Oceanic and temperate rainforest climates and their epiphyte indicators in Britain

Oceanic and temperate rainforest climates and their epiphyte indicators in Britain

Ecological Indicators 70 (2016) 125–133 Contents lists available at ScienceDirect Ecological Indicators journal homepage: www.elsevier.com/locate/ec...

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Ecological Indicators 70 (2016) 125–133

Contents lists available at ScienceDirect

Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Oceanic and temperate rainforest climates and their epiphyte indicators in Britain Christopher J. Ellis Royal Botanic Garden Edinburgh, 20A Inverleith Row, Edinburgh, EH3 5LR, UK

a r t i c l e

i n f o

Article history: Received 11 March 2016 Received in revised form 1 June 2016 Accepted 3 June 2016 Keywords: Biogeography Epiphyte Lichen Oakwood Oceanic Temperate rainforest

a b s t r a c t A biogeographic zone with high oceanicity is a well described feature of the European Atlantic region. This oceanic zone intersects with a zone of European temperate rainforest that has received increasing conservation recognition. Although having a degree of spatial overlap, the terminology applied to these different zones is not synonymous. Temperate rainforest is one example of an oceanic system, alongside others such as blanket bog or liverwort heath. Conversely, oceanic systems provide one type of climatic setting suitable to the development of temperate rainforest, alongside other and contrasting landscapes such as the orographic climate of continental mountains. Zones of high oceanicity and temperate rainforest are both strongly represented in the British Isles, and this study examines the degree of spatial overlap in Britain for standard definitions of each. Lichen epiphyte indicators associated with zones of oceanic woodland or temperate rainforest were quantified, and subsequently tested for conservation priority Atlantic oakwoods (Annex 1 Habitat Code 91A0: old sessile oak woods with Ilex and Blechnum in the British Isles). Discrepancies between oceanic woodland and temperate rainforest led to slightly different sets of indicator species that could be applied in biodiversity and habitat quality assessments. The definition of oceanic systems appeared to include warmer and lowland situations for example in coastal Wales and south-west England. In contrast, temperate rainforest extended to cooler upland areas in north-eastern Scotland. The species indicators for oceanic and temperate rainforest were nevertheless effective in identifying sites with different conservation priorities, such as for protection or restoration. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Temperate and boreal rainforest is receiving increased recognition as one of the most important habitats in European conservation. Bioclimatic conditions suitable to the formation of temperate and boreal rainforest occur across < 1% of the global land surface and 15% of this suitable bioclimatic space occurs in Europe (DellaSala, 2011). European rainforest is distributed within two discrete landscapes; either montane, and affected by orographic climates such as in the continental European Alps, or oceanic, such as along the Atlantic coastlines of Britain and Norway (DellaSala et al., 2011). Ecologically, European rainforest is characterised by a distinct assemblage of epiphytic lichens and understory bryophytes, which include different facies of the widely-recognised ‘Lobarion’ community (James et al., 1977; Rose, 1988) partitioned into montane and oceanic settings (Rose, 1988).

E-mail address: [email protected] http://dx.doi.org/10.1016/j.ecolind.2016.06.002 1470-160X/© 2016 Elsevier Ltd. All rights reserved.

The British Isles have the most strongly oceanic of European climates (Metzger et al., 2005), and therefore account for c. 40% of bioclimatic space suitable to the development of European rainforest (DellaSala, 2011). The appreciation of native British rainforest is growing, in terms of biodiversity importance (Coppins and Coppins, 2012) and public appeal (Bain, 2015). This has driven new research that extends the previously well-established theme of oceanic biogeography. Recent complementary studies have tended therefore to classify regional climates and their species distribution patterns as either ‘temperate rainforest’ (Ellis, 2014), or as ‘oceanic woodland’ (Rothero, 2005). The transferability of these different classifications (rainforest versus oceanic) is an open question however. Temperate rainforest is one example of an ecosystem that occurs in oceanic settings, alongside contrasting systems such as blanket bog (Lindsay et al., 1988; Goode, 1997), or liverwort heath (Averis, 1992; Hodd and Skeffington, 2011). The oceanic climate is one situation conducive to the development of temperate rainforest, alongside different climatic settings such as in mountain landscapes (Rose, 1988; DellaSala et al., 2011). This incomplete reci-

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Fig. 1. Maps for A. The trend in hygrothermy (≈oceanicity) across Britain, and B. The distribution of grid-squares corresponding to the temperate rainforest bioclimatic zone.

procity leads to potential confusion in the application of concepts. Furthermore, the lichen epiphytes that are important in characterising oceanic or temperate rainforest systems may be obligately associated in biogeographic terms, such as might appear to be the case for Pseudocyphellaria spp., or may be facultatively associated. Facultative species include those that transition from a broad habitat specificity in oceanic/temperate rainforest zones, to a narrower habitat specificity in relatively continental and sub-optimal climates (Ellis et al., 2009; Ellis, 2013). Examples again include elements of the Lobarion community (James et al., 1977; Rose, 1988), which can be relatively widespread and locally abundant in the British zone of oceanic/temperate rainforest. However, these same species, including the widely recognised Lobaria pulmonaria, tend to occur outside the oceanic/temperate rainforest zone in locally suitable microhabitats that are often associated with old-growth woodland (Coppins and Coppins, 2002; Whittet and Ellis, 2013). This paper attempts to clarify the ambiguous relationship between oceanic systems and temperate rainforest, both in terms of physical climate characteristics and epiphyte assemblage composition, by answering the following questions:

1. Do bioclimatic thresholds for temperate rainforest correlate with trends in oceanicity, such that these concepts are interchangeable in Britain? 2. Do a common set of lichen epiphytic indicators characterise oceanic and temperate rainforest systems?

The paper takes advantage of baseline Met Office climate data (Perry and Hollis, 2005), and a recent large-scale analysis in the distribution and modelling of British lichen epiphytes (Ellis et al., 2014, 2015), both to map the oceanic conditions of British temperate rainforest, and to identify compatible lichen indicator species. These indicators are then tested for a range of woodland sites selected

from the UK conservation network, designated under Article 3 of the EC Habitats Directive as Special Areas of Conservation. 2. Materials and methods 2.1. Bioclimatic zones Climate indices were calculated from 5 km grid-scale baseline climate data (1961–2006) interpolated across instrumental temperature and precipitation stations (Perry and Hollis, 2005). These data are publicly available for non-commercial purposes under the UKCP09 programme (see published data at http://www.metoffice. gov.uk/climatechange/science/monitoring/ukcp09/, access availability confirmed on 1st June 2016). The choice of these baseline data matched with recent bioclimatic modelling and scenario analysis for lichen epiphytes (see Section 2.2). Two standard indices were calculated. First, the presence-absence of temperate rainforest bioclimatic conditions (Alaback, 1991), circumscribed as having: (i) >1400 mm of precipitation per annum, (ii) a mean July isotherm <16 ◦ C, and (iii) with >10% annual precipitation occurring during the summer period (June, July and August). Second, using Amann’s index of hygrothermy to characterise an oceanicity-continentality transition: H = [(P × T )/t h − t c ]

(1)

where P is the mean annual precipitation (cm), T is the mean annual temperature, and th and tc are the mean temperatures of the warmest and coldest months, respectively. Amann’s index usefully integrates both hygric and thermic components which characterise contrasting oceanic or relatively more continental climates (Tuhkanen, 1980), and has been used over many years to explain the distribution of poikilohydric lichens (Seaward, 1975; Ellis et al., 2009) and bryophytes (Greig-Smith, 1950; Proctor, 1960). Values of hygrothermy were compared within and outside the temperate

C.J. Ellis / Ecological Indicators 70 (2016) 125–133

Fig. 2. The values of hygrothermy (≈oceanicity) within and outside the temperate rainforest bioclimatic zone in Britain, showing median, 25th and 75th percentiles (boxes) and 10th and 90th percentiles (whiskers), and 5th and 95th percentiles (dots).

rainforest zone, and were tested statistically using a nonparametric Mann-Whitney test. 2.2. Indicator species The identification of indicators was based on values for a species’ modelled environmental suitability within and outside a given bio-

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climatic zone (see published data at http://rbg-web2.rbge.org.uk/ lichen/scenarios/index.php, access availability confirmed on 1st June 2016). The use of modelled data as opposed to raw presence records can help moderate the confounding effects of both poorly sampled space within a species’ distributional range (with an absence of presence records), and outlying records (vagrants) beyond the normal distributional range that are accumulated into databases over time. It can also take into account the effect of climate alongside other covariables known to affect the distribution of lichens in mainland Britain (Ellis and Coppins, 2009, 2010), notably woodland habitat extent and the pollution regime. Environmental suitability values for 382 lichen epiphytes were derived from a recent bioclimatic analysis (Ellis et al., 2014, 2015). This integrated the effect of climate, woodland habitat and pollutants, for species that occur as epiphytes in ≥ 30 hectads, with models built, validated and projected using MAXENT (Phillips et al., 2006; Elith et al., 2011). A limited sub-selection of climatic variables for MAXENT modelling had to be compatible between the climate baseline and spatially-coherent climate change scenarios: annual precipitation (mm), summer precipitation (mm), winter precipitation (mm), mean annual temperature (◦ C) and mean temperature of the coldest month (◦ C). First, it was necessary to confirm that environmental suitability conditioned on the variables for bioclimatic modelling, was functionally related to hygrothermy as an index of oceanicity. Values of hygrothermy were therefore tested using a spatial regression against the MAXENT bioclimatic variables, with variance partitioned using the ‘hier.part’ function in R (R Development Core Team, 2013). Second, a t-test with Welch’s correction for unbalanced variance (R Development Core Team, 2013) was used to compare each species’ MAXENT environmental suitability scaled between 0 and 1 (logistic format), within and outside a specified bioclimatic zone, i.e. oceanic or temperate rainforest zones. The analysis sub-sampled the hectads corresponding to each specified bioclimatic zone, along with the species’ respective values for environmental suitability. The environmental suitability values were then compared to the same number of environmental suitability values though sampled from outside the bioclimatic zone, after pre-sorting to provide the highest ranked values. Indicators were therefore identified as species for which their environmental suitability values within a bioclimatic zone were significantly higher than the highest values for hectads falling outside the bioclimatic zone. Indicators were identified with and without a Bonferroni correction for multiple tests, applied to a P-value of 0.05 (Bland and Altman, 1995; Moran, 2003). Third, the significant indicators were combined in a metric, which was used to quantify the assemblage structure of woodland sites in the UK conservation network. The selected sites for testing were all designated as Special Areas of Conservation under Article 3 of the EC Habitats Directive, corresponding to the Annex 1 Habitat Code 91A0 (old sessile oak woods with Ilex and Blechnum in the British Isles) and representing Atlantic oakwoods (Baarda, 2005). Lichen species lists compiled within or overlapping these woodland sites were downloaded as British Lichen Society Mapping Scheme data (Simkin, 2012) from the NBN Gateway (see published data at https://data.nbn.org.uk/, access availability confirmed on 1st June 2016), for the period 1961–2010, and scored according to the formula: IndictorScore = (Ivi + Ivj + . . .)

(2)

where Ivi , j . . . are the indicator values for species recorded in the assemblage. The indicator values were calculated as 1 minus the ratio of the environmental suitability values outside and inside the bioclimatic zone. Thus, the indicator value for a species increases as the difference in the MAXENT modelled suitability becomes larger,

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contributing a greater effect on the overall indicator score of the assemblage.

3. Results Maps showing values in Amann’s index of hygrothermy (Fig. 1A) reveal a distinct bioclimatic region occurring over c. 8.1% of British land surface (212 hectads) subjectively defined here as ‘hyperoceanic’, with index values > 150 (values > 200 are extremely rare, representing < 1% of the land surface). This zone of hyper-oceanicity is largely restricted to a position in the central part of north-western Scotland, though encompassing outlying sites in the Scottish borders, the Lake District, North and montane South Wales and the south-west of England. The hyper-oceanic zone is nested within a much broader region of oceanic climate, covering c. 27.1% of the British land surface (710 hectads) and with index values > 100. This oceanic zone occurs extensively along the Atlantic coastline of Britain. The bioclimatic conditions characterising a temperate rainforest zone (Fig. 1B) extend over c. 20.8% of the British land surface (544 hectads) with a distribution that is broadly synchronous with the oceanic zone. Localities identified as temperate rainforest have significantly higher values of hygrothermy than those falling outside the temperate rainforest zone (Fig. 2). This relationship is significant (Mann-Whitney W = 16206189, P < 0.00001) but not exact however. Using the definitions employed here, there are certain sites that would be characterised bioclimatically as relatively more continental and rainforest, and others which would be characterised as oceanic and non-rainforest. Prior to identifying indicator species, the index of hygrothermy that was used to define oceanic climates was confirmed as functionally redundant with the climatic variables previously used to model epiphyte species environmental suitability in Britain. Thus, the five climatic variables used in MAXENT modelling explained 98% of the spatial variation in hygrothermy (P < 0.0001 with 2610 df). The extent to which each climatic variable uniquely explained hygrothermy, when having partitioned-out the effect of the other four variables, was calculated as: annual precipitation (28%), summer precipitation (25%), winter precipitation (27%), mean annual temperature (7.5%) and mean temperature of the coldest month (10.5%). Thus, values for a species’ environmental suitability can be expected to capture a response to hygrothermy, having accounted for the simultaneous effect of woodland habitat and pollution. On this basis, there were 39 species that had significantly higher values of projected environmental suitability in the hyper-oceanic zone (defined as the zone with hygrothermy > 150) compared to their highest values outside this zone (Fig. 1A; Table 1). This increased by an additional 103 species that were identified as significant indicators for the expanded oceanic zone (defined as the zone with hygrothermy > 100) (Fig. 1A; Table 1), to achieve a total of 142 indicators for the oceanic zone. There were 115 indicators for the temperate rainforest bioclimatic zone (Fig. 1B; Table 1), which included c. 76% of the 142 indicators relevant to the oceanic zone. However, an additional seven species were identified as unique indicators for the rainforest zone, and not for the oceanic zone. When examining the sensitivity of lichen assemblage structure to the climatic setting, using lichen indicators applied to Atlantic oakwoods, there was a significant positive correlation between the strength of the indicator score and the degree of oceanicity (Fig. 3A and B). However, there was scatter within the correlation, with positive and negative residuals indicating sites that, on average, have a higher or lower indicator score than expected for their given climate. The indicator scores for oceanic and temperate rainforest climates were highly correlated (Fig. 3C).

4. Discussion Biogeographically, Britain is important in Europe for having a very large proportion of bioclimatic space that is conducive to the development of European temperate rainforest. The bioclimatic concepts of oceanicity, and temperate rainforest, have been separately applied in biogeographic studies for British woodland diversity. Using two previously published (though not exclusive) bioclimatic measures for ‘oceanicity’ and ‘temperate rainforest’, this study shows that the concepts are likely to be broadly though not precisely interchangeable. Furthermore, it establishes a series of relatively widespread indicator species for two subjective degrees of oceanicity (hyper-oceanic and oceanic) and for British temperate rainforest. The species indicators must be interpreted given caveats to the bioclimatic modelling on which they are based (Ellis et al., 2014, 2015), including the four important points highlighted below. First, there is an assumption that occurrence data accumulated over a period of c. 50 years reflects a species’ distribution as an epiphyte, which can then be explained in response to environmental variables for the same period (Pearson and Dawson, 2003; Peterson et al., 2011). Britain is one of the most intensively sampled regions for its lichen diversity (Simkin, 2012), and this facilitates species distribution modelling; however, second, there was a threshold for presence records of ≥30 hectads prior to bioclimatic modelling, to ensure statistical rigour. This will have excluded some potentially significant but less frequent species of oceanic climates, such as those associated with Atlantic hazelwoods (Woods and Coppins, 2012) and including rare but potentially important species such as Arthothelium macounii, Graphis alboscripta, or Pyrenula hibernica. Third, the bioclimatic modelling was based on a sub-selection of variables that were consistent between the baseline environment (Perry and Hollis, 2005; Jenkins et al., 2008) and a set of probabilistic spatially-coherent climate change scenarios (Sexton et al., 2010). This excluded potentially important variables, such as the number of days >1 mm rainfall, which has been previously identified in explaining the distribution of epiphytic lichens (Coppins, 1976) as well as distributions for other poikilohydric guilds such as bryophytes (Ratcliffe, 1968; Hobbs, 1988). Nevertheless, in Britain at the 5 km resolution of the baseline climate used here (1961–2006), the number of days >1 mm rain is highly correlated with the alternatives used in bioclimatic modelling, such as the total amount of precipitation (r = 0.874, P < 0.0001 with 10,357 df). Given this redundancy, the results of statistical modelling will remain broadly complimentary to previous work that had focussed on different but inter-correlated variables (such as the number of days >1 mm rain). Fourth, the projected distribution patterns for indicators that have broad substratum requirements, such as for the frequently terricolous Cladonia portentosa, are relevant only to its occurrence as an epiphyte, and projections of environmental suitability must be interpreted within this context. Given a cautious interpretation of the bioclimatic modelling, the species indicators could highlight important epiphyte assemblage differences between contrasting bioclimatic zones, which are explored here in three key points. First, the expansion in zones of hygrothermy, from values > 150 (hyper-oceanic), to a wider zone with values > 100 (oceanic) has the effect of more than tripling the indicator set. Given the much larger sample of grid-squares corresponding to hygrothermy values > 100, a statistical effect size can be smaller yet achieve significance for a difference in species’ environmental suitability values within and outside the oceanic zone. The hyper-oceanic indicators remained among the strongest oceanic indicators, having the largest differences in their environmental suitability values within and outside the oceanic zone. In contrast, for some species the differences in environmental suitability were very small within and outside the oceanic zone (small statistical

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Table 1 List of indicator species associated with bioclimatic zones of increasing oceanicity (hygrothermy >100, or >150), and with temperate rainforest bioclimatic conditions (cf. Fig. 1). The code ‘Iv’ is the indicator value that can be applied to an epiphyte assemblage, when examining the representation of hyper-oceanic (the zone with hygrothermy > 150), oceanic (the zone with hygrothermy > 100), or temperate rainforest species, and P shows the statistical significance under a t-test. The code ‘IR’ indicates species for which the UK has an international responsibility for its conservation (Woods and Coppins, 2012). Results with P < 0.0001 can be considered significant allowing for Bonferroni correction. Taxon Name

Agonimia tristicula Anisomeridium ranunculosporum Anisomeridium viridescens (IR) Arthonia arthonioides Arthonia cinnabarina Arthonia elegans Arthonia ilicina (IR) Arthonia leucopellaea Arthonia stellaris Arthopyrenia carneobrunneola (IR) Arthopyrenia cerasi Arthopyrenia cinereopruinosa Arthopyrenia fraxini Arthopyrenia nitescens (IR) Arthopyrenia salicis Arthothelium ruanum Bacidia absistens Bacidia caesiovirens (IR) Bacidia squamellosa Bactrospora homalotropa (IR) Biatora vernalis Buellia erubescens Bunodophoron melanocarpum Caloplaca ferruginea (IR) Catinaria atropurpurea Celothelium ischnobelum Cetrelia olivetorum s. lat. Cladonia caespiticia Cladonia coccifera s. lat. Cladonia diversa Cladonia furcata subsp. furcata Cladonia portentosa Cladonia pyxidata Cladonia squamosa s. lat. Collema fasciculare (IR) Collema flaccidum Collema furfuraceum Collema subflaccidum Cystocoleus ebeneus Degelia atlantica (IR) Degelia plumbea s. lat. (IR) Dimerella lutea Eopyrenula grandicola (IR) Fuscidea arboricola Fuscopannaria mediterranea Fuscopannaria sampiana (IR) Gomphillus calycioides (IR) Graphina ruiziana (IR) Graphis elegans Gyalecta derivata Gyalideopsis muscicola (IR) Heterodermia obscurata Hypocenomyce friesii Hypotrachyna endochlora (IR) Hypotrachyna laevigata Hypotrachyna sinuosa (IR) Hypotrachyna taylorensis (IR) Imshaugia aleurites Japewiella tavaresiana Lecanora farinaria Lecanora jamesii Lecidea sanguineoatra Lepraria membranacea Lepraria umbricola Leptogium brebissonii (IR) Leptogium burgessii (IR) Leptogium cyanescens (IR) Leptogium lichenoides Leptogium teretiusculum Lichenomphalia umbellifera

Index of Hygrothermy

Rainforest

>150

>100

n.s. n.s. n.s. n.s. n.s. n.s. Iv = 0.395,P < 0.0001 n.s. n.s. Iv = 0.377,P < 0.0001 n.s. n.s. n.s. Iv = 0.386,P < 0.0001 n.s.

Iv = 0.211, P < 0.0001 Iv = 0.406, P < 0.0001 Iv = 0.288, P < 0.0001 Iv = 0.577, P < 0.0001 Iv = 0.289, P < 0.0001 Iv = 0.349, P < 0.0001 Iv = 0.911, P < 0.0001 Iv = 0.562, P < 0.0001 Iv = 0.622, P < 0.0001 Iv = 0.874, P < 0.0001 Iv = 0.305, P < 0.0001 Iv = 0.598, P < 0.0001 Iv = 0.402, P < 0.0001 Iv = 0.879, P < 0.0001 Iv = 0.199, P < 0.0001 Iv = 0.244, P < 0.0001 Iv = 0.166, P = 0.0103 Iv = 0.763, P < 0.0001 Iv = 0.594, P < 0.0001 Iv = 0.943, P < 0.0001 Iv = 0.704, P < 0.0001 Iv = 0.379, P < 0.0001 Iv = 0.879, P < 0.0001 Iv = 0.345, P < 0.0001 Iv = 0.319, P < 0.0001 Iv = 0.144, P = 0.0002 Iv = 0.838, P < 0.0001 Iv = 0.447, P < 0.0001 Iv = 0.337, P < 0.0001 Iv = 0.397, P < 0.0001 Iv = 0.459, P < 0.0001 Iv = 0.427, P < 0.0001 Iv = 0.067, P = 0.0169 Iv = 0.347, P < 0.0001 Iv = 0.406, P < 0.0001 Iv = 0.335, P < 0.0001 Iv = 0.448, P < 0.0001 Iv = 0.665, P < 0.0001 Iv = 0.649, P < 0.0001 Iv = 0.822, P < 0.0001 Iv = 0.53, P < 0.0001 Iv = 0.507, P < 0.0001 Iv = 0.476, P < 0.0001 Iv = 0.355, P = 0.001 Iv = 0.317, P < 0.0001 Iv = 0.839, P < 0.0001 Iv = 0.91, P < 0.0001 Iv = 0.763, P < 0.0001 Iv = 0.118, P < 0.0001 Iv = 0.187, P < 0.0001 Iv = 0.69, P < 0.0001 Iv = 0.534, P < 0.0001 n.s. Iv = 0.821, P < 0.0001 Iv = 0.766, P < 0.0001 Iv = 0.94, P < 0.0001 Iv = 0.908, P < 0.0001 n.s. Iv = 0.857, P < 0.0001 Iv = 0.161, P = 0.0003 Iv = 0.57, P < 0.0001 Iv = 0.629, P < 0.0001 Iv = 0.381, P < 0.0001 n.s. Iv = 0.969, P < 0.0001 Iv = 0.892, P < 0.0001 Iv = 0.901, P < 0.0001 Iv = 0.226, P < 0.0001 Iv = 0.143, P < 0.0001 Iv = 0.425, P < 0.0001

n.s. Iv = 0.287,P = 0.0005 n.s. Iv = 0.526,P < 0.0001 n.s. n.s. Iv = 0.332,P < 0.0001 n.s. n.s. n.s. Iv = 0.151, P = 0.001 n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Iv = 0.169,P = 0.0002 n.s. Iv = 0.382,P < 0.0001 n.s. n.s. n.s. n.s. n.s. Iv = 0.402,P < 0.0001 Iv = 0.591,P < 0.0001 n.s. n.s. n.s. n.s. n.s. n.s. Iv = 0.139,P = 0.0196 n.s. Iv = 0.489,P < 0.0001 Iv = 0.350,P < 0.0001 n.s. n.s. n.s. n.s. n.s. n.s. n.s. Iv = 0.705,P < 0.0001 Iv = 0.473,P < 0.0001 Iv = 0.462,P < 0.0001 n.s. n.s. n.s.

n.s. Iv = 0.162, P < 0.0001 n.s. Iv = 0.362, P < 0.0001 n.s. n.s. Iv = 0.785, P < 0.0001 Iv = 0.639, P < 0.0001 Iv = 0.522, P < 0.0001 Iv = 0.725, P < 0.0001 Iv = 0.245, P < 0.0001 Iv = 0.413, P < 0.0001 Iv = 0.258, P < 0.0001 Iv = 0.69, P < 0.0001 n.s. n.s. Iv = 0.234, P < 0.0001 Iv = 0.788, P < 0.0001 Iv = 0.445, P < 0.0001 Iv = 0.886, P < 0.0001 Iv = 0.799, P < 0.0001 Iv = 0.585, P < 0.0001 Iv = 0.881, P < 0.0001 Iv = 0.341, P < 0.0001 n.s. n.s. Iv = 0.631, P < 0.0001 n.s. Iv = 0.41, P < 0.0001 Iv = 0.491, P < 0.0001 Iv = 0.454, P < 0.0001 Iv = 0.478, P < 0.0001 n.s. Iv = 0.206, P < 0.0001 Iv = 0.458, P < 0.0001 Iv = 0.336, P < 0.0001 Iv = 0.241, P < 0.0001 Iv = 0.545, P < 0.0001 Iv = 0.647, P < 0.0001 Iv = 0.709, P < 0.0001 Iv = 0.474, P < 0.0001 n.s. n.s. Iv = 0.54, P < 0.0001 Iv = 0.345 P < 0.0001 Iv = 0.814, P < 0.0001 Iv = 0.831, P < 0.0001 Iv = 0.408, P < 0.0001 n.s. n.s. Iv = 0.512, P < 0.0001 n.s. Iv = 0.378, P < 0.0001 Iv = 0.696, P 0.0001 Iv = 0.531, P < 0.0001 Iv = 0.834, P < 0.0001 Iv = 0.86, P < 0.0001 Iv = 0.166, P = 0.024 Iv = 0.422, P < 0.0001 Iv = 0.192, P < 0.0001 Iv = 0.168, P < 0.0001 Iv = 0.695, P < 0.0001 Iv = 0.348, P < 0.0001 Iv = 0.14, P = 0.0062 Iv = 0.938, P < 0.0001 Iv = 0.904, P < 0.0001 Iv = 0.742, P < 0.0001 n.s. n.s. Iv = 0.454, P < 0.0001

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Table 1 (Continued) Taxon Name

Lobaria amplissima (IR) Lobaria pulmonaria (IR) Lobaria scrobiculata (IR) Lobaria virens (IR) Lopadium disciforme Loxospora elatina Megalaria pulverea Megalospora tuberculosa (IR) Melaspilea atroides (IR) Menegazzia terebrata (IR) Micarea adnata Micarea alabastrites (IR) Micarea cinerea f. cinerea Micarea doliiformis Micarea lignaria var. lignaria Micarea melaena Micarea peliocarpa Micarea stipitata (IR) Micarea synotheoides (IR) Micarea xanthonica (IR) Mycobilimbia epixanthoides Mycobilimbia pilularis Mycoblastus caesius Mycoblastus sanguinarius f. sanguinarius Mycomicrothelia confusa Mycoporum antecellens Nephroma laevigatum (IR) Nephroma parile Normandina pulchella Ochrolechia szatalaënsis (IR) Ochrolechia tartarea Pachyphiale carneola Pannaria conoplea (IR) Pannaria rubiginosa (IR) Parmelia omphalodes Parmeliella parvula (IR) Parmeliella testacea (IR) Parmeliella triptophylla (IR) Parmotrema crinitum Peltigera collina (IR) Peltigera horizontalis Peltigera hymenina Peltigera membranacea Pertusaria multipuncta Pertusaria opthalmiza (IR) Phaeographis smithii Phyllopsora rosei (IR) Platismatia glauca Porina borreri Porina coralloidea (IR) Porina hibernica (IR) Porina leptalea Protopannaria pezizoides Protoparmelia ochrococca Pseudocyphellaria crocata (IR) Pseudocyphellaria intricata (IR) Pseudocyphellaria norvegica (IR) Pyrenula laevigata (IR) Pyrenula macrospora Pyrenula occidentalis (IR) Ramalina calicaris Ropalospora viridis Schismatomma quercicola (IR) Sphaerophorus globosus Sticta canariensis (IR) Sticta fuliginosa (IR) Sticta limbata (IR) Sticta sylvatica (IR) Thelenella muscorum var. muscorum Thelotrema macrosporum (IR) Thelotrema petractoides (IR) Tomasellia gelatinosa Trapelia corticola Trapeliopsis pseudogranulosa Usnea cornuta

Index of Hygrothermy

Rainforest

>150

>100

n.s. n.s. n.s. Iv = 0.122,P = 0.0028 n.s. n.s. n.s. n.s. Iv = 0.484,P < 0.0001 Iv = 0.339,P < 0.0001 Iv = 0.365,P < 0.0001 n.s. n.s. n.s. n.s. n.s. n.s. Iv = 0.267,P < 0.0001 Iv = 0.338,P < 0.0001 Iv = 0.336,P = 0.0001 n.s. n.s. n.s. n.s. Iv = 0.502,P < 0.0001 n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Iv = 0.361,P < 0.0001 n.s. Iv = 0.251,P < 0.0001 n.s. n.s. n.s. n.s. n.s. Iv = 0.375,P < 0.0001 n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Iv = 0.422,P < 0.0001 Iv = 0.637,P < 0.0001 Iv = 0.602,P < 0.0001 Iv = 0.418,P < 0.0001 n.s. Iv = 0.287,P < 0.0001 n.s. Iv = 0.148,P = 0.0049 n.s. n.s. Iv = 0.231,P < 0.0001 n.s. n.s. n.s. n.s. Iv = 0.393,P < 0.0001 Iv = 0.488,P < 0.0001 n.s. n.s. n.s. n.s.

Iv = 0.51, P < 0.0001 Iv = 0.241,P < 0.0001 Iv = 0.485,P < 0.0001 Iv = 0.714,P < 0.0001 Iv = 0.182,P = 0.0063 Iv = 0.21, P < 0.0001 Iv = 0.381,P < 0.0001 Iv = 0.447,P < 0.0001 Iv = 0.882,P < 0.0001 Iv = 0.939,P < 0.0001 Iv = 0.727,P < 0.0001 Iv = 0.732,P < 0.0001 Iv = 0.637,P < 0.0001 Iv = 0.095,P = 0.0432 n.s. n.s. Iv = 0.398,P < 0.0001 Iv = 0.866,P < 0.0001 Iv = 0.85, P < 0.0001 Iv = 0.702,P < 0.0001 Iv = 0.281,P < 0.0001 Iv = 0.347,P < 0.0001 Iv = 0.515,P < 0.0001 Iv = 0.25, P < 0.0001 Iv = 0.926,P < 0.0001 Iv = 0.535,P < 0.0001 Iv = 0.405,P < 0.0001 Iv = 0.275,P < 0.0001 Iv = 0.288,P < 0.0001 Iv = 0.326,P < 0.0001 Iv = 0.577,P < 0.0001 Iv = 0.144,P = 0.0005 Iv = 0.485,P < 0.0001 Iv = 0.614,P < 0.0001 Iv = 0.65, P < 0.0001 Iv = 0.657,P < 0.0001 Iv = 0.795,P < 0.0001 Iv = 0.381,P < 0.0001 Iv = 0.761,P < 0.0001 Iv = 0.186,P < 0.0001 Iv = 0.28, P < 0.0001 Iv = 0.307,P < 0.0001 Iv = 0.326,P < 0.0001 Iv = 0.159,P < 0.0001 Iv = 0.854,P < 0.0001 Iv = 0.431,P < 0.0001 Iv = 0.6, P < 0.0001 n.s. Iv = 0.177,P < 0.0001 Iv = 0.665,P < 0.0001 Iv = 0.199,P < 0.0001 Iv = 0.067,P = 0.0385 Iv = 0.173,P = 0.0007 n.s. Iv = 0.864,P < 0.0001 Iv = 0.949,P < 0.0001 Iv = 0.962,P < 0.0001 Iv = 0.865,P < 0.0001 Iv = 0.631,P < 0.0001 Iv = 0.766,P < 0.0001 Iv = 0.341,P < 0.0001 Iv = 0.532,P < 0.0001 Iv = 0.362,P < 0.0001 Iv = 0.446,P < 0.0001 Iv = 0.887,P < 0.0001 Iv = 0.757,P < 0.0001 Iv = 0.585,P < 0.0001 Iv = 0.651,P < 0.0001 Iv = 0.312,P < 0.0001 Iv = 0.815,P < 0.0001 Iv = 0.315,P < 0.0001 Iv = 0.108,P = 0.0012 Iv = 0.481,P < 0.0001 Iv = 0.339,P < 0.0001 Iv = 0.291,P < 0.0001

Iv = 0.397, P < 0.0001 Iv = 0.082, P = 0.0058 Iv = 0.458, P < 0.0001 Iv = 0.542, P < 0.0001 Iv = 0.363, P < 0.0001 Iv = 0.123, P = 0.0017 Iv = 0.209, P < 0.0001 n.s. Iv = 0.778, P < 0.0001 Iv = 0.819, P < 0.0001 Iv = 0.738, P < 0.0001 Iv = 0.601, P < 0.0001 Iv = 0.584, P < 0.0001 n.s. Iv = 0.307, P < 0.0001 Iv = 0.165, P = 0.0001 Iv = 0.325, P < 0.0001 Iv = 0.851, P < 0.0001 Iv = 0.882, P < 0.0001 Iv = 0.683, P < 0.0001 Iv = 0.213, P < 0.0001 Iv = 0.157, P = 0.0002 Iv = 0.51, P < 0.0001 Iv = 0.416, P < 0.0001 Iv = 0.815, P < 0.0001 Iv = 0.371, P < 0.0001 Iv = 0.298, P < 0.0001 Iv = 0.271, P < 0.0001 n.s. Iv = 0.513, P < 0.0001 Iv = 0.667, P < 0.0001 n.s. Iv = 0.35, P < 0.0001 Iv = 0.561, P < 0.0001 Iv = 0.644, P < 0.0001 Iv = 0.532, P < 0.0001 Iv = 0.729, P < 0.0001 Iv = 0.293, P < 0.0001 Iv = 0.572, P < 0.0001 Iv = 0.177, P < 0.0001 n.s. Iv = 0.158, P < 0.0001 Iv = 0.183, P < 0.0001 n.s. Iv = 0.897, P < 0.0001 n.s. Iv = 0.213, P < 0.0001 Iv = 0.138, P = 0.0003 n.s. Iv = 0.234, P < 0.0001 n.s. n.s. Iv = 0.287, P < 0.0001 Iv = 0.35, P = 0.0001 Iv = 0.766, P < 0.0001 Iv = 0.892, P < 0.0001 Iv = 0.941, P < 0.0001 Iv = 0.754, P < 0.0001 n.s. Iv = 0.717, P < 0.0001 n.s. Iv = 0.465, P < 0.0001 n.s. Iv = 0.498, P < 0.0001 Iv = 0.578, P < 0.0001 Iv = 0.434, P < 0.0001 Iv = 0.358, P < 0.0001 Iv = 0.443, P < 0.0001 Iv = 0.147, P = 0.0003 Iv = 0.756, P < 0.0001 Iv = 0.891, P < 0.0001 n.s. Iv = 0.218, P < 0.0001 Iv = 0.217, P < 0.0001 n.s.

C.J. Ellis / Ecological Indicators 70 (2016) 125–133

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Table 1 (Continued) Taxon Name

Index of Hygrothermy

Rainforest

>150

>100

Usnea flammea Usnea florida Usnea fragilescens var. mollis Usnea rubicunda

Iv = 0.205,P < 0.0001 n.s. Iv = 0.209,P < 0.0001 n.s.

Iv = 0.779,P < 0.0001 Iv = 0.134,P = 0.0209 Iv = 0.737,P < 0.0001 Iv = 0.522,P < 0.0001

Iv = 0.536, P < 0.0001 n.s. Iv = 0.689, P < 0.0001 n.s.

Fig. 3. Comparison of a site value for hygrothermy (≈oceanicity) with assemblage scores for A. Hyper-oceanic and B. Oceanic lichen epiphyte indicator species in Atlantic oakwood Special Areas of Conservation (SACs). Example positive and negative residuals are labelled for: i. Loch Etive Woodlands, ii. Tarbert Woodlands, iii. Blackmill Woodlands and iv. Naddle Forest SACs. C. The correlation between the oceanic indicator score and the respective score for temperate rainforest indicator species.

effect sizes), such as for Bacidia absistens, or Cladonia pyxidata. Thus, individual species should not be used as unique proxies for bioclimatic conditions, but the accumulation of species indicator scores is essential, with species that are characterised by the greatest contrasts within and outside a bioclimatic zone contributing most to an assemblage indicator score. Second, there were insightful differences between the species identified as indicators within the oceanic and temperate rainfor-

est bioclimatic zones. This related to the way in which the oceanic zone extended into the coastal lowlands of Wales and south-west England, so including species with tendencies towards warmer climatic conditions that were not temperate rainforest indicators, such as Heterodermia obscurata, Phaeographis smithii, and Usnea florida. In contrast, the temperate rainforest zone was less extensively coastal, and had a more restricted upland distribution in Wales and south-west England (matching more closely with Fig.

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C.J. Ellis / Ecological Indicators 70 (2016) 125–133

6 .1 in DellaSala et al., 2011). It also extended into humid but cooler conditions in the upland valleys of central and eastern Scotland, supported by indicators such as Hypocenomyce friesii, Imshaugia aleurites, and Protoparmelia ochrococca. This pattern observed for Britain is consistent with observations at a European scale. Temperate rainforest has been delimited biogeographically in the cooler montane regions of Europe, but not in the warmer climates of northern Spain or Portugal (Fig. 6.1 in DellaSala et al., 2011), while oceanic conditions (such as Köppen’s oceanic climate zone Cfb) do not occur within the continental European mountain ranges, but do extend southwards along the European Atlantic coastline to include parts of the Iberian Peninsula. Third, in terms of conservation status, 30 of the hyper-oceanic indicator species (c. 77% of indictors) are assigned international responsibility within UK conservation planning (Woods and Coppins, 2012), reflecting the limited extent of their suitable bioclimatic space globally, and its prevalence in Britain. In contrast, a lower 39% or 43% of oceanic or temperate rainforest indicator species have been assigned international responsibility, reflecting a more facultative association of this group with oceanic bioclimatic conditions, and the potential for their occurrence in relatively more continental regions of Europe. Nevertheless, these values compare to an overall figure of c. 8% of British lichens with assigned international responsibility status, highlighting the contribution made to UK biodiversity conservation by oceanic and rainforest associated epiphytes. In practical application, the attribution of hyper-oceanic and oceanic indicators to example sites classified as old sessile oakwoods with Ilex and Blechnum in the British Isles (Annex 1 Habitat Code 91A0) and representing Atlantic oakwoods (Baarda, 2005), showed an effective relationship with hygrothermy. The lichen records used for this analysis represent species lists associated with, or in the vicinity of the woodland site. Thus, they are only a guide to assemblage structure, and they also provide no information on the abundance or viability of the recorded species over the long-term. Allowing for this form of data aggregation, the scatter around the regression line helps to identify richer than expected sites with positive residuals, such as for the Loch Etive and Tarbert woodland systems (Fig. 3). These sites might be expected to have relatively unpolluted air, ecological continuity and/or high environmental heterogeneity, creating a wide range of microclimatic conditions that exceeds an estimate of assemblage structure at the macroclimatic scale. There are also sites with negative residuals, and which therefore have a poor representation of indicator species despite having a suitable climate (Fig. 3). These speciespoor examples include Blackmill Woodlands (South Wales) and Naddle Forest (northern England), where the climate is strongly oceanic, but with epiphyte assemblages that are likely to have been affected by historic pollution and landscape-scale forest loss. The indicator sets can therefore be applied within the course of lichen inventory, to understand how a site performs in its representation of hyper-oceanic, oceanic or temperate rainforest species, relative to an expectation for the site based on its climatic setting. Sites scoring higher than expected on average might be priorities for protection, while sites scoring lower become priorities for ecological restoration. Finally, the recognition of a British rainforest system has been characterised in this study climatically and using a suite of lichen epiphytic indicator species which can be applied in site assessments for biodiversity and habitat quality. High scoring and low scoring sites – relative to their climatic setting – will reflect good and poor ecological status for British temperate rainforest, respectively. In terms of their structure, these rainforests are characterised by tree species such as ash (Fraxinus excelsior), hazel (Corylus avellana), sessile oak (Quercus petraea), and Scots pine (Pinus sylvestris), with tree-growth in oceanic western Britain often stunted by relatively

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