates the potential of these methods.

Suitability layer. We built a model of habitat suitability (SDM) for great crested newts in the area around the pipeline, and specifically the area within a 5 km radius around the pipeline. However, due to the lack of both presence-only and presence-absence great crested newt records within the area, we built an SDM for of a much wider area (see Fig. 16). We used Maxent, 47 presence records11 and the same environmental layers as described in Appendix 1. Maxent was used since the great crested newt records available were presence-only, plus the eDNA-based GLMs for Lincolnshire did not have sufficient predictive power for the whole county or when extrapolated to other areas (Appendix 1).

Figure 36. Output from the SDM, including the pipeline area. Darker green indicates higher habitat suitability for great crested newts.

This SDM layer provides an indication of whether the ponds (within the 5 km, 500 m and 250 m buffers) around the pipeline are in suitable areas (Fig. 17).

11 Due to the total lack of great crested newt records from the development area, we also used four presence records provided to us by the ecologist responsible for the surveys for this specific pipeline development.

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Figure 17. The 5 km buffer around the pipeline. The suitability layer derived from the SDM is plotted as well as the ponds - coloured according to suitability. Note that sections of the 5 km distance overlap with the coast, hence the uneven buffer as mapped here.

Using the SDM layer and the pond suitability values (extracted from the SDM), crucial range and habitat information for assessing the potential net impact of the pipeline development was derived at the 5 km scale around the development:

(a) Range: Extent of Occurrence or Total Suitable Habitat (Maxent Minimum Training Presence) in area units or as percentage.

(b) Habitat:- Area of Occupancy as the total sum of cell suitability values as % of total cell count;- Occupied ponds as the total sum of pond suitability values as % of ponds (

Occupied ponds=∑ SN

∗100, where S is the suitability value of the pond and N is the

number of ponds).

To assess the impact of the pipeline on these metrics, we subtracted from the pre-development metrics the area that would be temporarily destroyed by the construction (a 43 m wide corridor). For example, we subtracted from the pre-construction Area of Occupancy metric, the total sum of cell suitability values that would be within the 43 m corridor. Similar approaches were taken for the other metrics.

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Risk of harm. To assess the risk of killing or injuring individual great crested newts we derive a novel metric called Risk of Harm (HR). The Risk of Harm layer is a relative measure of the risk of great crested newt killings/injuries per cell due to the works to be undertaken during development, without considering mitigation. While the construction works would only influence a 43 m wide area, the assumption behind the risk of harm layer is that since great crested newts can disperse widely from breeding ponds, there is a risk of harm from the construction even if a breeding pond is not within this 43 m area. Furthermore, our hypothesis is that HR is (1) positively related to the suitability of a cell; and (2) has a negative exponential relation to distance from the pipeline. This is based on evidence for dispersal patterns in great crested newts (e.g. Kupfer 1998; Kupfer and Kneitz 2000; Griffiths 2004; Kovar et al. 2009).

Risk of Harm (HR) is calculated as:

HR=S×e−kD (1 )

where S is the suitability value derived from the SDM, and D is the distance to the pipeline. According to the Maxent SDM, S is also a negative function of distance to pond. Thus HR increases as distance to pond decreases12. We set the constant k = 10 after experimenting with various values based on the average (≈ 500 m) dispersal distance of the great crested newt (e.g. see Karlsson et al. 2007; Kovar et al. 2009), i.e. that the effect of pipeline should decrease exponentially until 500 m. After 500 m the risk should be ≈ 0, irrespective of its suitability value. Large k’s increase the curve of the negative exponential function; thereby the higher k is, the more local the effect of the pipeline construction is (Fig. 18 illustrates how k influences the exponential function).

Figure 18. Illustration of the two values of k on an exponential function the function Y = e-kX. On the left k = 7, making for a larger and steeper decline of Y, while on the right, k = 2, resulting in an almost linear relationship between Y and X. In this illustrative example, X represents distance and Y risk of harm.

12 As distance to pond increases, suitability [S in function (1)] decreases; thus DK also decreases.

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To make HR more intuitive, we turned it into a percentage, the Risk of harm layer, by multiplying by 100 and dividing by maximum HR (Fig. 19 and equation (2)). Therefore, Risk of harm layer cells represent a % risk of great crested newt loss due to the construction, the reference value being maximum risk, namely at zero distance from the pipeline in the most suitable area.

Risk of harm=DK × 100max (DK )

(2)

Based on our results no pond has > 26.16% risk of great crested newt killing or injury compared to maximum possible risk at zero distance from the pipeline at the most suitable area, while only two ponds within the most suitable area around the pipeline have a relatively high risk of harm (Fig. 20). Note that this analysis identifies the ponds from which dispersing newts are most likely to encounter harm; in this particular case study no ponds would be directly impacted by the development.

Figure 19. Risk of harm layer, representing the pipeline installation works-related threat to great crested newts as a function of distance from the pipeline and habitat suitability.

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Figure 20. Zoom-in on the Risk of harm layer at the west side of the pipeline, the most suitable area for great crested newts and the only two ponds (here shown in red) that run a relatively high risk of harm to newts.

The HR metric could be further improved if HSI data for the ponds are available. HR could then be modified to take account of habitat suitability to provide a pond-level metric:

Risk of harm( pond level )=Risk of harm× HSI (3)

Using equation (3) we derived pond-level risk of harm as shown in Fig. 21, showing only one case of slightly increased risk of harm in the most high-risk area (out of three in total). To derive a single metric for any development at the landscape-wide scale, an approach similar to the one used for calculating area of occupancy could be used, that is summing the cell values of the risk layer and

comparing with the total number of cell values: Risk of harm(landscape level)=∑ HRC

∗100,

where HR is the Risk of harm value for every cell, and C is the total number of cells.

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Figure 21. Pond-level risk of harm in the most high-risk area. Only one pond appears to have a slightly higher risk of harm compared to the landscape-wide risk of harm layer (circled, compare with Figure 20)

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Number of populations. We present here an approach towards assessing Current Conservation Status - Populations (CCS-P) that does not rely on population numbers (which are typically difficult to acquire without dedicated surveys), but on metapopulation numbers and landscape connectivity. The approach involves three steps:

Creating a resistance-to-movement layer; Identifying least cost paths between the ponds; Identifying metapopulations within the 5 km buffer around the pipeline.

The first two steps have been described in the first case in section 3.2. Identifying the metapopulatins within the buffer zone is completed as follows:

Using the least cost path distance as calculated with the Linkage Mapper software for ArcGIS, we used a metric contained in Conefor software, called Number of Components (NoC). According to the software developers:

“A component (or connected region) is a set of nodes in which a path exists between every pair of nodes. Thus, there is no functional relation between nodes belonging to different components. An isolated node (patch) makes up a component itself. As a landscape gets more connected, it will present fewer components”

Based on species dispersal distance, the idea behind the metric is that since species have a maximum dispersal distance, ponds that are within a certain distance would form a distinct metapopulation, or “component” as the authors call it.

Using a threshold distance defined by the user and dependent on the species being analysed, Conefor’s NoC analysis can identify how many isolated clusters of ponds (metapopulations) are in the landscape. However, that includes clusters of any number of nodes: ranging from one pond to many ponds if all the ponds are within the dispersal distance of the species. In our case, we used four ponds as the minimum number of ponds for a cluster of nodes to be identified as a great crested newt metapopulation, based on the population viability analysis carried out by Griffiths (2004). Only suitable ponds (as derived from the SDM) were retained for the NoC analysis and we used a threshold of 900 m, which is less than the maximum recorded dispersal distance recorded for great crested newts (1290 m; Kupfer 1998). See Fig. 22 for the results.

More components, i.e. clusters of ponds or metapopulations means a more fragmented landscape, thus if a standardized metric expressed as a % was required it could be calculated as:

Populations index=1−NoC (¿3 ponds )Total NoC

∗100

The above metric does not take into account the possibility of two units of analysis having the same number of metapopulations, but some metapopulations may have a reduced number of ponds as an impact of the construction (or permanent development) in other cases (see Fig. 22, red metapopulation).

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Figure 22. Different coloured groups of points represent clusters of ponds identified through Number of Components metrics. In (A), the pre-development landscape, we can identify seven clusters of ponds. In (B), the landscape during development, we can identify eight clusters of ponds since the pipeline splits the biggest cluster of ponds [purple clusters in (A)]. Furthermore, the pipeline significantly reduces the number of ponds in the red cluster. Grey points are ponds that are not part of the network because they are too far away from other ponds (>900 m). For clarity, unsuitable ponds (per the SDM) are not shown on the maps.

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(A)

(B)

Compiling the values obtained, along with the Conservation Status measures described in Appendix 2, provides a concise summary of potential development impact, using an evidence-based, documented approach (Table 4). For this analysis, due to limited time availability we have taken a simplistic approach to setting FCS, opting for the status around the time the Habitats Directive came into force. This is done purely for illustrative purposes and does not necessarily indicate that such levels should be considered favourable. As discussed in the FCS principles in Appendix 2, selecting a particular date as a basis for FCS is generally considered unwise, but in this case it was done for expediency.

Table 4. Conservation Status measures and the metrics used to assess them in three “conditions”: before development, during development, and post development (all without mitigation). All metrics refer to a 5 km buffer around the pipeline, except Killing/injuring.

Conservation Status measure Metric

Before Developmen

t

During Developmen

t

Post developmen

tγ

Favourable Conservation Status (FRV)

(1990)≠

Range (CCS – R)β

Suitable habitat (Maxent Minimum Training Presence)

28% 26.4% 28% 30.24%

Habitat (CCS – H)

Area of Occupancy (total sum of cell

values as % of total cell count)

15.51 %(46808 of 301579)

15.47 %(46676 of 301579)

15.51 % 16.75%

Populations (CCS-P)

Populations index 83% 80.4% 83% 89.6%

Occupied ponds (total sum of pond suitability values as

% of ponds)¥

49%(73.50 of

147)

49%(73.50 of

147) 49% 52.92%

Future prospects (CCS-

F)

Landscape connectivity index:

Probability of Connectivity (PC, in

parenthesis weighted PC)

59.48 (7.71) 59.92 (7.74) 59.48 (7.71) 64.23%

100 - Sum of Risk of harm layer values for a 500 m buffer

around the pipeline*

- 91% - -

≠ For FCS calculations we used the rate of pond loss as calculated in Appendix 2 (2% for the decades following 1975) for all metrics. Calculating the other metrics using the same methodology as the one applied above would require detailed maps of the past.

µ Not enough presence records in the development area or the 5 km buffer around the pipeline. β Conceptually closer to a bio-environmental, potential nicheγ Assuming no post-development changes in the landscape.¥ The pipeline works (43 m wide) does not overlap with any pond.*100 would be the maximum, if the 500 m buffer around the pipeline was highly suitable and all its area as affected by the

development as if it were 0 m from the pipeline.

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In conclusion, the analysis predicts that there will be minimal impact of the pipeline on the status of great crested newts during or after the development. Using the framework developed in Appendix 2, the modelling therefore predicted that the current conservation status (CCS) will be maintained (Table 4). Transcribing the information in Table 4 with the impact matrix described in Appendix 2, provides an assessment of the impact of the development under two scenarios: an equilibrium approach and an assessment against local FRVs:

Equilibrium approach:

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Current Conservation Status

(status in absence of development)

Metrics calculated as %

CCS – RC → 28

CCS – HC → 15.51

CCS – P1C → 83 (Populations index)

CCS – P2C → 49 (Occupied ponds)

CCS – F1C → 59.48 (7.71) (PC connectivity index)

Metrics requiring expert interpretation

CCS – F2C → 0 (Risk of harm from the development)

Impact of development

(post-development status)

Metrics calculated as % (change in parentheses)

CCS – RI → 28 (0)

CCS – HI → 15.51 (0)

CCS – P1I → 83 (0) (Populations index)

CCS – P2I → 49 (0) (Occupied ponds)

CCS – F1I → 59.48 (0) (PC connectivity index)

Metrics requiring expert interpretation

CCS – F2I → 9 (9) (Risk of harm from the development)

Change in Current Conservation Status (CCCS)

(CCCS = CCS – Impact of development

e.g. (CCS – RC) – (CCS – RI) = 0 )

Metrics calculated as %

CCCS – R → 0

CCCS – H → 0

CCCS – P1 → 0 (Populations index)

CCCS – P2 → 0 (Occupied ponds)

CCCS – F1 → 0 (7.71) (PC connectivity index)

Metrics requiring expert interpretation

CCCS – F2 → 9 (Risk of harm from the development)

Local FRVs approach (comparing current status and impact status with local FCS).

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Net difference between CCS and FCS in absence of development

(= CCS – FCS from Table 4)

Metrics calculated as %

CCS – R → - 2.24

CCS – H → - 1.24

CCS – P1 → - 6.6 (Populations index)

CCS – P2 → - 3.92 (Occupied ponds)

CCS – F1 → - 4.75 (7.71) (PC connectivity index)

Metrics requiring expert interpretation

CCS – F2C → 0 (Risk of harm from the development)

Net difference between CCS and FCS post-development

Metrics calculated as %

CCS – R → - 2.24

CCS – H → - 1.24

CCS – P1 → - 6.6 (Populations index)

CCS – P2 → - 3.92 (Occupied ponds)

CCS – F1 → - 4.75 (7.71) (PC connectivity index)

Metrics requiring expert interpretation

CCS – F2C → 9 (Risk of harm from the development)

Status with development

(Post-development status)

Metrics calculated as % (change in parentheses)

CCS – RI → 28 (0)

CCS – HI → 15.51 (0)

CCS – P1I → 83 (0) (Populations index)

CCS – P2I → 49 (0) (Occupied ponds)

CCS – F1I → 59.48 (0) (PC connectivity index)

Metrics requiring expert interpretation

CCS – F2I → 9 (9) (Risk of harm from the development)

3.6.3. Comparison of with actual mitigation

This analysis clearly demonstrates that the effect of the pipeline was minimal in terms of impacts on great crested newt conservation status. Viewed in this perspective, it is possible to propose a different mitigation scenario from the one actually undertaken in 2010. Critically, for this to occur, there would need to be a revised interpretation of the species protection provisions and derogation regime. These are not considered in detail here but would entail an acceptance (either through interpretation of the provisions or via a licence) of harm to a small number of individual great crested newts, along with the loss of a minimal resting place habitat.

Accepting this approach would entail minimal capture, exclusion and translocation effort, and potentially none at all. To take the extreme approach, it is possible to argue that the minimal impact on individual newts and their breeding sites and resting places would in fact merit no capture effort in this case, so long as reasonable working procedures were adopted (careful attention to timing and methods). Habitat enhancement measures would be undertaken to compensate for the minimal impacts, thus improving the local status of great crested newts, moving them towards a Favourable Conservation Status. Pond restoration and creation would be targeted using the Species Distribution Model and GOHSA outputs, to provide substantial increase in metapopulation viability. These actions would almost certainly outweigh any impact on population status from the development. Thus there would be clear benefits to the great crested newt population in this scenario compared to the traditional approach used in 2010.

The benefits to the developer and the regulator in this scenario are clear and comprise three main components. Firstly, there would be increased certainty, because the mitigation activities would be clear as soon as the modelling assessment were completed, and should not be subject to last minute changes due to discoveries of newts (as sometimes happens with the existing regime). Secondly, there would be reduced time to complete assessment and mitigation planning. Thirdly, the total costs of addressing the impact on great crested newts would be lower, since the costs associated with survey, capture, exclusion and translocation would be lower than using a traditional approach.

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3.7. Software used for the analyses in this report (including the case studies)

Running all the analyses included in this report and case studies requires a significant level of expertise in three areas: Geographic Information Systems (GIS), statistics, and ecological knowledge about the great crested newt. Table 5 documents all the software (including extensions, add-ons and libraries) that are essential and were used in this exercise.

Table 5. Software required for completing the different parts of this exercise. ArcGIS 10.2.1 Basic licence, with the Spatial Analyst licence was used. Excel was used as spreadsheet software.

Component exercise Core SoftwareArcGIS and QGIS

extensionsR extensions - libraries

GCN records preparation

ArcGIS, QGIS for

deleting duplicates

(deleting duplicates is

unavailable in ArcGIS

under the Basic licence)

- -

Environmental layer

manipulation and new layer

extraction

ArcGIS, QGIS

SDM toolbox (for

ArcGIS), Lecos (for

QGIS)

-

Modelling: GLMsArcGIS (mainly for

viewing results), R-

boot, ecospat, raster,

dismo, sp, auc, car,

gmulti

Modelling: Maxent,

ensemble models, SVM

ArcGIS (mainly for

viewing results), R-

dismo, raster, spatial,

sp, biomod2, AUC,

e1071, rgdal

Connectivity Analysis ArcGIS, Conefor

Linkage Mapper

(ArcGIS), Conefor inputs

(ArcGIS, for preparing

files for use with

Conefor)

-

FRVs ArcGIS - -

Uncertainty mappingArcGIS (mainly for

viewing results), R- HH

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