Habitat connectivity

Figure 1: Composite trends in functional connectivity of butterflies in the UK, 1985 to 2012, using a 10-year moving window

Figure 2: Summary of individual species trends in functional connectivity of butterflies in the UK across three time periods, 1985 to 2012, using a 10-year moving window

Figure 3: Composite trends in functional connectivity of woodland birds in the UK, 1985 to 2012, using a 10-year moving window

Figure 4: Summary of individual species trends in functional connectivity of woodland birds in the UK across two time periods, 1985 to 2012, using a 10-year moving window

Relevance

Habitat loss and fragmentation was identified by the Millennium Ecosystem Assessment as one of five direct drivers of biodiversity loss. Habitat loss is a significant driver of biodiversity loss in the UK (Lawton et al., 2010). It results in fragmentation whereby habitats are separated into small, isolated patches (Fahrig, 2003). This inhibits individuals from dispersing across the landscape which is essential for metapopulation persistence, range shifts under climate change, and maintaining genetic diversity (Hanski, 1997; Watts & Handley, 2010). Quantifying functional connectivity — the ability of a focal species to move between resource patches (Oliver et al., 2017; Powney et al., 2011) — is therefore important in order to manage landscapes appropriately and reduce the risk of species extinction (Powney et al., 2012).

Habitat fragmentation and loss can be cumulative over time, but may be reversed through habitat management, restoration and recreation. Many of the habitats in the UK landscape are already highly fragmented. The effects of habitat fragmentation can be compounded by habitat management affecting source populations, as well as changes in land use between patches. The importance of these changes depends on the configuration of habitats (for example, edge effects) and the ease with which species can move through the intervening landscape (permeability).

International/domestic reporting

The UK Biodiversity Indicators have been reviewed in response to the latest goals and targets agreed under the Kunming-Montreal Global Biodiversity Framework (GBF) of the Convention on Biological Diversity (CBD). The indicators are being adapted, with some new indicators being developed, to better align them with the monitoring framework for the GBF. The suite of biodiversity indicators is therefore expected to change ready for forthcoming UK national reports to CBD in 2026 and 2029.

Web links for further information

• UK Butterfly Monitoring Scheme: Home Page
• UKCEH: Countryside Survey
• UKCEH: Land Cover Maps
• Forestry Commission: Evaluating Biodiversity in Fragmented Landscapes
• Millennium Ecosystem Assessment: Home Page

Background

Functional connectivity is determined by the number of individuals leaving patches (for example, emigration often when local abundance is high), the intrinsic dispersal capability of individuals, and the structure of the landscape facilitating or hindering movement (Figure 5). Certain methods to measure functional connectivity, such as mark-release-recapture studies or landscape genetics are expensive, time consuming and can only be conducted over small spatial scales. Larger-scale (national) indicators therefore have tended to focus on structural metrics based on land cover combined with expert opinion on species’ habitat associations and movement capacity (Watts & Handley, 2010). While useful, these approaches are limited by the frequency by which land cover data are updated and by substantial uncertainty in using expert opinion to estimate species’ movement capabilities across land cover types. This indicator uses a data-derived method based on widely available, annually updated species monitoring data – which gives a ‘species-eye-view’ (empirically derived) measure of functional connectivity.

Figure 5: Schematic of factors that influence functional connectivity, which in turn can affect species extinction risk

Figure 6: Locations of the monitoring sites in the UK for each dataset, (a) UKBMS (n = 701), (b) CBC (n = 109), and (c) BBS (n = 2,499)

Methodology

Population synchrony in growth rates (that is, interannual population changes; following Powney et al., 2010) was calculated for all pair-wise monitoring site combinations, using a 10-year moving window from 1980 to 2016 for the UKBMS dataset, 1980 to 2000 for the CBC dataset, and 1994 to 2016 for the BBS dataset. A mixed effects model was fitted, with population synchrony as the response variable, and the mid-year of each moving window included as a fixed categorical effect. To account for other known drivers of population synchrony, distance between sites, habitat similarity index, and mean northing were included as predictors in the model (Powney et al., 2011). Site pair ID and species were included as random intercepts. Coefficients for each year were extracted and used as the measure of functional connectivity along with standard errors to reflect uncertainty in the estimate.

To determine how many butterfly species were changing in functional connectivity over time, three periods of change were investigated: two short-term trends; early (1985 and 2000) and late (2000 and 2012), and one long-term trend (1985 and 2012). For birds, two time periods were chosen: early short-term using the CBC dataset (1985 to 1996) and late short-term using the BBS dataset (1999 to 2012). These time periods were chosen to ensure there was no overlap in the 10-year moving window (that is, they represent independent input data). For each time period comparison, and for each species, coefficients and their uncertainty were extracted from the mixed effects model to determine whether connectivity had significantly increased or decreased, or there had been no significant change between the two comparison years (Figure 1 and 3).

It is important that the measure of functional connectivity reflects the three main components determining movement between sites (Figure 5) and not confounding effects. Two possible confounding effects could be a) a temporal trend in spatial autocorrelation in climate over time, or b) increasing variance in climate over time. To test for a), Moran’s I (a measure of the overall spatial autocorrelation in a data set) was calculated for four climatic variables: mean temperature and rainfall for each season (spring, summer autumn, winter). Linear and quadratic regression models were fitted for each variable against year. These models showed no significant trends suggesting no evidence for changes in spatial autocorrelation in climate over time. To test for b), the variation in seasonal mean temperature and total precipitation were compared between 1985 to 2000 and 2000 to 2012. Analysis using an F-test revealed no significant changes in variance between the two time periods.

Findings

Evidence supporting a strong signal of species movement on population synchrony:

1. Estimated quality of intervening landscape between sites is positively related to population synchrony (Speckled wood butterfly at scales of up to 100 km, Powney et al., 2011)
2. Distance along woodland edges is a better predictor of movement than Euclidean distance – a similar result was found for both population synchrony and mark-release-capture (Ringlet butterfly; Powney et al., 2012)
3. Patches with higher frequency of movements between them from mark-release-capture experiments also have higher population synchrony (bog fritillary butterfly; Oliver et al., 2015).
4. Species that are mobile and more abundant have higher levels of population synchrony and these results are robust to an additional effect of species position in their geographic range (61 butterflies and birds; Hordley et al., 2022).

Evidence finding little signal of climatic changes on trends in population synchrony:

1. No significant temporal trend in spatial autocorrelation of seasonal temperature and precipitation variables (Hordley et al., 2022).
2. No significant temporal trend in variability of seasonal temperature and precipitation variables (Hordley et al., 2022).

Development plan

In line with our commitment to the Code of Practice for Statistics, we have published our plan for developing these statistics further. This plan outlines what we hope to do to improve our publications in future. This plan is informed by responses from users and stakeholders, as well as the UK Biodiversity Indicators Steering Group. We are keen to hear feedback from users of these statistics, please send your feedback to: Enviro.Statistics@defra.gov.uk.

We will look to update our development plan at least annually.

If the Biodiversity Indicators production team have the capacity to update this indicator next year, our planned developments will be:

We will review the methodology to ensure its usefulness for users and the commentary for ease of interpretation.We will investigate alternative data sources that are relevant to habitat connectivity, such as Living England, and links with other indicators of connectivity (D1: Quantity, quality and connectivity of habitats).

If these planned developments affect the way that you use these data, including the possibility that this indicator may not be updated again in the future, please do get in touch.

Archived measure

Until 2013, this indicator was based on an analysis of the change in habitat connectivity for selected broad habitats in the wider countryside. The indicator methodology was developed by Forest Research in collaboration with the UK Centre for Ecology & Hydrology, using Countryside Survey data collected consistently from 591 Countryside Survey 1 km² sample squares in Great Britain in 1990, 1998 and 2007. The results of this work provided a significant step forward in understanding and describing habitat fragmentation and connectivity, but unfortunately it has not been possible to update this indicator since the last Countryside Survey was carried out in 2007.

Given the age of the most recent data and the lack of any future opportunities to source updates in a consistent way, the UK Biodiversity Indicators Steering Group decided to reclassify this indicator as ‘under development’ and look at new options for a headline measure. Key messages from the previous indicator update are presented here; the previous indicator can be viewed in full via The National Archives.

The indicator presented the change in the degree of habitat connectivity in Great Britain between 1990 and 2007, for two broad habitats (see Figure 7):

1. Broad-leaved, mixed and yew woodland. This includes all woodland with a canopy cover of at least 25%, where more than 80% of the canopy trees are broad-leaved species or yew trees.
2. Neutral grassland, which includes all grassland on neutral soils including both unimproved and semi-improved grassland.

The indicator provided a measure of functional connectivity of these two habitats in the wider landscape (that is, the relative likelihood of species typical of the habitat being able to move within and between habitat patches). The calculations took into account the area of habitat patches, how isolated they are, which habitats are next to each other, and the ease with which species are able to move through the surrounding landscape. The influence of habitat quality on species was only partially covered by this indicator.

The measure required further analysis to better explain the causes of the changes in connectivity and, as a result, the information available was insufficient for an assessment of change to be made. The indicator did however show a non-significant declining trend in the connectivity of broad-leaved, mixed and yew woodland in Great Britain, and an increasing trend in the connectivity of neutral grassland. The increase seen for neutral grassland was significant between 1990 and 2007 but not in the short term between 1998 and 2007 (Figure 7).

Figure 7: Change in habitat connectivity for selected broad habitats in the wider countryside of the UK, 1990 to 2007