Snapshot isolation and isolation history challenge the analogy between mountains and islands used to understand endemism

Global Ecol Biogeogr. 2020;00:1–23. | 1wileyonlinelibrary.com/journal/geb Received: 3 October 2019 | Revised: 11 June 2020 | Accepted: 16 June 2020 DOI: 10.1111/geb.13155 RESEARCH REVIEW Snapshot isolation and isolation history challenge the analogy between mountains and islands used to understand endemism Suzette G. A. Flantua1,2 | Davnah Payne3 | Michael K. Borregaard4 | Carl Beierkuhnlein5,6,7 | Manuel J. Steinbauer6,8 | Stefan Dullinger9 | Franz Essl9 | Severin D. H. Irl10 | David Kienle5 | Holger Kreft11 | Bernd Lenzner9 | Sietze J. Norder2,12 | Kenneth F. Rijsdijk2 | Sabine B. Rumpf9,13 | Patrick Weigelt11 | Richard Field14 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd 1Department of Biological Sciences, University of Bergen, Bergen, Norway 2Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, the Netherlands 3Global Mountain Biodiversity Assessment, Institute of Plant Sciences, University of Bern, Bern, Switzerland 4Center for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark 5Department of Biogeography, University of Bayreuth, Bayreuth, Germany 6Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany 7Institute of Geography (GIB), University of Bayreuth, Bayreuth, Germany 8Sport Ecology, University of Bayreuth, Bayreuth, Germany 9Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria 10Biogeography and Biodiversity Lab, Institute of Physical Geography, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 11Biodiversity, Macroecology and Biogeography, University of Göttingen, Göttingen, Germany 12Faculdade de Ciências, Centre for Ecology, Evolution and Environmental Changes (cE3c)/Azorean Biodiversity Group, Universidade de Lisboa, Lisboa, Portugal 13Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland Abstract Aim: Mountains and islands are both well known for their high endemism. To explain this similarity, parallels have been drawn between the insularity of “true islands” (land surrounded by water) and the isolation of habitats within mountains (so-called “mountain islands”). However, parallels rarely go much beyond the observation that mountaintops are isolated from one another, as are true islands. Here, we challenge the analogy between mountains and true islands by re-evaluating the literature, focusing on isolation (the prime mechanism underlying species endemism by restricting gene flow) from a dynamic perspective over space and time. Framework: We base our conceptualization of “isolation” on the arguments that no biological system is completely isolated; instead, isolation has multiple spatial and temporal dimensions relating to biological and environmental processes. We distinguish four key dimensions of isolation: (a) environmental difference from surroundings; (b) geographical distance to equivalent environment [points (a) and (b) are combined as “snapshot isolation”]; (c) continuity of isolation in space and time; and (d) total time over which isolation has been present [points (c) and (d) are combined as “isolation history”]. We evaluate the importance of each dimension in different types of mountains and true islands, demonstrating that substantial differences exist in the nature of isolation between and within each type. In particular, different types differ in their initial isolation and in the dynamic trajectories they follow, with distinct phases of varying isolation that interact with species traits over time to form presentday patterns of endemism. Conclusions: Our spatio-temporal definition of isolation suggests that the analogy between true islands and mountain islands masks important variation of isolation over long time-scales. Our understanding of endemism in isolated systems can be greatly enriched if the dynamic spatio-temporal dimensions of isolation enter models 2 | FLANTUA eT AL. Every continent, every country, and every island on the globe, offer similar problems of greater or less complexity and interest, and the time has now arrived when their solution can be attempted with some prospect of success. Many years of study of this class of subjects has convinced me that there is no short and easy method of dealing with them; because they are, in their very nature, the visible outcome and residual product of the whole past history of the earth. (Wallace, 1880) 1 | INTRODUCTION Mountains are known for hosting about half of the biodiversity hotspots of the world (Barthlott, Rafiqpoor, Kier, & Kreft, 2005; Hoorn, Perrigo, & Antonelli, 2018; Myers, 1988; Orme et al., 2005), for their high levels of endemism (Hughes & Eastwood, 2006; Körner, 2004) and for their iconic radiations (Hughes & Atchison, 2015; Nürk et al., 2020). To explain the high concentrations of endemic species in mountain areas, parallels have long been drawn between “mountain islands” (see Glossary), which are surrounded by land, and “true islands”, defined here as islands surrounded by (oceanic) water bodies. In fact, elevation-driven isolation and consequent endemism is a common situation for many mountain species, because many taxonomic groups show maximum species richness (Heaney et al., 2016; McCain, 2005, 2009; McCain & Grytnes, 2010) and higher rates of endemism at higher elevations (Steinbauer et al., 2016). Analogies between mountain islands and true islands typically invoke high levels of isolation (e.g., Särkinen, Pennington, Lavin, Simon, & Hughes, 2012), high levels of endemism (e.g., Nogué, Rull, & Vegas-Vilarrúbia, 2013), legacy effects of past surface areas during climate fluctuations (e.g., Van der Hammen, 1974; Simpson, 1974), geophysical dynamism (Ali, 2017; Antonelli et al., 2018) and high frequencies of in situ speciation (Hughes & Eastwood, 2006; Nürk et al., 2020). Besides these commonly quoted parallels, few studies directly compare the drivers of endemism (Box 1) in mountain islands and true islands (but see Itescu, 2019; Steinbauer et al., 2016). Accordingly, comparisons of their intrinsic characteristics, including their geological ontogeny, life span, isolation characteristics and isolation history, and of the contribution of these characteristics to contemporary patterns of endemism, are uncommon. Here, we revisit the concept of isolation and its link with endemism by focusing on, and questioning, the postulate (and common assumption) that mountain islands and true islands are analogous systems. In comparing these two systems, we clarify what can be learned about islands as drivers of endemism. For convenience, we use the term “island” to refer to both mountain islands and true islands. 2 | ISOLATION AS A STATE AND A PROCESS “Isolation” is defined in common English as “the process or fact of isolating or being isolated”, highlighting the ambiguity with respect to being a state or a process. What “being isolated” means is often biased by what humans intuitively perceive as isolated (“habitat bias”; Wiens, 1995), and this is reflected in the measures to quantify isolation (Box 2). An example is the Euclidean distance or Haversine distance between islands, which is easy to quantify and conceptualize, but may neglect ecological and evolutionary dimensions of isolation, 14School of Geography, University of Nottingham, Nottingham, UK Correspondence Suzette G. A. Flantua, Department of Biological Sciences, Postbox 7803, 5020 Bergen, University of Bergen, Norway. Email: s.g.[email protected] Funding information Marie Sklodowska-Curie actions, Grant/ Award Number: 707968; Austrian Science Foundation FWF, Grant/Award Number: I 3757-B29; European Union’s Horizon 2020, Grant/Award Number: 641762; Netherlands Organization for Scientific Research, Grant/Award Number: 2012/13248/ ALW; Global Mountain Biodiversity Assessment; Fundação para a Ciência e a Tecnologia, Grant/Award Number: UID/ BIA/00329/2013 and PD/BD/114380/ 2016; ERC Advanced Grant, Grant/Award Number: 741413 Humans on Planet Earth (HOPE); Danmarks Grundforskningsfond, Grant/Award Number: DNRF96 Editor: David Storch as explanatory variables and if these models account for the trajectories of the history of a system. KEYWORDS endemic species, flickering connectivity system, geological ontogeny, glacial–interglacial fluctuations, island biogeography, isolation, mountain islands, palaeoclimate, past connectivity, sky islands | 3 FLANTUA eT AL. such as intermittent gene flow (for a review of isolation indices, see Itescu, Foufopoulos, Pafilis, & Meiri, 2020). Here, we advocate for a more sophisticated biogeographical conceptualization of “isolation” based on the arguments that: (a) no biological system is “isolated” in an absolute sense (Taylor, Fahrig, & With, 2006); and (b) isolation has multiple spatial and temporal dimensions that relate to isolating biological and environmental processes (Gillespie, Lim, & Rominger, 2020). The effect of isolation on endemism results from multiple ecological and evolutionary processes of different intensities (Figure 1). For instance, higher levels of isolation (Figure 1, right side) are reflected in reduced levels of gene flow, resulting in the potential for allopatric speciation and genetic drift (Gillespie et al., 2012; Heaney, 2000). Isolation changes over time, modulated by changing environments, direction, continuity and intensity of vectors (wind, ocean currents and human transport) and by species traits (Gillespie & Roderick, 2002; Gillespie et al., 2020; Pepke, Irestedt, Fjeldså, Rahbek, & Jønsson, 2019; Steinbauer, 2017). This means that through time, an island experiences different levels of isolation (Figure 1, top) and, as a result of the different processes at play (Figure 1, centre), present-day patterns of endemism carry a mix of the legacies from these processes (Figure 1, bottom). Accordingly, we define isolation of an island (i.e., island-like entity) as “a continuum of processes whose strengths vary in space and time, modulated by species traits and by environmental and geological conditions that influence the (spatial) characteristics of the island and, as a result, change the degree of gene flow”. Based on this definition, a change in isolation represents a change in how influential processes that lead to reduced (e.g., cladogenetic/allopatric speciation, genetic drift) versus increased gene flow (e.g., “dispersification”, Glossary; Moore & Donoghue, 2007; hybridization after secondary contact: Grant, 2014; Petit et al., 2003) are for the ecological and evolutionary pool of a focal species assemblage or, in this case, the percentage endemism (Figure 1). In our theoretical framework, “isolation” is always defined from the perspective of focal taxa or assemblages (Gillespie & Roderick, 2002; Wiens, 1995), which is also the case for endemism, and is best viewed as encompassing both patterns and processes. With this definition, we also embrace the complexity of patterns and processes as quantified by landscape “connectivity” in terrestrial systems (Box 2), where “isolation” is only one of several variables to quantify the spatial composition and arrangement of patches. Building upon our redefinition of isolation, we develop a conceptual framework for mountain islands and true islands that takes into account the degree of isolation at a certain moment in time (i.e., “contemporary”), differences in isolation between species groups, and dynamic changes of isolation over time (Figure 2). The framework allows testing how these variables jointly contribute to contemporary patterns of endemism. We start by discussing the main dimensions that influence what we call “snapshot isolation”, which is the degree of isolation of mountain islands and true islands at any point in time (Figure 2a). We then address “isolation continuity”, which considers the past dynamics of isolation (Figure 2b), and the record of past BOX 1 Identifying and measuring endemism There is a key distinction between endemism (see Glossary) as the proportion of species that are endemic (here “percentage endemism”) and endemism as the number of species that are endemic (here “endemic species richness”). Herein, we focus primarily on percentage endemism. Endemism occurs at various spatial scales, from large (e.g., continents) to small (e.g., islands or mountain tops), and at different taxonomic levels, mostly from families to (sub-) species (Morrone, 2008). Accordingly, the spatial delimitation (size and shape) of an area over which to estimate endemism can be contentious but is a prerequisite for defining endemism (Crisp, Laffan, Linder, & Monro, 2001; Daru, Farooq, Antonelli, & Faurby, 2020; Guerin, Ruokolainen, & Lowe, 2015). Two main approaches exist in the literature to identify endemism spatially: one uses geographical units as reference entities, the other a gradual range size-based approach. The first approach is binary and defines whether a species occurs only within a given entity or not (e.g., a single island, archipelago, mountain range or country) and is, therefore, often evolutionarily meaningless. According to this definition, endemism can be nested, that is, a single-island endemic is, by definition, also an archipelago endemic. In contrast, the second approach is continuous; the smaller a species' range size, the higher is its level of endemism. The sum of “endemism values” of all species in a given area results in its overall level of endemism and can be related to the geographical extent of the area (i.e., endemics–area relationships). On a temporal scale, endemics can be separated into two groups: “neoendemic” and “palaeoendemic” (Stebbins & Major, 1965). The former describes species formed by “recent” speciation (e.g., divergence and reproductive isolation, hybridization and polyploidy in plants) that failed to disperse out of the ancestral area (Laffan & Crisp, 2003; Morrone, 2008). Palaeoendemics are usually relict species whose ranges became spatially restricted over evolutionary time-scales (Gillespie, 2009; Mishler et al., 2014) but can also have persisted by dispersing between volcanic islands while they emerge and perish (FernándezPalacios et al., 2011). Empirically distinguishing between these alternatives is often difficult. As alternatives, various authors have proposed “phylogenetic endemism” (Mishler et al., 2014; Rosauer, Laffan, Crisp, Donnellan, & Cook, 2009) and “weighted endemism” (Crisp et al., 2001; Laffan & Crisp, 2003). Although different in their approaches to capture endemism, each endemism metric is inherently related and strongly influenced by the spatial extent at which it is studied (Daru et al., 2020). 4 | FLANTUA eT AL. isolation (“isolation history”), which combines isolation continuity with the overall duration of isolation (Figure 2c). Together, current isolation and isolation history mediate the dominant isolation-related processes driving endemism (Figure 2d). We specifically discuss how endemism depends on the continuity of isolation through time and argue that the degree and dynamics of isolation differ substantially among types of mountain islands and true island systems. 3 | SNAPSHOT ISOLATION Snapshot isolation is the degree of isolation of a location at a given point in time and consists of two main dimensions (Figure 2a): (a) the environmental difference of a location from its surroundings (“Differencesur”); and (b) the effective distance from an equivalent environment (“Distanceequiv-env”). Here, “equivalent” means that an environment is similar enough to be within the environmental tolerance of a focal organism. Both dimensions depend on the pre-adaptations of a species, such as its environmental niche (Janzen, 1967) and dispersal ability, which could potentially evolve at the focal location. Life-history strategies of evolving clades affect success rates for colonization of islands and island-like environments (e.g., Pepke et al., 2019). Thus, the isolation of a given location varies between organisms according to the breadth of their environmental tolerance, dispersal capacity and adaptations to use existing dispersal vectors to establish in new locations (Gillespie & Roderick, 2002; Gillespie et al., 2020; Steinbauer, 2017). 3.1 | Environmental difference from surroundings (Differencesur) This dimension is related to the concept of the inhospitable matrix and the patch–corridor–matrix contrast (Forman, 1995), but we question the notion of using a “habitat patch” to represent islands as units of analysis to understand species richness (also see Fahrig, 2013). Here, we assume that the difference in environmental conditions between a location and its surroundings, here termed Differencesur, is sufficient to impose ecophysiological constraints on a particular species' range, such as the prevention or the inhibition of gene flow through species dispersal and establishment. Differences in environmental conditions are easiest to identify when there is a sharp transition in space between two environments, such as between land and water at the coast of true islands. In such cases, the difference is so large for most organisms that isolation is often measured simply by the distance to another landmass (See section 3.2; Itescu et al., 2020). Although true islands are rather clear in their sharp transitions from land to water (although coastal and intertidal zones can form wide transitions), a gradient of transitions exists for islands in mountain environments, from abrupt to gradual. Sharp transitions are typically brought about by three phenomena: (a) strong abiotic environmental gradients, such as the temperature gradient along steep slopes, or sharp changes in bedrock or geomorphology; (b) forest BOX 2 Isolation, connectivity, connectedness and fragmentation The way that isolation in island biogeography has commonly been defined and used is a solely distance-based measure. “Decreased isolation”, meaning decreased distances between islands, is frequently equated with “increased connectivity” in the literature, suggesting a continuous gradient of isolation along which connectivity represents the other side of the same coin. This usage poorly represents the concept of “connectivity” as formalized originally in landscape ecology. Connectivity in a landscape as defined by Taylor et al. (1993; Glossary) was always intended to include both the physical structure and arrangement of patches and also the behaviour of organisms within the landscape in response to these physical characteristics and the surroundings. The former was described to be the “structural connectivity” (Glossary), often quantified by interpatch distances alone (e.g., straight-line distance, nearest-neighbour measures), but can also include the surface area of the patch, type of habitat and suitability of the patch for focal species [nicely summarized by the “intrapatch connectivity” within the concept of “habitat availability” or “reachability” by Pascual-Hortal & Saura (2006) and Saura & Pascual-Hortal (2007)]. “Connectedness” (Glossary) refers only to the degree of physical connection between patches. Isolation as usually defined in island biogeography is thus one aspect of structural connectivity. However, “connectivity” is not properly captured by an index of linear distances alone. The variability in the movement and behaviour of taxa (e.g., resulting from influences of dispersal capacities and directional dispersal vectors) is represented by “functional connectivity” (Glossary). In landscape ecology, the importance of an organism-centred approach to quantification of connectivity has been much emphasized (e.g., Pearson, Turner, Gardner, & O’Neill, 1996; Saura & Rubio, 2010; Taylor et al., 2006; Wiens, 1995); this is ignored when considering only structural connectivity. The functional connectivity explains why a given arrangement of patches/ islands can be perceived as being both connected and disconnected by two species with different dispersal capabilities and opportunities (Taylor et al., 2006). Thus, “connectivity” is an inherent description and integration of the landscape characteristics and the behaviour of taxa within this landscape (Tischendorf & Fahrig, 2000). Numerous connectivity indices have been developed and later on compared and reviewed by Tischendorf and Fahrig (2000) and by Saura and Pascual-Hortal (2007), who also | 5 FLANTUA eT AL. ecotones, such as the upper forest line (highest elevation of continuous forest), which results in abrupt changes in solar radiation and water availability, for example; and (c) boundaries of human land use. Examples of gradual transitions in “sky islands” (see Glossary), include the Madrean sky islands in North America, which feature gentle slopes and thus have blurred gradient boundaries. The table-top mountains in Venezuela, moreover, feature clear and sharp transitions. The effectiveness of environmental or land-use gradients in influencing the isolation of mountain systems varies between species. Although clearly bounded by a water body, true islands also feature a mix of transitions, because they are often environmentally heterogeneous (e.g., large spatial variety in soils, topography and microclimates). For true islands, this results in different degrees of snapshot isolation, both within islands and between islands within a (meta-)archipelago. The Hawaiian Islands, for instance, show a high environmental heterogeneity (Seijmonsbergen, Guldenaar, & Rijsdijk, 2018). Hawai'i is the youngest island of the archipelago (c. 0.6 Myr old) and features the highest volcanic peak on an island worldwide [Mauna Kea, 4,207 m above present sea level (a.s.l.)]. Some aspects of the abiotic diversity are low attributable to the relative youth of the mountain, whereas its elevation creates high variability in microclimatic zonation and orographic rainfall, producing a wide range of vegetation zones and, as such, represents a sky island within a true island (Steinbauer et al., 2016). In contrast, one of the oldest islands of the archipelago, Kauai (c. 5.3 Myr old; 1,598 m a.s.l.) displays high abiotic environmental variability and limited microclimatic zonation. Thus, although Differencesur can be characterized by a sharp boundary, this dimension is better regarded as a continuum ranging from abrupt (e.g., water and land at the coast of a true island) to gradual transitions (e.g., gentle slopes), or combinations of the two, and is applicable to a wider range of systems with island-like properties (Gillespie & Roderick, 2002). 3.2 | Effective distance from equivalent environment (Distanceequiv-env) The geographical distance between landmasses is often the only dimension of isolation accounted for in models of island biogeography and is traditionally measured as straight-line distances to other landmasses (Itescu et al., 2020; Whittaker & Fernandez-Palacios, 2007). In simulation models, this has proved valuable to test hypotheses on the influences of distance to the mainland and island size on proposed an approach that is potentially useful for comparing mountain islands and true islands (further details in Supporting Information Appendix S1). The concept of “fragmentation” (Glossary) has gone through a similar process of becoming increasingly diffuse and ambiguous in its usage since its original formulation [see reviews by Franklin et al. (2002) and Fahrig (2019)]. Often (mis)used in the literature as analogous to the opposite of “landscape connectivity”, originally it described only the breaking up of habitat that results in reduction of surface area, increase of patch numbers and increase of isolation, without accounting for the responses of organisms. BOX 2 (Continued) FIGURE 1 Isolation is a continuum of different processes that interact with species traits to result in particular levels of endemism. Darker/warmer and lighter/colder colours in the bars indicate high and low levels, respectively. The degree of isolation of an island or other insular system changes, often resulting in different processes influencing the species composition and thus the degree of endemism in an island. A lesser degree of isolation (left) is not a synonym for higher “connectivity” (see Box 2). Percentage endemism is the percentage of native species that are endemic. For definitions of the terms “endemism”, “taxon cycle” and “dispersification”, see Glossary 6 | FLANTUA eT AL. endemism (Rosindell & Phillimore, 2011). However, the effective isolation captured by measures of straight-line distances can vary between species and higher-level taxonomic groups (Gillespie & Roderick, 2002; Weigelt & Kreft, 2013). Defining isolation only by distances between landmasses ignores the role of the environmental tolerances of species or assumes that all landmasses are homogeneous. It also ignores differences between species in their ability to use existing dispersal vectors and the directionality of many vectors (a) (c) (d) (b) | 7 FLANTUA eT AL. (e.g., wind or water currents; biotic agents; Gillespie et al., 2012, 2020). Thus, we argue that Distanceequiv-env is more meaningful as a species-specific measure, which can differ between co-existing species (Steinbauer et al., 2016; Steinbauer, Irl, & Beierkuhnlein, 2013; Weigelt & Kreft, 2013). The equivalent environments may be within the same island, archipelago or mountain range, or beyond. Despite pronounced gradients, delimiting mountain islands and quantifying Distanceequiv-env can be challenging in the absence of clear boundaries between habitats that vary in suitability for focal species (Fahrig, 2013). For “alpine islands” (Glossary), the upper forest line might serve as a simplified equivalent to the coastline of true islands in defining relatively pronounced boundaries, making it possible to use connectivity metrics that require clearly delimited units of analysis (Supporting Information Appendix S1). However, such landscape ecological measures of “connectivity” (Box 2) are rarely used in marine archipelagos (but see Cabral, Weigelt, Kissling, & Kreft, 2014). Comparing connectivity (ideally from the perspective of a focal species) between archipelagos of mountain islands and true islands (Table 1) could help in estimation of the importance of the spatial organization of islands in shaping endemism, especially when integrated over time-scales as long as the Quaternary (section 4). Additionally, the use of directional network models that take into account island age (Carvalho, Cardoso, Rigal, Triantis, & Borges, 2015) and randomized simulations to test the effect of archipelago configuration on richness in “oceanic archipelagos” (Glossary; Jõks & Pärtel, 2019) can provide additional common ground to compare mountains and true islands. 4 | ISOLATION CONTINUITY The dimensions that define snapshot isolation are dynamic and change through time. Isolation continuity (Figure 2b) comprises two main components: (a) the temporal variability of snapshot isolation, and (b) the initial level of isolation when the island is formed. Isolation history (isolation continuity combined with the overall duration of isolation) is addressed in Section 5. 4.1 | Temporal variability of isolation Long-term environmental changes have influenced the distribution of species and evolutionary processes globally. True islands and mountains were formed and shaped by different geological processes that act at different temporal scales, affecting isolation through time. Generally, both mountains and islands composed of bedrock are essentially shaped by tectonic forces or volcanic activity, whereas islands composed of unconsolidated sediments (e.g., barrier islands) are formed by sedimentary processes (Ali, 2017; Molnar, 2018; see “Different types of mountains” in Supporting Information Appendix S2). Surface processes related to long-term erosion and Quaternary climate fluctuations have further reshaped the geomorphology of mountains (Antonelli et al., 2018 and references therein) and islands (e.g., Geirsdóttir, Miller, & Andrews, 2007) over time. Here, we focus on how the Quaternary (the last 2.58 Myr; Gibbard, Head, & Walker, 2010) influenced the temporal variability of isolation of islands and mountains over geologically recent times and how this variability influenced the distribution of biota and their evolution. Although during the last c. 2,500 years, global sea level has remained relatively constant (rate of change < 0.4 m/1,000 years; Lambeck, Rouby, Purcell, Sun, & Sambridge, 2014), fluctuating sea levels during most of the Quaternary (60–100 m; Figure 3a) caused substantial changes in island isolation (Figure 4; e.g., Ali & Aitchison, 2014; Fernández-Palacios et al., 2016; Norder et al., 2018, 2019; Rijsdijk et al., 2014; Weigelt, Steinbauer, Cabral, & Kreft, 2016). The effects of mid-to-late Quaternary sea-level stands on true islands in the Sunda Sea were depicted on maps as long ago as the first half of the 20th century (Dickerson, 1941; Molengraaff & Weber, 1919), showing massive increases in surface area as numerous islands connected (Woodruff, 2010). The Pleistocene Aggregate Island Complexes model (PAICs; Brown et al., 2013), Oscillating Geography Model (Ali & Aitchison, 2014) and Glacial-sensitive Model (Fernández-Palacios et al., 2016) all assess the influence of island isolation and “connectedness” (Glossary) by sea-level change on biota and evolutionary processes. The magnitude of change in isolation varies with the bathymetry of true islands and with archipelago configuration (Figure 4a; Table 2; Norder et al., 2018, 2019; Voris, 2001). Higher sea levels during interglacials (Figure 3a) caused many true islands to become smaller and more isolated, whereas during glacial periods they were larger and sometimes connected to other islands or continents (Figure 4a). Some archipelagos, such as the continental islands of the Seychelles (Figure 4a), the atolls of Phoenix and Aldabra, largely submerged for a short period in the last interglacial FIGURE 2 A framework for endemism in mountain islands and true islands, derived from bringing together key aspects of the overall isolation of these islands and its dynamics through time. This scheme highlights both similarities and differences between mountain islands and true islands and between different types of islands and mountains. We separate important dimensions of isolation, each of which is expected to affect the amount and nature of contemporary endemism at any given place. (a) Snapshot isolation is the degree of isolation at any given moment in time, depending on species traits. (b) Isolation continuity describes the temporal aspect of isolation in terms of its dynamics through time and the degree of isolation when the island arose. Note the reversed axis for temporal variability of isolation. (c) Isolation history considers the total duration of isolation (time) alongside isolation continuity. (d) These aspects of isolation history together shape current patterns of endemism, in conjunction with current levels of isolation of the island, which in most cases can be considered to represent the last c. 2,500 years (Lambeck et al., 2014). The considerable variation within the types of insular systems depicted is not shown; instead, each type is located according to what we suggest might be representative of that type overall and integrated across the full range of organisms. For definitions of the terms, including “oceanic islands”, “continental shelf islands” and “continental fragments”, see Glossary 8 | FLANTUA eT AL. (LIG, c. 129–116 ka; Felde et al., 2020; Norder et al., 2018). The consequences of sea-level changes on isolation were less drastic for remote “hotspot volcanic oceanic islands” (Glossary), such as Hawai'i, the Canary Islands, Azores and Galápagos, which mainly lost land but maintained much of their original geographical configurations. With lower sea levels, many “continental shelf islands” (Glossary) of the Sunda plain, Tasmania and the Aegean archipelago became connected to the mainland by land bridges or, as in the case of the Seychelles, to other islands, forming large microcontinental landmasses. For instance, the Cyclades islands in the Aegean Sea, currently comprising 44 islands of >1 km2 and totalling 3,250 km2, extended over 10,750 km2 during glacial times, mainly as a single landmass (Simaiakis et al., 2017). Numerous studies have explored the relationships between Quaternary sea-level fluctuations, speciation and endemism patterns on true islands (e.g., Ali & Aitchison, 2014; Fernández-Palacios et al., 2016; Heaney, 1985; Heaney, Walsh, & Peterson, 2005; Mayr, 1941; Norder et al., 2019; Papadopoulou & Knowles, 2017; Rijsdijk et al., 2014; Weigelt et al., 2016). Results suggest that changes in archipelago configurations related to past climatic conditions can be good predictors of present-day endemism patterns, although somewhat contradictory conclusions emphasize the need for further research. Weigelt et al. (2016) found, for instance, that the increased surface area of true islands during the Last Glacial Maximum (LGM) is important in explaining current endemism and proposed a negative relationship between past connectivity and the number and proportion of endemic species today, suggesting that past connections to neighbouring islands result in fewer single-island endemics. This, in turn, could be explained by intermixing of taxa during increased connectivity (Flantua & Hooghiemstra, 2018), although differential extinction might cause complex spatial patterns TABLE 1 Spatial configurations of mountain islands and true islands Type of archipelago Mountain island examples True island examples Stepping stone archipelago between two or more large surface areas Stepping stone archipelago between mountain ranges: Stepping stone archipelago between a large island and the mainland or between two large islands: Isolated massif with outliers • Madrean archipelago • Great Basin archipelago • Altai/Tien Shan Basin • Meso-American massifs • Archipelago Sea (Baltic Sea) • Lesser Sunda islands • Sulu archipelago • Kuril Islands (islands between Japan and Taiwan) • Lesser Antilles • Tuscany Archipelago • Mozambique Channel islands (Comoros Mayotte) • Indian Ocean islands between Madagascar and India Isolated massif with smaller outlier mountains: Balearic islands • Ethiopian highlands • East African arc • Saharan massifs • Atlas Mountains • Jabal Lubnan • Drakensberg • Central European massifs • Caucaso-Iranian massifs • New Caledonia • Madagascar with surrounding islands Linear chain of outlier islands at one end of a large island Cordillera with outliers of mountains islands at one end of a cordillera: Linear chain of outlier islands at one end of the mainland: • Malay peninsula • Baja California peninsula • Coastal Cordillera (South America) • Southern Andes • Aleutian and Bering Sea Islands • South Shetland Islands • Izu Islands • Solomon islands Isolated island chains and groups Completely isolated sky islands: Easter Islands • Western Ghats • Pantepuis (tabletop mountains) • Hawai'i • Galapagos • Seychelles • Canaries Type of archipelago Mountain island examples True island examples High-elevation (biotic) sky islandsa High-altitude flora in: High-altitude flora on: • The Northern Andes (páramos) • The Central Andes (punas) • Himalaya– Hengduan Mountains • Indonesian archipelago • The island of New Guinea • Malaysian portion of the island of Borneo Note: Here, we draw parallels between archipelago configurations as proposed by Warschall (1994; mountain island examples) and true islands. Further research could assess similarities and differences in patterns of endemism among and within each type of archipelago, and among and between mountain islands and true islands, also considering their isolation histories; the archipelago types proposed by Warschall represent only present-day snapshot isolation (Figures 3 and 4). aReferring here to high-elevation ecosystems/populations, not geological features. TABLE 1 (Continued) | 9 FLANTUA eT AL. in archipelago endemism (García-Verdugo et al., 2019). Similar results were found for continental shelf islands connected via land bridges to the mainland or forming large continental shelf islands (Sondaar & Van der Geer, 2005). Norder et al. (2019) showed that on oceanic islands current endemism is better explained by longterm spatial archipelago configurations that have been more common and persistent in the Quaternary (Figure 3, “Intermediate”, cool stadials and interstadials) than those configurations characteristic of the extreme warm interglacial (Figure 3, Interglacials) or extreme cold (Figure 3, Glacials) glacial maxima conditions. This work highlights the importance of extending beyond the LGM when quantifying the role of past isolation on species richness and endemism (Porter, 1989). Clearly, past surface area and inter-island connections (isolation history) played an important role in explaining present-day richness and genetic diversity patterns of endemics, whereas exclusively considering the current snapshot isolation state is insufficient to understand patterns of endemism. The effects of past climatic fluctuations on processes related to endemism have likewise been substantial on mountain islands (Table 1; e.g., Adams, 1985; Simpson, 1974; Sklenář & Balslev, 2005). Considerable range shifts during the Quaternary have been recorded in mountains around the world, with alpine species lowering and raising their distribution along elevational and latitudinal gradients in response to cooling and warming temperatures, respectively (e.g., Flantua & Hooghiemstra, 2018; Flantua, O'Dea, Onstein, Giraldo, & Hooghiemstra, 2019). In contrast to true islands, FIGURE 3 The variability of isolation for true islands and alpine islands is dictated by sea-level change and temperature change during the last c. 800 ka, respectively. (a) Changes in sea level as modelled by Norder et al. (2018) based on Bintanja, van de Wal, and Oerlemans (2005). The last interglacial (LIG, c. 129–116 ka; Felde et al., 2020) represents sea levels possibly equivalent to expectations if global temperatures rise by 1.5–2°C (2–6 m higher than present; Polyak et al., 2018). The LIG peak displayed deviates from that of Bintanja et al. (2005) because their averaged modelled values underestimated maximum sea-level rise. (b) The percentage of time over the last c. 800 kyr that sea levels were within each interval (10 m bins), recalculated from Norder et al. (2018). The intervals marked with asterisks correspond to the configurations displayed in Figure 4. (c) Temperature change (in degrees Celsius) estimates relative to the 1960–1990 average (set here at 0°C), based on EPICA Dome C Ice Core (Jouzel et al., 2007). “[×0.5]” refers to the calculated factor of polar temperature to global mean surface temperature. Adjusted from Fergus (2018). (d) The percentage of time over the last c. 800 kyr that temperatures were within each interval (0.5°C bins). The intervals marked with asterisks correspond to the configurations displayed in Figure 4 ED GF 16 | FLANTUA eT AL. habitat or region (Moorcroft, 2009: p. 445). (c) Species with small geographical ranges (Hughes, 2009: p. 482). The more range restricted a species is, that is, the smaller its range size or the smaller the reference area in which a species occurs (e.g., mountain range versus single mountain top or archipelago versus single island), the higher its endemicity, that is, the more “endemic” it is (Guerin & Lowe, 2015; Noroozi et al., 2018; Steinbauer et al., 2016; also see Box 1 and overview of definitions of endemic areas by Parenti & Ebach, 2009).; Endemic (species) richness, The number of species that are endemic in a given region (also see Box 1).; Fragmentation, (a) The breaking up of a habitat, ecosystem or type of land use into smaller parcels (Curtis, 1956; Forman, 1995; Moore, 1962; see reviews by Fahrig, 2003, 2019). The definition of habitat fragmentation implies four effects of the process of fragmentation on habitat pattern: (i) reduction in habitat amount, (ii) increase in number of habitat patches, (iii) decrease in sizes of habitat patches, and (iv) increase in isolation of patches (Fahrig, 2003). (b) The state of habitat fragmentation as discontinuity, resulting from a given set of mechanisms in the spatial distribution of resources and conditions present in an area at a given scale that affects occupancy, reproduction or survival in a particular species (Franklin, Noon, & George, 2002). (c) The process of habitat fragmentation as the set of mechanisms leading to that state of discontinuity (Franklin et al., 2002). For a full list of definitions of fragmentation, see Bogaert et al. (2011).; Habitat islands in mountains, Isolated patches of a certain habitat type within a mountain. Often found in island-like distributions with highly variable distances, for example, seasonally dry forests in the Andes (Särkinen et al., 2012). Including but not restricted to alpine islands.; Hotspot volcanic oceanic islands, Islands initially formed on the ocean floor by hotspot activity and may follow a geo-ontogeny characterized by an emergent state which consists of: (a) a juvenile volcanic active growing state, (b) a mature volcanically inactive erosive state, and (c) a senile subsiding atoll to sea mount state. We can distinguish between volcanic oceanic islands that became connected during sea-level reductions or remained isolated. These islands are among the most isolated true islands on the planet and include Easter island, the Galapagos and Hawai'i.; Islands, (a) Areas of land surrounded by water (sensu stricto; i.e., true islands). (b) Landmass isolated in geographical and environmental space (sensu lato). Different types of true islands (Figure 2) are as follows: (a) oceanic islands, such as the Hawaiian Islands, the Canary Islands, Sulawesi, Luzon and Mindanao; (b) continental fragments, such as Madagascar and New Zealand; and (c) continental shelf islands, such as the British Isles and the Bass Strait islands in Australia, and atolls, such as the Florida Keys.; Isolation of an island, A continuum of processes whose strengths vary in space and time, modulated by species traits and environmental and geological conditions that influence the (spatial) characteristics of the island and, as a result, change the degree of gene flow.; Isolation continuity, Considers the historical dynamics of isolation of an island/archipelago in terms of: (a) the temporal variability of “snapshot isolation”, and (b) the initial level of isolation when the island is formed.; Isolation history, Considers the historical dynamics of isolation of an island/archipelago in terms of: (a) “isolation continuity”, and (b) the overall duration of isolation.; Mountain islands, Mountains or biomes within mountains (or mountain ranges) in which the geological features, species composition, habitat and ecosystem are distinct from the surrounding landscape, often characterized by sharp gradients that accentuate the “island” boundaries. Used in this paper as a general term to describe “alpine islands”, “habitat islands in mountains” and “sky islands”.; Mountain island archipelagos, Biogeographical coherence of an assemblage of mountain islands resembling limited species dispersion and in situ evolutionary processes seen in true island archipelagos.; Oceanic islands/ archipelagos, (Clusters of) islands located on oceanic crust, either at plate boundaries near subduction zones (arc islands) or those which were formed by hotspot volcanism (see hotspot volcanic oceanic islands). The geodynamics of islands are highly complex, and more detailed geological classifications are provided by Ali (2017) and Nunn, Kumar, Eliot, and McLean (2016).; Patch, (a) A relatively homogeneous area within a landscape that differs markedly from its surroundings (Fischer, Lindenmayer, & Hobbs, 2009: p. 431). (b) A discrete, bounded area of any spatial scale that differs from its surroundings in its biotic and abiotic structure and composition (Peters, Gosz, & Collins, 2009: p. 458).; Percentage endemism, The proportion of species that are endemic. At large scales, percentage endemism can reflect speciation (Steinbauer et al., 2016), whereas at smaller scales (e.g., on the plot scale) percentage endemism describes compositional uniqueness (e.g., Gillespie, Claridge, & Roderick, 2008; Irl et al., 2015; see Box 2.; Sky islands, (a) Mountain islands in a “desert sea” with limited genetic exchange between them (Dodge, 1943). (b) Continental landforms characterized by a substantially different climate, vegetation and species composition that are as different from their surroundings as if they rose from some remote sea (Heald, 1951, 1967). (c) Geological features with a species composition or ecosystem distinct from the surrounding landscape, often with steep gradients that accentuate the “island” boundaries, for example, table-top mountains in Venezuela and Colombia (Rull, 2010) and the Madrean archipelago (see Table 1). More recently, the term has been expanded also to describe mountain top islands, such as the high-elevation páramos of the Northern Andes (e.g., Diazgranados & Barber, 2017) and the Hengduan sky islands (e.g., He & Jiang, 2014). As such, true islands can also include sky islands with numerous endemics, for example, Sulawesi and Luzon.; Snapshot isolation, The degree of isolation of mountain islands and true islands at a point in time. The degree of isolation of mountain islands and true islands at a point in time.; Taxon cycle, (a) Temporal sequence of geographical distribution of species from (i) colonizing through (ii) differentiating and (iii) fragmenting to (iv) specializing (Gillespie, 2009: p. 144). (b) Taxon cycles are sequential phases of expansion and contraction of the ranges of species, usually associated with shifts in ecological distribution and adaptations to changing ecological relationships through the cycle (Ricklefs & Bermingham, 2002, citing Wilson, 1959, 1961).; Vicariant | 17 FLANTUA eT AL. speciation, A mode of allopatric speciation that involves a physical barrier, such as an ocean channel or mountain range, that subdivides a range and prevents gene flow between the two resulting populations (Phillimore, 2013).. AUTHOR CONTRIBUTIONS Initial ideas for this paper were developed at the Macroecology meeting in Vienna (2017) by S.G.A.F., D.P., M.K.B., M.J.S., S.D., F.E., S.D.H.I., D.K., H.K., B.L., S.B.R. and P.W., with further important contributions by R.F., C.B., S.J.N. and K.F.R. at a later stage. The conceptualization (writing and reviewing) was led by S.G.A.F., R.F. and D.P., and all authors contributed to the draft editing. Visualizations were drafted by S.G.A.F., M.J.S., R.F., K.R. and S.J.N. and commented on by all co-authors. ORCID Suzette G. A. Flantua https://orcid.org/0000-0001-6526-3037 Davnah Payne https://orcid.org/0000-0001-9170-7834 Michael K. Borregaard https://orcid.org/0000-0002-8146-8435 Carl Beierkuhnlein https://orcid.org/0000-0002-6456-4628 Manuel J. Steinbauer https://orcid.org/0000-0002-7142-9272 Stefan Dullinger https://orcid.org/0000-0003-3919-0887 Franz Essl https://orcid.org/0000-0001-8253-2112 Severin D. H. 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Biodiversity and Conservation, 19, 919–941. https://doi. org/10.1007/s1053 1-010-9783-3 | 23 FLANTUA eT AL. BIOSKETCH SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section. How to cite this article: Flantua SGA, Payne D, Borregaard MK, et al. Snapshot isolation and isolation history challenge the analogy between mountains and islands used to understand endemism. Global Ecol Biogeogr. 2020;00:1–23. https://doi.org/10.1111/geb.13155 Suzette G. A. Flantua has a background in palaeoecology, biogeography, landscape ecology and spatial analyses and enjoys integrating them all to gain new insights into the drivers of mountain biodiversity. Davnah Payne is an evolutionary biologist with a wide interest in mountain conservation and biodiversity. She serves as executive director of the Global Mountain Biodiversity Assessment. Richard Field's main interests are macroecology, biogeography, islands, geodiversity and plant ecology, with particular focus on biodiversity and the forces that structure ecological communities.