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Developing models to estimate the occurrence in the

English countryside of Great Crested Newts, a protected

species under the Habitats Directive

[Defra Project Code WC1108]

Project Summary Report*

Dimitrios Bormpoudakis1, Jim Foster2, Tony Gent2, Richard A. Griffiths1, Liam Russell2,

Thomas Starnes2, Joseph Tzanopoulos1, John Wilkinson2

1Durrell Institute of Conservation and Ecology, School of Anthropology and

Conservation, University of Kent, Canterbury, CT2 7NR, UK

2Amphibian and Reptile Conservation, 655a Christchurch Road, Bournemouth,

Dorset BH1 4AP, UK

*Authors listed alphabetically

General Summary

The great crested newt is a European Protected Species (EPS) with a widespread distribution within Great Britain. This results in the species frequently coming into conflict with development. Consequently, decision-makers in local government and licensing authorities face complex issues when it comes to reconciling development and conservation. New approaches are therefore needed to ensure that conservation decisions are based upon the best available science. The project set out to evaluate new potential approaches to these issues using three work packages: (1) Develop, test and compare species distribution models (SDMs) for great crested newts; (2) Building on these models, develop a methodology for assessing the impact of a plan or project on the local conservation status of great crested newts; and (3) End-user testing to assess model applications and fitness for purpose. Defra commissioned the project with additional funding from Natural Resources Wales, and together with Natural England and JNCC, also provided guidance.

GLM models developed using eDNA presence-absence data for a small area of Kent provided a good prediction of the county-wide distribution of the species. GLM models developed for Cheshire and Lincolnshire using eDNA data yielded weaker models. Equally, the Kent model did not reliably fit Cheshire and Lancashire, suggesting that the predictor variables vary geographically.

Maxent and ensemble models yielded good fits to county-wide distributions but poor fits to the localised eDNA data in all three counties. These models may have utility at a broad scale, but cannot account for absences at a local scale. Equally, some important variables at a local scale cannot be obtained through GIS layers and need to be obtained through field surveys. Constructing models for different scales therefore requires different modelling tools and different types of predictor variables.

Maxent models of the national distribution of great crested newts in England gave predictions that were broadly consistent with current knowledge and can be used to calculate potential areas of occupancy.

A framework for assigning and measuring Favourable Reference Values (FRVs) for great crested newts at different scales was developed using both an ‘equilibrium’ (=’no net change’) approach and FRVs set using baseline data according to other criteria. These principles were combined with SDMs and connectivity analysis of five case studies. The case studies combine both real and hypothetical data, and illustrate how a modelling approach can be used to identify important areas of newt habitat, identify connectivity between ponds, predict potential impacts of development, and design and evaluate mitigation measures.

Three end-user consultation exercises showed that there was considerable interest and enthusiasm for the development and application of SDMs across a range of applications and stakeholders. Concerns were expressed over the quality and quantity of data available for modelling using current data-flow systems; the predictive power of models; and the potential for model outputs to be misused. Challenges that need to be addressed include training, expertise and building capacity, enhancing the regulatory framework for protected species, and the improvement and centralisation of data management systems.

Species Distribution Models (SDMs) provide an objective and evidence-based tool for use within decision-making processes involving great crested newts. They have the potential to identify priority areas for conservation, target survey effort, assess the impacts of development, and assign Favourable Reference Values for the species. However, great crested newt records and habitat data are currently dispersed across multiple recording systems and vary in quality and quantity. A well-integrated data management system is required if SDMs are to make the best use of available information.

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ContentsList of Figures.........................................................................................................................................6

List of tables..........................................................................................................................................6

List of appendices..................................................................................................................................6

Acknowledgements...............................................................................................................................7

Highlights...............................................................................................................................................8

1. General Introduction......................................................................................................................10

2. Develop, test and compare species distribution models for great crested newts..........................12

2.1. Background...............................................................................................................................12

2.2. Methods...................................................................................................................................12

2.2.1. Model development.........................................................................................................12

2.2.2. Modelling at the pond level...............................................................................................13

2.2.3. Number of records required for modelling........................................................................13

2.2.4. Large-scale SDMs: England-wide Maxent models at two resolutions................................13

2.3. Results......................................................................................................................................13

2.3.1. Kent...................................................................................................................................13

2.3.2. Cheshire and Lincolnshire..................................................................................................14

2.3.3. Influence of record availability and sample size................................................................16

2.3.4. Large-scale SDMs: England-wide Maxent models at two resolutions................................16

3. Development of a methodology for assessing the impact of a plan or project on Great Crested Newts..................................................................................................................................................18

3.1. Background...............................................................................................................................18

3.2. Background to FCS and FRVs....................................................................................................18

3.3. Setting Favourable levels..........................................................................................................19

3.4. Setting units for Favourable Reference Values.........................................................................19

3.5. Setting values for Favourable Reference Values.......................................................................21

3.6. Assessing impacts on conservation status................................................................................22

4. Applications and case studies.........................................................................................................23

4.1. Background...............................................................................................................................23

4.2. Applying connectivity analysis to a pond network....................................................................23

4.3. Determining the impacts and mitigation measures of a development using Favourable Reference Values, species distribution models and connectivity analysis.......................................23

4.4. Targeting eDNA surveys based on uncertainty mapping..........................................................24

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4.5. Development of a baseline model for evaluating FCS and FRVs within National Character Areas................................................................................................................................................25

4.6. Hull gas pipeline mitigation......................................................................................................25

4.7. Software requirements and model choice................................................................................26

5. End-user testing – to test model applications and fitness for purpose...........................................27

5.1. Background...............................................................................................................................27

5.2. Questionnaire survey................................................................................................................27

5.2.1. Results...............................................................................................................................27

5.3. Herpetofauna Workers’ Meeting Workshop............................................................................28

5.3.1. Results...............................................................................................................................28

5.4. Professional end-user workshop..............................................................................................29

5.4.1. Results...............................................................................................................................30

6. Conclusions..................................................................................................................................31

6.1. Opportunities and challenges in adopting predictive modelling...............................................31

6.2. Lessons from spatial risk assessment in other fields of nature conservation...........................33

6.3. Implementing a modelling framework for great crested newt conservation...........................35

6.4. Suggestions for future work......................................................................................................38

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List of Figures

Figure 1. Maxent models for the three areas, including the presence-only records they were built upon. Suitability is measured on an integer scale of 0-1000, instead of a continuous scale from 0 – 1 for reducing calculation time...............................................................................................................14

Figure 2. National scale Maxent model, 400 x 400 m cell size. Suitability has been converted to a scale of 0-1.0 (1.0 represents maximum suitability). Black dots show ponds on which the model was built.....................................................................................................................................................16

Figure 3. Standard error map for the Kent Generalized Linear Model SDM. High standard error indicates higher uncertainty regarding the occupancy probability for each cell. Standard error values allow for the computation of lower and upper confidence intervals for the predictions and can also be used for targeting surveys..............................................................................................................23

Figure 4. Zoom-in on the Risk of harm layer at the west side of the pipeline, the most suitable area for great crested newts. Only two ponds (shown in red) run a relatively high risk of harm to newts. Maximum risk of harm (100 %, dark red areas in the map) represents the risk that a pond would face if it was in the most highly suitable area and at zero distance from the pipeline. For more information see Appendix 3................................................................................................................25

Figure 5. Figure demonstrating how the IRZ tool has been used to assess potential impacts from hypothetical development near Hothfield Common SSSI (Ashford, Kent)...........................................33

List of tables

Table 1. Metrics for measuring reference values for FCS criteria at different spatial scales...............21

Table 2. A summary of strengths and weaknesses of the models for a variety of applications. In all cases, reliable presence-absence (PA) data are preferable to presence-only (PO) data. Thus, in the column Type below, we refer to what kind of data would be sufficient, instead of preferable..........39

List of appendices

Appendix 1: Develop, test and compare species distribution models for great crested newts.

Appendix 2: Development of a methodology for assessing the impact of a plan or project on Great Crested Newts

Appendix 3: Applications and case studies.

Appendix 4: End-user consultations-workshops

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Acknowledgements

This project was funded by Defra and Natural Resources Wales. We thank the project steering group, and Dr Jeremy Biggs and the Freshwater Habitats Trust for their feedback, advice and guidance at the different stages of the project. We thank the many stakeholders who contributed through the consultation exercises. We thank Natural England for arranging access to great crested newt records from the NBN Gateway and other sources. Further data were kindly provided by Kent Amphibian and Reptile Group, RoyalHaskoning DHV and Milner Ecology. We are grateful to Natural England for providing further information and to peer reviewers for their constructive comments and feedback on the final report.

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Highlights

Maxent can provide reasonable predictions* of the distribution of great crested newts using presence-only data at a regional (i.e. county, National Character Area) scale at a variety of resolutions (25, 100 m grids) using publically accessible data.

Maxent cannot provide reliable predictions of great crested newts at a local (i.e. site-specific) scale as there may be local factors determining the presence or absence of newts that cannot be included within a model (e.g. presence of fish in a pond) due to lack of available GIS layers.

Ensemble models can provide slightly better predictions of distribution than Maxent, but require more technical expertise to run them.

Generalized Linear Models (GLMs) can provide reasonable predictions of the distribution/site occupancy of newts at a local or site specific scale but require data on absences as well as presences. Building and running GLMs requires more statistical expertise than Maxent models.

Including connectivity and site-specific variables into GLMs improves the predictive power of the models.

Predictive models constructed for one region (e.g. at county level) may not be transferrable to other regions because of different predictors in different areas.

All current field methods for assessing the status of great crested newt populations (including eDNA) have imperfect detection. Incorporating repeated field surveys with occupancy modelling in SDMs can provide estimates of detectability.

Favourable Conservation Status concepts provide a framework for assessing great crested newt status and impacts of developments on status. Proposals are made for setting and evaluating status metrics.

Case studies of typical development scenarios demonstrate the value of predictive models in deriving Conservation Status values and assessing impacts. This approach represents a substantial shift from current impact assessment methods, potentially allowing a more strategic interpretation of the Habitats Directive.

A new index and GIS layer termed Risk of harm was derived, which may have utility in assessing the potential impact of a development.

Building Maxent models and connectivity analyses within GIS requires some training in spatial modelling.

Building GLMs and more advanced models requires training in the R-statistical analysis platform and a range of other software.

There is a range of options for the implementation of SDMs, ranging from local to national use. The governance of SDMs and model-based regulation needs further attention since the consequences of some decisions can be legally sensitive.

Current systems for the collection, management and dissemination of great crested newt records are not well-integrated and there is variability in the quality and quantity of records available on those systems. This could be a barrier to the successful implementation of SDMs for great crested newts.

There is broad support among stakeholders for further use of SDMs in regulation and conservation. However, a range of concerns - including the potential for misuse of both data and model outputs - should be addressed before any full-scale implementation.

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Statistical models provide an additional tool for evidence-based decisions, but may not necessarily provide the high levels of confidence sometimes required by developers and utility providers for decision-making processes.

Statistical models are always simplistic reflections of what might be reality, rather than reflections of actual reality. They make assumptions that may or may not be valid and therefore require ongoing development, testing, validation and ground-truthing.

Improved training, capacity building and centralization of data processing systems would improve the implementation of SDMs for great crested newts.

For SDMs to be useful in routine decision-making, a clear framework is needed for their integration. Particular attention should be paid to amending planning and licensing processes, statutory guidance, and addressing ecological capacity and capability shortfalls in Local Planning Authorities.

*Prediction here refers to the ability of the model to predict independent test data

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1. General Introduction

The great crested newt Triturus cristatus is subject to a system of strict protection, due to listing on European and domestic legislation. The system of strict protection means that, in summary, it is an offence to capture, kill, injure or disturb great crested newts, or to damage key elements of their habitats. Despite legal protection, regulation and conservation initiatives, the species continues to decline across parts of its range.

The great crested newt’s status as a European Protected Species (EPS) has important implications for regulators in England and Wales. Moreover, the legal position has become increasingly challenging in recent years for those undertaking work that might affect great crested newts and their habitats. The great crested newt’s widespread distribution and its occurrence in habitats often proposed for development make these issues more pressing. Decision makers in local government and licensing authorities face especially complex issues. This situation arises from successive amendments to the domestic legislation, EC compliance action and UK case law. The outcomes have been narrower definitions of offences, the removal of the statutory defence, increased penalties on conviction, and increased complexity of the legislation resulting in confusion over interpretation. In turn, this has generated risk aversion among developers, landowners and even, to some extent, regulators and conservation organisations. In recent years, some elements of the construction industry have made plain their disapproval of the implications of regulation for great crested newts, and this has resulted in government interest in resolving the problems. Those working in the conservation sector have also expressed concerns at the outcomes of the current regulatory regime, and noted increasingly negative messages about newts in the media. To add to the list of issues for government to address, there has been a complaint to the EU about the UK Government’s compliance with its obligations for great crested newts under the Habitats Directive.

These regulatory issues are compounded by two topics that this report addresses. Firstly, there is currently a lack of sound evidence on great crested newt status upon which to make regulatory and conservation decisions. Secondly, the framework in which those decisions are made is unsatisfactory, especially since it is based largely on the protection of individual newts rather than the conservation of newt populations.

Predictive modelling has significant potential to improve our evidence base on great crested newt status. Given the widespread nature of this species in lowland England, the costs associated with effective surveys and data flow issues, it is unrealistic to derive anything approaching a comprehensive dataset for the whole country. Recent advances in modelling methods can address this by using data from sample surveys, combined with habitat and other data, to produce maps and values reflecting predicted status. Models can predict, for instance, whether great crested newts are likely to occur in a selected area, and can evaluate the connectivity of habitat for newts. Whilst modelled outputs are not a complete replacement for survey data, they offer many advantages in some applications.

The framework in which regulatory and conservation decisions are made is imperfect in several key respects. The assessment of conservation status is insufficiently embedded into decision making in a way that promotes effective conservation. This arises partly from a lack of understanding about how to apply the Favourable Conservation Status (FCS) concept. The FCS concept is defined in the Habitats Directive and is, or should be, central to decision-making on activities that may affect great

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crested newt populations. However, its use has tended to be patchy and the underlying principles have not been sufficiently applied. In particular, there is major scope for the increased use in regulatory decisions of Favourable Reference Values (FRVs), which describe target levels for components of FCS. Working on an FCS basis, using FRVs to guide decisions will allow regulation to move away from an individual protection approach. Embedding the FCS concept will also bring dividends for great crested newt conservation more broadly, by setting out a clear vision that will help with project planning and impact assessment.

The project work was organised around three objectives:

(1) To develop, test and compare species distribution models for great crested newts;(2) Building on these models, develop a methodology for assessing the impact of a plan or project

on the local conservation status of great crested newts;(3) End-user evaluation, to test the model applications and fitness for purpose.

The project outputs are organised into four sections focusing on model development; development of favourable reference values (FRVs) for assigning favourable conservation status; applications and case studies; and end-user evaluation. These are described in detail in four technical appendices (Appendices 1-4).

This document provides a non-technical summary of the appendices, and the supporting appendices should be consulted for the technical details, explanations of some terminology, and associated supporting literature. Drawing together the results of the modelling exercises, the conceptual model for FRVs and end-user feedback, the components of a modelling implementation programme are considered together with suggestions for future work.

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2. Develop, test and compare species distribution models for great crested newts

This section summarises the main findings of the technical report on the modelling (Appendix 1).

2.1. Background

Species Distribution Modelling (SDM) has the potential to improve the evidence base for great crested newt status and decision-making. Given the widespread distribution of the species, the costs associated with surveys, and data flow issues, it is unrealistic to derive a comprehensive dataset on conservation status for the whole country. Recent advances in modelling methods can address this by using data from sample surveys, combined with habitat and other data, to produce maps and values reflecting predicted status. Models have the potential to predict, for instance, whether great crested newts are likely to occur in a selected area, and can evaluate the connectivity of habitat for newts. Whilst modelled outputs are not a complete replacement for survey data, they offer many significant advantages in some applications.

This part of the project addressed these issues using the following approaches:

Development and evaluation of new models based on presence-absence data collected for great crested newts in Kent, Lincolnshire and Cheshire;

Compare the performance of the resulting models with presence-only data using MaxEnt and ensemble modelling;

Assess the geographical scale over which models can successfully be developed; Provide guidance for end-users on the best modelling approaches for different purposes.

2.2. Methods

2.2.1. Model development

Presence-absence eDNA data for 195 ponds in Kent, 169 ponds in Lincolnshire and 298 ponds in Cheshire were provided by Defra together with associated Habitat Suitability Index (HSI) data. The ponds sampled for eDNA were from relatively small areas within each county, so may not have been representative of the wider landscape.

Additional presence-only records for each county were obtained from a variety of other sources (e.g. National Biodiversity Network Gateway, local record centres and recording groups) as follows: 18,947 for Kent, 162 for Lincolnshire and 1200 for Cheshire. Unlike the eDNA data, these records were more widely representative of each county, but not necessarily provided at fine-scale (i.e. < 25 m and < 100 m grids) level, since the majority are at 1 km grid resolution and above.

Generalized Linear Models (GLMs) were used to build models of the eDNA distribution data while the additional presence-only data sets were modelled using Maxent, ensemble models (i.e. a combination of several modelling approaches that use presence-only data), and Support Vector Machines (SVM), a novel modelling method that uses presence-only data.

Maxent and ensemble models for Kent were built using the same suite of environmental predictors as the GLM models plus a soil layer. These were built using the county-wide, presence-only data set at <25 m grid resolution. These models were validated on (1) 30% of the presence-only records that

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were excluded from the model construction; (2) the eDNA data. Because of the resolution of the data, Maxent and ensemble models were built at 100 m grid scale, and used the same environmental predictors as the GLM, excluding only those variables that need a smaller cell size than this scale.

The initial modelling was carried out at the 1 km grid resolution using bioclimatic, habitat and elevation data that can be freely extracted as GIS layers from open sources. At this scale, the modelling aims to predict the potential distribution of great crested newts across a landscape, rather than prediction of likely presence/absence in a particular pond (which would require pond-specific variables that were not available as a GIS layer; see Appendix 1 for variables used).

Once the best-fitting GLM model was determined using the eDNA data set, the model was validated by testing how well it predicted the wider distribution of newts as depicted using the wider presence-only data set.

2.2.2. Modelling at the pond level

Further models were constructed after adding in pond-specific variables that were collected along with the eDNA samples (NB these are variables that would need to be obtained using on-the-ground surveys as they cannot be obtained as GIS layers). These models were constructed using three different types of data: (1) pond-level variables only; (2) pond, climatic and landscape variables; and (3) pond and Habitat Suitability Index (HSI) variables. The modelling approach was otherwise the same as described above using the GLM.

2.2.3. Number of records required for modelling

Reliable models can only be constructed if there are sufficient records available. The performance of the models was tested using different sample sizes of sites in repeated simulations, ranging from 30-130 randomly selected records from the eDNA data sets. 60 records were retained to test the models, and examine how the model fit improved in relation to sample size.

2.2.4. Large-scale SDMs: England-wide Maxent models at two resolutions

Using records from the NBN and from a consultancy, we constructed national predictive models using Maxent at two resolutions 400 x 400 m and 900 x 900 m. To reduce geographical bias, we included one record per 1 km2 cell and used a range of bioclimatic and landscape layers as predictors. We compared the Area of Occupancy (AoO) estimates produced by the two models.

2.3. Results

2.3.1. Kent

The GLM model constructed using the eDNA data in combination with bioclimatic, habitat and elevation data layers was a very good fit. Great crested newts are more likely to be found in areas with high pond density; where minimum temperature in winter is low and maximum temperature in the summer is high; in areas relatively close to natural or semi-natural grassland; and in areas relatively far from arable land. Although the GLM eDNA models were built on records from a small area of Kent, the models managed to predict occupancy across Kent reliably. For example, the models reliably predicted Dungeness as an area of high occupancy, even though this is a landscape

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quite different from that where the eDNA samples were collected. Overall, the model predicted that 51% of Kent has a high probability of being occupied by great crested newts. For Kent, it was therefore possible to produce reliable models that predicted distribution using publically accessible data to construct the layers within a GIS. Including additional pond-level variables to the GLM resulted in significantly improved fits to the data.

Data were excluded from model construction to test or validate model outputs. The Maxent models gave good predictions of these data, but gave poor predictions of the eDNA sites (Fig. 1). A collective analysis using ensemble modelling gave slightly better predictions than Maxent, but the predictions of the eDNA sites remained poor. SVM models did not result in any improved fits over Maxent or ensemble models.

2.3.2. Cheshire and Lincolnshire

In contrast to Kent, GLM models built using the eDNA data sets for Cheshire and Lincolnshire gave poor predictions of wider distribution within these counties. Equally, the GLM model developed for Kent did not reliably predict the distributions for Cheshire or Lincolnshire. This suggests that the environmental predictors of distribution in Kent are not good predictors of distribution in other counties. As with Kent, adding in the pond-level variables to the GLM resulted in significantly improved fits to the data. Underperformance of the models constructed using eDNA is probably related to the limited geographical scale over which the samples were collected. Sampling protocols that are designed to collect data for modelling therefore need to cover the domains over which the predictions are intended.

In both counties, Maxent modelling of the county-wide, presence-only records gave good fits when validated against the 30% excluded records, but poor fits to the eDNA data, as Maxent is unable to take account of absences (Fig. 1). SVM models did not result in any improved fits over Maxent or ensemble models.

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Figure 1. Maxent models for the three areas, including the presence-only records they were built upon. Suitability is measured on an integer scale of 0-1000, instead of a continuous scale from 0 – 1 for reducing calculation time.

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Kent

Cheshire

Lincolnshire

2.3.3. Influence of record availability and sample size

A minimum of about 100 records were needed to optimise the predictions of the GLM models for Kent. For Cheshire, a minimum of about 150 records were needed to obtain a reasonable predictions from the model. For Lincolnshire, there appeared to be no improvement in model fit as the sample size of records increased above 50

2.3.4. Large-scale SDMs: England-wide Maxent models at two resolutions

SDMs produced at 400 x 400 m and 900 x 900 m cell size (resolution of the model) produced similar distribution maps, but slightly different estimates of Area of Occupancy (AoO). Larger cell sizes tend to overestimate the AoO. The distributions illustrated in the maps were broadly consistent with published information concerning great crested newt distribution (Fig. 2). Differences could be due to biases within either the modelling protocol or published information.

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Figure 2. National scale Maxent model, 400 x 400 m cell size. Suitability has been converted to a scale of 0-1.0 (1.0 represents maximum suitability). Black dots show ponds on which the model was built.

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3. Development of a methodology for assessing the impact of a plan or project on Great Crested Newts

This section summarises the development of a methodology for assessing the impact of a plan or project on great crested newts, detailed in Appendix 2.

3.1. Background

The Habitats Directive calls for scheduled species (including the great crested newt) to be maintained at or restored to a Favourable Conservation Status. This is essentially a status in which the species is prospering and is expected to do so in future. Favourable Conservation Status (FCS) is therefore a fundamental element of the Habitats Directive. Favourable Reference Values (FRVs) provide a framework for assessing conservation status. Whilst the Directive provides a high level definition of conservation status, there is no common agreement on exactly how this might work in practice. In particular, the application of FCS concepts to status assessment and licensing decisions needs further exploration.

There is great potential for predictive modelling to assist with a new way of assessing and regulating impacts on great crested newts. In turn, this could lead to more effective implementation of the Directive, with benefits to newts, regulators, conservation organisations and business.

It is important to note that the EC has recently (October 2015) commissioned a project on Favourable Reference Values. This project, combined with other ongoing work such as an FCS project recently set up by RSPB and work commissioned by Natural Resources Wales, should help to refine understanding of FRVs. In the current project, a large amount of current information has been used to derive a basis for FRVs and FCS for great crested newts. However, it is useful to state at the outset that there remain some unresolved areas, and whilst we report considerable progress, further work will be required to embrace these ongoing developments.

This element of the project therefore set out to:

Develop a methodology for assessing the impact of a plan or project on the local conservation status of great crested newts;

Develop a conservation status impact matrix.

3.2. Background to FCS and FRVs

Guidance provided by the EC indicates that FCS can be described as “a situation where a habitat type or species is prospering (in both quality and extent/ population) and with good prospects to do so in the future as well”. Favourable Reference Values (FRVs) essentially provide a framework for assessing conservation status against a ‘favourable’ benchmark. SDMs and connectivity analysis can be used as tools in the derivation of ‘favourable’ status, and when combined with FRVs, be used to objectively assign targets for development mitigation undertaken under licence.

According to the Habitats Directive 1992, conservation status is defined as “the sum of the influences acting on the species concerned that may affect the long-term distribution and abundance of its populations”. It is defined as favourable when:

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population dynamics data on the species concerned indicate that it is maintaining itself on a long-term basis as a viable component of its natural habitats, and the natural range of the species is neither being reduced nor is likely to be reduced for the foreseeable future, and there is, and will probably continue to be, a sufficiently large habitat to maintain its populations on a long-term basis.

Conservation status therefore has four components, each of which can be assigned a Favourable Reference Value: population, range, habitat and future prospects. Whilst conservation status is typically used at a national scale, it can also be considered at smaller spatial scales.

Favourable Reference Values, as a method of describing FCS, are central to this project since they could potentially help with more effective regulation of great crested newts. For example, when a developer proposes a construction project for an area supporting great crested newts, there would typically be a mitigation project carried out under licence. For the licence to be issued, Natural England (in Wales, Natural Resources Wales) needs to be satisfied that – among other matters – there would be no detriment to the maintenance of the population at a favourable conservation status. Potentially, FRVs could be used more explicitly in this regulatory process. Whilst there is a range of guidance and commentary on FCS, there is a lack of clarity on the precise methods of assessment of conservation status, and how the concept is used in practice.

3.3. Setting Favourable levels

To decide whether great crested newts are at FCS, there needs to be agreement on FRVs. In this element of the project we set out seven principles to underpin the determination of favourable levels. We based these on the legislation itself, EC guidance, various commentaries, input from the project steering group, stakeholder consultation and our own interpretation. The principles are:

Principle 1: FRVs should be developed for all appropriate spatial scales. Principle 2: FRVs should be driven primarily by an ‘ecological’ definition. Principle 3: FRVs relate to ‘favourable,’ not ‘minimal’, population levels. Principle 4: FRVs need to take account of variations in time and especially anthropogenic

impacts. Principle 5: FRVs need to be pragmatic and achievable. Principle 6: FRVs need to take account of the four pillars of FCS defined in the Habitats

Directive and take account of trends within each of these. Principle 7: ‘Top down’ and ’bottom up’ approaches should be undertaken iteratively when

determining metrics and FRVs.

3.4. Setting units for Favourable Reference Values

When setting Favourable Reference Values, the units (or metrics) to describe them need to be determined before assigning the values for each unit. This project has explored the associated issues, such as ease of measurement, and proposed units as described in the following table. Note that the units are described according to four spatial scales, from local up to national. Note also that there is some uncertainty over whether it is necessary to set FRV values for the future prospects and habitat parameters. In this report we have interpreted the EC guidance (Evans & Arvela, 2011) to

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indicate that in fact it would be useful to set FRVs for these parameters. This matter is discussed further in Appendix 2.

Table 1. Metrics for measuring reference values for FCS criteria at different spatial scales

FCS criterion Measurement Unit (metric)Local (e.g. site) Landscape/ district County National

Favourable Reference Range

Polygon area by Alpha-Hull analysis

n/a n/a km2 (or n/a?) km2

Measure of actual area of occupancy: including modelled distribution

uses units below 0.5km2, then the summed area (in Ha)

Ha (or 1km2) 1km2 or 10km2 10km square

Occupied counties/ vice counties

n/a (though record: Presence/ absence)



No. of occupied vice-Counties’

Favourable Reference Habitat

Measure the extent of ‘potential habitat types’ within the natural range of the species: This can be done using land classification data at 1km2 level (units = 1km squares), though we advocate for local levelling modelling using data at the sub-1 km level of resolution and measurement via GIS

Ha (or km2) Using land classification data at 1km2 level (units = 1km squares),

Estimated from modelling at the sub-1 km level of resolution (units = Ha (or km2).

Using land classification data at 1km2 level (units = 1km squares),

Modelling using data at the sub-1 km level of resolution (units = Ha (or km2).

km2

Extent of ‘suitable habitat’ areas: This can be done using land classification data at 1km2 level (units = 1km squares), though we advocate for local levelling modelling using data at the sub-1 km level of resolution and measurement via GIS

Ha (or km2) Using land classification data at 1km2 level (units = 1km squares),

Modelling using data at the sub-1 km level of resolution (units = Ha (or km2).

Using land classification data at 1km2 level (units = 1km squares),

modelling using data at the sub-1 Km level of resolution (units = Ha (or km2).

km2

Favourable Reference Population

Occupied ponds as a proxy for ‘number of populations’

Number of occupied ponds with an HSI>0.7




Viability measure as a proxy for ‘population size above MVP’. An approach trialled in this project uses Getis-Ord Hot-Spot Analysis (GOHSA), which quantifies clustering of high and low occupancy values based both on Euclidean distance and the occupancy probability from a model.

An index of viability that takes into account minimum thresholds for connectivity, terrestrial habitat extent and pond density. Options explored in case studies, e.g. statistic from GOHSA.




Calculation of an index of trend in abundance, based on counts undertaken to standardised method. Surveys must be from a structured sample of ponds, undertaken at fixed intervals of say 3 years (approx.

Index of trend in abundance, based on structured sample of ponds in local area.

Index of trend in abundance, based on structured sample of ponds in landscape/district.

Index of trend in abundance, based on structured sample of ponds in county.

Index of trend in abundance, based on structured sample of ponds in country.

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generation time for this species). Index to take into account proportion of increasing vs decreasing counts, as well as size of changes in abundance.

Favourable Reference Future Prospects

‘Risk assessment’ approach to determine net adverse/ beneficial impacts in the near & foreseeable future: [note: this can be done as separate evaluations at each geographic scale or could be developed to form an aggregatable system, evaluated at a national level from sampling local situations and aggregating/ averaging the scores]

Population ‘risk’ score, via expert opinion, assessing key threats and potentially beneficial activities: Record as: Good, Medium, Poor (plus intermediates) or use coding as per Article 17 reporting: F / U

Population score, via expert opinion, assessing key threats and potentially beneficial activities: Record as: Good, Medium, Poor (plus intermediates) or use coding as per Article 17 reporting: F / U



3.5. Setting values for Favourable Reference Values

Using the principles described in Table 1, and taking into account guidance from all available sources, we propose a process for setting Favourable Reference Values. This is summarised as follows:

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Whilst the approach to FRVs outlined in this report may appear complex at first, the recommended overall method is relatively simple. Combining all of the considerations about the FCS metrics, it is possible to define the range of great crested newts using 1994 as a baseline, and then assess population viability. Using modelling and connectivity approaches outlined elsewhere in this report, it should be possible with modest additional work to describe current status and favourable status using population data and habitat proxies. Note that trends should also be considered when setting FRVs.

3.6. Assessing impacts on conservation status

We also propose a method for assessing the effects of an activity, such as a development, on great crested newts. This involves a series of assessments, which is collectively termed a “conservation status impact matrix”. The rationale is that by following an assessment of each component of current conservation status, the impacts of a particular activity can be assessed objectively using a standard scoring system. This is a somewhat complex process, since conservation status and impacts each have multiple components.

There are two main approaches to assessing impacts on status: an “equilibrium approach”, which effectively assesses impacts on a ‘no net loss basis’. The alternative is to use an “FRV approach”, in which impacts are assessed specifically in relation to the maintenance of great crested newts at FCS. Both approaches have advantages and disadvantages. In essence, the FRV approach is appealing since it is closer to the wording of the Directive, and potentially allows more flexibility in regulation. However, there are more uncertainties and problems with data availability with the FRV approach. We explore the combination of FCS/FRV concepts with SDMs and connectivity analysis through a series of case studies based on real applications (section 4; Appendix 3).

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4. Applications and case studies

This section summarises the applications of the models in case studies, detailed in Appendix 3.

4.1. Background

Integrating the SDMs developed in section 2 and the conceptual model for FRVs in section 3 with connectivity analyses, this section illustrates how a modelling approach may be applied in practice to a range of scenarios and case studies. The scenarios and case studies are based on a combination of real and hypothetical data, and are aimed to show how a modelling approach can be used to inform decisions that often need to be made in conservation, planning and development mitigation. These include identifying potentially important ponds and habitat, mapping connectivity between ponds, identifying functional pond clusters, deriving baseline information to inform FRVs and evaluating risk and impact.

4.2. Applying connectivity analysis to a pond network

Using a 4 km2 area of Kent containing a pond network, SDMs were combined with connectivity analysis and existing knowledge of great crested newt dispersal to identify potential dispersal routes between ponds and other metapopulation characteristics. These models used the existing GLMs built for Kent (section 2.3.1) but assumed no knowledge of the status of newts in the ponds or pond characteristics. The model outputs can be used to identify potentially important populations, important areas of habitat and connectivity, and barriers to dispersal. However, such models need ground-truthing in order to evaluate the reliability of their predictions.

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

A hypothetical case study was constructed in which an airport was proposed within a National Character Area (NCA). The exercise used real pond distribution data but assumed that there was no information on which ponds were occupied by great crested newts. The SDM developed for Kent was applied to predict:

(1) How many great crested newt ponds would likely be affected;

(2) What the impact would be on the immediate area and associated NCA; and

(3) The effect of potential mitigation actions.

Connectivity analysis was used within the GLM to determine the impact of the development on pond clusters and linkage under three scenarios: 1. no development, 2. development proceeds with no mitigation and 3. development proceeds with mitigation.

The SDM was then used to optimise the number and location for newly created ponds that were needed to compensate for those lost to development (i.e. following the ‘equilibrium’ model). A simplistic model using a baseline set at 1990s levels (i.e. a FRV type model using reference values estimated from the time the Habitats Directive was implemented) predicted that larger scale pond creation was needed.

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4.4. Targeting eDNA surveys based on uncertainty mapping

One issue that arose from the end-user consultation was the level of uncertainty associated with the predictions that models make. End-users require an indication of the reliability of model predictions if models are to be understood and used appropriately. An advantage of using GLMs over Maxent, ensemble models and SVMs is that confidence intervals1 for the predictions can be obtained, which can be mapped. Inspection of a map showing geographical variation in the reliability of prediction would be a useful tool in targeting on-the ground surveys (Fig. 3). For example, targeting surveys towards those areas with a low reliability of prediction could result in a cost-effective way of improving the accuracy of predictions.

Figure 3. Standard error map for the Kent Generalized Linear Model SDM. High standard error indicates higher uncertainty regarding the occupancy probability for each cell. Standard error values allow for the computation of lower and upper confidence intervals for the predictions and can also be used for targeting surveys.

1 Upper and lower limits can also be obtained for Maxent and ensemble models through bootstrapping. However, compared to GLMs, these are not analytical confidence intervals and are computationally intensive to obtain for e.g. 10,000 bootstrap samples.

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Area of low uncertainty

Area of high uncertainty

4.5. Development of a baseline model for evaluating FCS and FRVs within National Character Areas

This case study demonstrates how Current Conservation Status (CCS) may be estimated using reference values derived from modelling. The reference values were based on the four core components of Favourable Conservation Status: population, range, habitat and future prospects.

The areas of great crested newt occupancy in the different NCAs in Kent were compared using GLM and Maxent models. Getis-Ord Hot-Spot Analysis (GOHSA) was then used to identify clustering of ponds within the NCAs. Future prospects can be modelled by extrapolating from historical trends in land use and connectivity, although this will not take account of uncertainties associated with climate change, disease risks and changes to habitat management. The case study illustrates how modelling can be used to explore the influence of different estimates of FRVs and the potential impact of development mitigation and land-use change at the NCA level.

4.6. Hull gas pipeline mitigation

This case study uses real data collected as part of a great crested newt mitigation project carried out in 2010. The project involved the installation of a 24 km pipeline near Hull that was followed by habitat restoration. A combination of SDMs, risk assessment and FCS principles was used as an alternative approach to exploring the mitigation options. A habitat suitability map was initially constructed using Maxent and presence-only records (no presence-absence data are available), and this was used to identify whether ponds likely to be impacted by the pipeline were in suitable areas. A new metric and GIS layer was then developed, termed Risk of harm, that estimated the likely impact of the development on newt populations. This layer was based on habitat suitability, distance from the pipeline and potential dispersal ability of newts (Fig. 4). Current Conservation Status (CCS) was then estimated for the four components of FCS (Range, Populations, Habitat and Future prospects) by applying the SDM to a 5 km area around the pipeline. Conservation status was then compared before, during and after the pipeline development.

Using a modified approach to impact assessment and mitigation would require a revised interpretation of the legislation. If that could be secured, there is potential for substantial changes to survey and mitigation compared with a traditional approach. In this case study, modelling clearly demonstrated that the pipeline would have minimal impact on conservation status. Depending on exactly how a revised legal interpretation were framed, at one extreme it could be possible to omit measures to capture, exclude and translocate newts, and instead focus on habitat enhancement. This would bring benefits to the newt population since habitat enhancement would likely have greater long-term positive impacts than traditional mitigation approaches. 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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Figure 4. Zoom-in on the Risk of harm layer at the west side of the pipeline, the most suitable area for great crested newts. Only two ponds (shown in red) run a relatively high risk of harm to newts. Maximum risk of harm (100 %, dark red areas in the map) represents the risk that a pond would face if it was in the most highly suitable area and at zero distance from the pipeline. For more information see Appendix 3.

4.7. Software requirements and model choice

A variety of software packages are required to carry out the analyses described (Table 2). In addition to ArcGIS and QGIS, GIS extensions are needed to conduct connectivity analyses (e.g. SDM toolbox, Lecos, Circuitscape, Linkage mapper, Conefor). Much of the modelling was carried out using the free statistical software R, which has various extensions and libraries that are appropriate for GLMs. Practitioners wishing to carry out the modelling would usually need training in both GIS and statistical software (including R).

For applications that might involve FRVs, presence-absence data and applying GLMs are appropriate. Data required for development mitigation include pond site-level information that may not be available from publically accessible layers. As this would require on-the-ground surveys GLMs would again be appropriate. GLMs – or other methods that can generate confidence intervals – would also be applicable for developing models to guide the level of survey effort needed. At a wider landscape scale, Maxent (or ensemble models) have utility for predicting distribution patterns for applications such as strategic land use planning and larger-scale conservation assessments.

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5. End-user testing – to test model applications and fitness for purpose

This section summarises the end user testing detailed in Appendix 4

5.1. Background

Multiple stakeholders and sectors are involved with great crested newt issues. Before any species distribution modelling (SDM) protocols are implemented it is essential that the needs of end-users are assessed and analysed.

The aim of this consultation was therefore to

clearly define end-user needs; understand the drivers and key issues for information needs; understand the constraints to embedding models in decision-making.

In addition, we used the end-user consultation activities to appeal for further case- and plan-based information that can be trialled in the emerging models.

Three approaches were used to gather end-user feedback on the modelling process:

an online questionnaire; a workshop for a variety of stakeholders from the amphibian community; a workshop targeted at specific end-user sectors.

5.2. Questionnaire survey

The questionnaire was designed to collect information on the requirements and views of potential end users. A draft version of the questionnaire was tested using the online platform SurveyMonkey (http://surveymonkey.net). This was then tested within the project team and further refined before being distributed to 24 consultees selected from a range of sectors involved with great crested newts (the sample size of 24 was dictated by government survey protocols, but the initial consultees were encouraged to disseminate the questionnaire to other contacts). The questionnaire gathered information on data needs, current data sources, end-user knowledge of predictive modelling, potential applications for predictive modelling, and any constraints and reservations.

5.2.1. Results

At total of 37 usable responses were received, with over 80% of respondents employed by local or national government agencies, with the remainder coming from development, consultancy and NGO sectors. Most of the respondents use great crested newt data to assess whether a plan or project is likely to affect the species, or to decide whether a survey is necessary.

Data needs were varied, with information on distribution, habitat quality, population size, presence-absence and potential impacts all cited as being important. Most of the data needs are met through bespoke surveys, local record centres, and third parties (e.g. ARGs, NGOs), with the NBN, published guidance notes and scientific publications less used. Over half of the respondents stated that they usually needed such information within a month. Views on whether the seasonal nature of great

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crested newt fieldwork was a problem varied, but most did not regard the cost of data collection as a particular problem. Over 70% of respondents had not used predictive modelling before, but only about 20% considered themselves to lack an understanding of the applications of modelling for biodiversity conservation. Those that had used modelling used mainly ARC GIS and Maxent. Over 70% of those who had modelling experience considered that models had advantages over traditional methods, but over half of these cited problems they had encountered when using a modelling approach. These included the wide confidence intervals often associated with predictions, the assumptions that need to be made, the resources and expertise needed to build the models, and the fact that models are not a substitute for field surveys. Nevertheless, over 90% of respondents considered models to have potential value in informing decision-making.

The 1 km grid square (or finer) resolution was considered the most useful for end-users. Ease of use, and understanding and communicating the outputs were regarded as particularly important features of using models. The reliability of determining presence, absence and the likelihood of a potential impact were regarded as particularly important outputs from models. With regard to potential constraints, ensuring that models complied to current policy and legislation and that outputs were effectively communicated and were acceptable to other organisations were important.

5.3. Herpetofauna Workers’ Meeting Workshop

Two workshops were held on 7-8 February at the annual Herpetofauna Workers’ Meeting.

The aims of the workshops were to

(1) introduce the principles of Species Distribution Modelling as applied to great crested newts; and (2) gather structured feedback from participants on the potential of a modelling approach.

One hundred and seven delegates, consisting of both volunteers and professionals, participated in the workshops. Each workshop had the same structure. The project team delivered a 30 minute introduction to SDMs as they might be applied to great crested newts. Delegates were then divided into groups of seven to 12 personnel, and presented with one of five hypothetical scenarios to discuss within their group (N.B. the objective was for these scenarios to provide a narrower focus for discussion rather than to gather scenario-specific feedback). After 40 minutes each group was asked to capture their discussions on a feedback sheet using the headings: ‘advantages’, ‘disadvantages’, ‘barriers to implementation’, and ‘potential end-users’, and a group member provided brief verbal feedback on their findings to the whole workshop. Groups were also asked to note any related issues that arose during the course of their discussions on post-it notes, and these were collated at the end of the workshop.

Finally, a straw-poll was taken: “Would you eventually like to see predictive modelling included as a fundamental aspect of great crested newt conservation policy, planning and guidance?”, with possible responses: Yes, No, Don’t know.

5.3.1. Results

Advantages: Most groups identified the potential of predictive modelling to assist with great crested newt conservation at a landscape level and for the various scenarios that were presented for discussion. Activities that were identified as possibly benefiting from a modelling approach included

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planning, the design and implementation of surveys, identifying important habitats and areas for great crested newts, and for analysing patterns of connectivity. Several groups identified the potential for models to inform the targeting of appropriate resources, and therefore achieve improved cost-effectiveness in project design and implementation. However, modelling was seen as an additional tool – not a replacement tool – for practitioners. Species distribution models have the potential to be adapted to cover other protected species and habitats, and harmonisation with initiatives focusing on other taxa was mentioned.

Disadvantages: Issues associated with data collection, management and dissemination came out strongly in the feedback forms and ensuing discussion. Survey effort is very patchy across the country, and models built using data from areas with good coverage may not be replicable in other areas. Equally, concerns were expressed over current national and regional platforms and protocols for collecting data on great crested newts, in terms of the amount, quality and availability of the data they contain. This resulted in a degree of scepticism from some delegates concerning whether the construction of reliable models would be possible for some areas. Ground-truthing would still be required to confirm status and habitat quality in most areas. Model performance was also raised as a concern, particularly with regard to the levels of confidence that may be associated with predictions. Great crested newts sometimes occur in unusual habitats and these may not be predicted by models. Equally, the possibility that decision-makers may place inappropriate emphasis on models was raised (e.g. either rejecting model outputs out of hand, or – at the other extreme – accepting them with insufficient scrutiny of assumptions and limitations).

Barriers to implementation: There was considerable discussion about how a modelling protocol might be implemented and who would have access. Would there be local or regional hubs that use a common protocol or a single centralised facility? Would access be granted to both volunteers and professionals and what would the cost implications be? How would the skills and training requirements be met? As current legislation regards individual great crested newts as protected, policy and legislation may be needed if a risk-based approach is adopted.

Data collection and dissemination issues (as discussed under ‘Disadvantages’) were seen as a significant barrier. There may be little point in investing in a sophisticated modelling system when infrastructures are not in place to access and validate the relevant data.

Potential end-users: Every group identified ecological consultants and Local Planning Authorities as potential users. Statutory agencies, universities, Local Records Centres, voluntary Amphibian and Reptile Groups (ARGs), national NGOs and local NGOs were all identified as other potential users by 11 of the 12 groups. Many groups suggested the models may be used by ARGs and volunteer surveyors.

Results of the straw-poll: Responses to the question “Would you eventually like to see predictive modelling included as a fundamental aspect of great crested newt conservation policy, planning and guidance?” Yes: 73%, No: 5%, Don’t know: 22%.

5.4. Professional end-user workshop

An end-user workshop for professionals involved with great crested newt issues was held on 30 June 2015.

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The aims were to:

(1) present results of initial modelling exercises to stakeholders; and

(2) obtain feedback from different stakeholder sectors on how such models might be received and applied within their sector.

Following consultation with the steering group, 59 personnel from a range of sectors were invited to attend the workshop. Twenty-four personnel responded positively, representing five sectors as follows: statutory agencies (N=6), local government (N=5), recording groups/NGOs (N=3), consultants (N=6), developers/utilities (N=4).

Following introductions and an explanation of the aims of the workshop, a presentation described how models were constructed using the eDNA dataset; demonstrations of different models; model performance in different regions and at different scales; and data, hardware, software and expertise requirements. This was followed by a general discussion. Participants were then divided into five break-out groups by sector, and a facilitated discussion aimed to capture:

(1) applications for that sector;

(2) advantages and disadvantages;

(3) implementation; and

(4) general comments.

The results of the discussions were captured on a structured form and a representative from each groups provided oral feedback at the end of the session. Groups were also asked to note any related issues that arose during the course of their discussions on post-it notes, and these were collated at the end of the workshop. Each sector was also asked to provide a response to the question: “Does your group think that a modelling approach could help decision-making within your sector?” with possible responses: Yes, No, Don’t know.

5.4.1. Results

The applications, advantages, disadvantages and implementation issues were broadly the same as those raised at the Herpetofauna Workers’ Meeting workshop (section 3.1), but it was possible to identify some sector-specific issues.

Developers/Utilities: Identification of ‘risk’ and early flagging of great crested newt issues were perceived as potential applications. The impacts of canals, roads and railways comprise a substantial component of casework, and connectivity analysis could assist with exploring the impacts of these as both potential barriers and potential corridors. There was concern that the risks and levels of uncertainty associated with model outputs may be too high for due diligence purposes, and scepticism about the effectiveness of current data management systems. Equally, there was concern that modelling might increase – rather than decrease – the bureaucracy associated with licensing. In response to the question “Does your group think that a modelling approach could help decision-making within your sector?”, three respondents gave a qualified ‘yes’ that predicated upon the issues raised being resolved, and the fourth respondent said ‘no’.

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Consultants: Modelling was regarded as a potentially useful tool for many aspects and stages of consultancy work (e.g. scoping, designing surveys, impact assessment). Issues were raised concerning the data quality, data management and availability, expertise and resource requirements, and the possible impact on small businesses with no modelling expertise. A well-managed centralised, online system for managing data was regarded as important. The group response to the question “Does your group think that a modelling approach could help decision-making within your sector?” was ‘yes’.

Local government/planning authorities: Modelling could be used to inform development decisions, planning and site allocation, and improve landscape-level decision-making. It was also seen as a tool that can raise awareness and strengthen the evidence base for decisions. Concerns were expressed over the level of certainty associated with model predictions: it would not remove the need for local surveys at a site level. Equally, model outputs could be misused in order to object to decisions that may be made. Needs include training, improved expertise and communication, and clarity concerning ownership of the models and the data that they utilise. The group response to the question “Does your group think that a modelling approach could help decision-making within your sector?” was ‘yes’.

Government agencies: Potential applications included strategic planning, agri-environment schemes, targeting surveys, licensing, and strengthened reporting for Favourable Conservation Status under the Habitats Directive, and improved impact assessment for mitigation. Multiple models at a range of different scales may need to be developed for different purposes and appropriate resources and expertise need to be in place. Better integration between public and private sectors is needed for implementation, and embracing other species and habitats within modelling protocols would be cost-effective. The group response to the question “Does your group think that a modelling approach could help decision-making within your sector?” was ‘yes’.

NGOs and recording groups: Modelling approaches can support the work of volunteer groups and local record centres (LRCs), improve validation, and identify gaps that need to be filled. As most volunteers and many LRCs don’t have the expertise to run models or interpret the outputs. Implementation, capacity building is needed so that modelling can be carried out to an approved standard at local level. Further standardisation of the collection and processing of eDNA samples may be needed. The group response to the question “Does your group think that a modelling approach could help decision-making within your sector?” was ‘yes’.

6. Conclusions

6.1. Opportunities and challenges in adopting predictive modelling

This report demonstrates there are clear benefits to using predictive modelling in decision making affecting great crested newts. Models can be created relatively easily, and could benefit a range of conservation and management applications. Whilst spatially fine-scale predictions are difficult to achieve with high confidence, the increased value of information that modelling can provide could transform some work areas with modest additional development; in some instances, the existing techniques are sufficient to see real world changes in decision making.

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It is crucial to recognise, however, that two parallel considerations are needed to maximise the benefits of predictive modelling: risk-based decision-making, and an explicit reference to FCS. Evidently, some level of risk assessment is already inherent in operational work, although it is not always recognised explicitly. For example, even though it is known that great crested newts can disperse over 1 km from a breeding pond, very few developments over 500 m from such ponds would ever consider the risk of impacting on newts. This would appear generally sensible, as the evidence shows that long distance dispersal is comparatively rare; yet, there remains a risk and inevitably many newts are killed each year nationally because of this accepted norm. Likewise, even on well implemented mitigation projects where there are efforts to remove newts before development, it is highly likely that some individuals will remain uncaptured and would therefore be killed with earthworks commence. Moreover, there is little recognition of the fact that survey methods (including the newly developed environmental DNA techniques) can fail to detect newts in some circumstances, and therefore any survey runs a risk of incorrectly concluding that newts are absent. The fact that such risks are taken routinely, and that newts are inevitably killed, often seems not to be explicitly recognised.

For substantial gains possible with predictive modelling, it would be useful to re-assess the framework for risk assessment. Presently, risks tend to be framed around the likelihood of harm to individual newts. Using modelling combined with an FCS approach, it would be possible to shift risk assessment to an evaluation of the likely impact on conservation status. That would lessen the reliance on assessing risk to individual newts, and allow a substantially more strategic interpretation of the Directive. Importantly, this would need a clear FCS framework, as explained elsewhere in this report.

The consequences of using predictive model outputs in decision-making require some further consideration. It was clear from the stakeholder consultation that there is a deep-seated concern over activities leading to a risk of killing newts, often based on concern over whether a model fails to correctly establish whether newts occur in a particular pond. Yet, with a modified approach to regulation based clearly on FCS principles, it would be possible to change this paradigm radically. Incorrect model predictions have different consequences under different circumstances, and using an FCS approach would mean that the risks associated with killing newts could be relegated where appropriate. In essence, the likely scale of impact of any given activity on conservation status could be used to moderate the consequences of relying on model outputs. For example, imagine a case in which a well-supported model predicts that a particular 1km square is unlikely to support great crested newts. There is a proposal to undertake development activity there that would, in the worst case scenario, only have minimal impacts on conservation status if newts occurred despite model predictions, and those impacts would be easily mitigated via habitat enhancement. In such a case, it could be argued that no prior survey or mitigation would be needed. Even if newts were subsequently discovered, a revised policy might accept that the habitat enhancement measures mean that no further attention need be paid to individual newt protection, since the FCS approach is supported. The full implications of such an approach need further investigation but do show merit.

In some cases, the consequences of a model being incorrect would potentially be more problematic if that is the only tool available. However, where it is critical to have a high confidence in model

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outputs, it might be sensible to recommend additional habitat survey to improve model performance, or to undertake traditional newt surveys.

Alongside prediction of presence and absence, it is important to recall that models can help in other ways. In particular, there is massive potential in the connectivity and hot spot analyses shown in this project and in recent work in Wales (e.g. Arnell & Wilkinson, 2013). Such work can, and indeed already has, usefully informed planning casework and habitat restoration.

Alongside these possible changes, there would need to be shifts in the interpretation of the legislation, backed up by statutory guidance, to allow stakeholders the confidence to make decisions without fear of legal challenge. Underlying this approach there would need to be enhanced national surveillance so that the status of great crested newt populations can be assessed with reasonable confidence. Specific monitoring of regulatory outcomes would also be required, although it would be possible to do this using a graded approach that minimised the costs to developers.

The published evidence indicates that outcomes for great crested newt populations subject to mitigation are often uncertain or poor in the long-term (e.g. Lewis et al, 2014; Griffiths et al, 2005). Any shift in policy on managing newt populations would therefore need to consider the underlying reasons for these poor outcomes. This is a complex area not considered in detail further here, but it is suggested that any revised management approach should pay particular attention to long-term security of habitat management, subsequent habitat fragmentation, increased risks of fish or invasive plant introduction, and monitoring feeding into management action.

6.2. Lessons from spatial risk assessment in other fields of nature conservation

This project has assessed how information transfer has been addressed in other conservation issues with important elements of spatial analysis and risk assessment. Resulting from this, one implementation option is to make great crested newt predictive models (or their outputs) available as an online GIS tool. This would perhaps work best for applications where the function is a simple risk assessment or “heat map”. Perhaps the best example is Natural England’s Impact Risk Zone (IRZ) tool (see Middlehurst & Knight, 2015). The IRZ tool is designed to help Local Planning Authorities and developers check whether a particular proposed development is likely to affect a SSSI. One of the benefits of this approach, Natural England hopes, is that LPAs will effectively answer development control queries themselves without seeking case-by-case support from Natural England, thus reducing casework burdens.

The IRZ tool can be accessed via one of two routes: using a download of GIS datasets from gov.uk for use on the user’s computer, or online via Magic (the government’s interactive mapping website). The user selects the location of a proposed development, and this generates a query that interrogates a dataset comprising SSSI boundaries, impact zones and sensitivities. See below for screenshots (Fig. 5).

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Figure 5. Figure demonstrating how the IRZ tool has been used to assess potential impacts from hypothetical development near Hothfield Common SSSI (Ashford, Kent).

The IRZ tool has relevance to simpler modelling applications great crested newts for the following reasons:

Essentially it is a way of allowing many users to undertake risk assessment online, which is the fundamental purpose behind a possible risk assessment application for great crested newts

It allows graded assessment of risk It uses a shared, open access online platform

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There is an existing infrastructure (the Magic website) and so additional infrastructure cost is avoided.

To further explore this possibility it would be desirable to investigate various issues, including data flow, guidance and governance of the tool. In particular, the scope and limitations would need to be explained clearly.

6.3. Implementing a modelling framework for great crested newt conservation

Based on the results of the modelling exercises, the application of SDMs to the case studies, consideration of the FRV requirements and end-user feedback, Table 2 provides suggestions for data choice and availability, modelling method, and the expertise needed to perform the main types of modelling exercises for great crested newts. The information in the table is compiled by application type, considering that different applications would require different outputs from the models, with different levels of accuracy and at a variety of scales.

The most demanding models in terms of data requirements, accuracy requirements and uncertainty estimation would be required for planning decisions at the local scale. Examples include risk-based development planning, strategic planning and evidence-based mitigation. In general, SDMs and connectivity applications that require the smallest possible scale (e.g. pond level, or 25 m cell size) are the most demanding in terms of data (and data costs), expertise and the diversity of model outputs required.

A fundamental difference between SDM models built using presence-only data and models built use presence-absence data is that the output of presence-only models are relative maps of habitat suitability. In contrast, SDMs built using presence-absence data reflect more closely true occupancy probability. This difference should play a significant role when choosing what model to use, and when interpreting the model output.

Presence-absence data are more informative than presence-only data for model building. However, a large sample of sites may be needed for reliable model construction (as a rule of thumb, over 100 sites with at least 50% occurrences). Two caveats have to be introduced here:

(1) all great crested newt detection methods developed so far, including eDNA, run the risk of producing false absences. One solution, though expensive, is to employ occupancy modelling to try and model occupancy and detection thereby reducing uncertainty due to non-detection (Guillera-Arroita et al. 2015). However, occupancy modelling requires repeated surveys in order to estimate occupancy and detectability.

(2) because SDMs are usually constructed on map grid cells, only complete surveys of cells can produce true absences for model building and validation.

Combining our findings with those in the literature, our suggestions for modelling data requirements are:

- If reliable presence-absence data are available together with the required expertise, computing power, and model accuracy, the preferred option would be GLMs. For local case

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studies with a small cell size (e.g. 25 m), model choice is not the limiting factor for model accuracy. The limiting factor is that as area extent and/or cell size decrease, pond-related variables (such as presence of fish) become more important and these are not generally available as layers within a GIS. Thus, while the modelling exercise could be carried out at the pond level, the resulting model would not be an SDM, because it would not be a map of occupancy probability or suitability. Thus it would be unsuitable for many potential applications (e.g. most FRV values analyses, connectivity analysis, or risk of harm analysis).

- The environmental variables used are crucial in determining what the model represents in terms of niche. When mostly bioclimatic and general environmental layers are used as covariates and the grain (cell size) is large (e.g. 400 m), the resulting model is more a representation of fundamental niche (i.e. the habitat potentially available for newts). When the resolution of the model decreases (e.g. 25 m) and environmental variables are more “tied” to pond characteristics (e.g. distance to grassland, amount of woodland cover at a 250 radius from the cell), the model may be closer to the realised niche (i.e. the habitat actually occupied by newts, which is always smaller than the fundamental niche). However, the realised niche is difficult to model, as it involves taking account of a diversity of – often unknown – factors, including biotic interactions, resource restrictions, disturbances and catastrophes.

- Considering the wide (potential) availability of presence-only records for great crested newts, the accuracy with which Maxent can model habitat suitability and its relative ease of use, Maxent-based models using presence-only data could be the tool of choice for a variety of applications. As building SDMs using GLMs usually requires the use of R and its script-driven interface, Maxent, with its user-friendly interface and input formats could prove suitable for local record centres, local and county councils, or consultants/developers2. While ensemble models perform slightly better than Maxent models, they are more difficult and time-consuming to build (R is required for building the ensemble SDMs described here).

- The SDM-building methodologies described here are not very accurate at predicting at the local scale, mainly because certain important and statistically significant variables cannot be mapped. For example, fish presence and pond permanence – both important factors affecting great crested newts – cannot be mapped as a continuous grid that is the building block (and result) of SDM exercises. This fact does limit the use of SDMs for predicting great crested newt presence and absence, but does not preclude their use for a variety of different applications, as shown in the case studies (section 4).

- Moving from a pond level for great crested newt conservation to a metapopulation level does open up the potential for the use of SDMs, utilising connectivity information that pond level analyses and methods cannot provide.

- Both the SDM models and the connectivity analyses require rigorous field validation and testing. In particular, the connectivity analyses and the methodologies cannot be fine-tuned – and could even be inaccurate – without further field work and data collection related to great crested newt dispersal through heterogeneous landscapes.

2 Suggestions on experience are based on our impressions from the six one-to-one sessions we conducted with interested parties. These included record centres, consultants, local councils, and Natural England. Additional input was gleaned from the workshop conducted in Defra in July 2015.

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Table 2. A summary of strengths and weaknesses of the models for a variety of applications. In all cases, reliable presence-absence (PA) data are preferable to presence-only (PO) data. Thus, in the column Type below, we refer to what kind of data would be sufficient, instead of preferable.

Strengths and weaknessesGreat crested newt data Modelling Model outputsµ

General CommentsApplication Typeγ Availability Quality Management Method Expertise* Accuracy

National/county

strategic conservation

planning

PO (4 or 6 digit)β

There are a large amount of records

available at the NBN gateway, Local

Record Centres or ARGs

Not all areas are covered equally; wide variation in

date and verification status

Major improvement possible, considering

difficulties in accessing available datasets

Maxent, provided it is understood that its output

is a map of relative suitability and not

occupancy probability

Maxent-only methods are less demanding in

terms of expertise; there is a plethora of freely available tutorials and scientific papers online

Maxent models are known/ were shown in this report

to be very accurate at large scales; ensemble models

are more accurate, though much harder to run

Using the full NBN Gateway dataset and a sufficient amount of

consultants’/developers’ data, extra refinement to national scale models could be undertaken (e.g. downscaling to 25 m resolution)

Strategic planning, from

local to NCA scale

Application dependent, both PA and PO at a high

resolution (8-digit)

Difficult to acquire, dependent on data

holders

Patchy, some areas very good quality and quantity of

data; some areas almost no data

Room for improvement; wider access to (>6 digit)

accurate great crested newt records could be contested by holders

Maxent/GLM, depending on the application (see

general comments); connectivity analysis would

also be suitable

Maxent-only methods easiest to use; if GLM

and connectivity analysis are employed, advanced statistics, R, and a high level of GIS experience

are required

Accurate predictions at the local scale (small extent,

fine grain e.g. 25 m resolution) using an SDM

approach is unlikely

If relative suitability is required, e.g. for prioritising great crested

newt protection, PO data/Maxent would be sufficient; if occupancy

probabilities are needed, PA/GLM. Combination with connectivity

analysis highly preferable

Local development

planning/ mitigation/lice

ncing

Application dependent, both PA and

PO (preferably 8-

digit); PA would be

preferable

Difficult to access; dependent on

individual holder

Patchy, some areas with good quality and quantity of

data, some areas almost no data. Very

few PA datasets available

Room for improvement,

although wider access to (>6 digit) accurate great crested newt

records could be contested by holders

Maxent/GLM, depending on the application (see

general comments), although PA/GLM would be

preferable; connectivity analysis would also be

valuable for metapopulation level

analyses

Possibly the application that requires the highest level of expertise, since GIS, R, Conefor/Linkage

Mapper are all used

Accurate predictions for individual ponds (small

extent, fine grain e.g. 25 m resolution ) using an SDM

approach is unlikely. Possible use of a pond-level

approach using PA

Considering that a better understanding of absence would

be important, PA/GLM is preferable and suitable;

combination with connectivity analysis suggested

Conservation management

Data type and resolution depends

mostly on the application

Varies, depending on type and scale;

PA much more difficult and costly to acquire than PO

Patchy, some areas very good quality and quantity of

data, some areas almost no data; very

few available PA datasets

Could greatly benefit from central data repository, since

currently data management is very

heterogeous

All modelling methods and data types depending on application; scale is less important than type of

output (confidence intervals,

suitability/occupancy)

Application dependent; from the simplest

(PO/Maxent) to the most complex

(PA/GLM/Connectivity analysis)

From very accurate models (see Kent-wide GLM), to

fundamental niche models (less information on

absence)

Could benefit from all approaches; the possibility to move from

individual/pond level to a metapopulation/landscape level

could be useful

Conservation monitoring

(e.g. CCS, FRV)

If true and not relative

values are required, PA data; per grid cell sampling is important

PA data are not readily available for

most areas in England and the UK; as scale increases,

PA sampling becomes costlier

We have no knowledge of any

per grid cell (any cell size) survey for PA

data

Major improvement needed. Data integration at

reporting/ monitoring level could be very

beneficial

PA/GLM models would be preferred

Complex, depending on FRV/CCS value being

modelled; PA/GLM/Connectivity

analysis the most expertise demanding

From very accurate models (e.g. Kent-wide GLM), to

fundamental niche models (less information on

absence)

GLM models are able to provide standard errors and confidence intervals; connectivity analysis

could also be used for metapopulation level analyses

* All analyses and models constructed in this report require knowledge and experience with GIS-platforms; use of GIS software should be considered de-facto for all applications. A basic understanding of ecological modelling is also required for all the analyses and models presented herein; scientific papers and tutorials should be consulted and understood before proceeding with analysis and interpretation.γ Two types of data are used in SDMs: presence-absence (PA), and presence-only (PO).µ From the models presented here, only SDMs allow for the estimation of analytical standard errors and confidence intervals.β Digits refer to Ordnance Survey Grid codes’ digits. Eight-digit records are centred on the cells of a ten-by-ten metre grid that covers the UK (e.g. TQ23451234; digits refer to the numerical characters). Six digit records are centred on the cells of a 100-by-100 metre grid, and 4 digit records of a 1000-by-1000 metre grid.

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6.4. Suggestions for future work

Both traditional (e.g. torch counts, trapping) and environmental DNA methods suffer from imperfect detection of great crested newts. Further development of SDMs should incorporate the principles of occupancy modelling to account for imperfect detection (Sewell et al. 2010; Guillera-Arroita et al. 2015).

The development of a SDM protocol for great crested newts and their habitats would benefit from alignment with SDM protocols being developed for other taxa and their habitats (e.g. bats).

Remote sensing may provide some of the site-specific data that are needed for small scale SDMs.

Predictions from SDMs would benefit from ground-truthing in the field. Process-based mechanistic models could be coupled with SDMs to account for factors such

as dispersal, disturbance and population dynamics (Franklin 2010). This might improve predictive power at the local scale.

Combining economic-land use modelling and SDMs to assess the future conservation status of great crested newts under different agricultural, development and policy scenarios.

Data on great crested newt distribution are fragmented between a range of organisations, vary in quality, and are not well-coordinated. Data management and data flow systems need to be improved if SDMs are to utilise all available information.

Combining available population data and expert opinions within a Bayesian modelling framework deserves exploration within great crested newt conservation (e.g. Kuhnert et al. 2010).

Further development of a working rationale for describing FCS and FRVs at appropriate spatial scales, based on the work developed here and ongoing work elsewhere. The legal framework requires particular attention.

Protocols for assessing impacts on great crested newt conservation status using an FRV approach could be further developed, particularly in light of the encouraging case studies presented here.

Further assessment of the advantages and disadvantages of combining modelling, risk assessment and FRV concepts in different applications.

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