Patrones de diversidad alfa, beta y gama en sistemas insulares
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(2) PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE Facultad de Ciencias Biológicas Programa de Doctorado en Ciencias Biológicas Mención en Ecología. PATRONES DE DIVERSIDAD ALFA, BETA Y GAMMA EN SISTEMAS INSULARES.. Tesis entregada a la Pontificia Universidad Católica de Chile en cumplimiento parcial de los requisitos para optar al Grado de Doctor en Ciencias con mención en Ecología. Por. LEONOR ADRIANA VALENZUELA OSPINA Director de tesis Dr. Pablo A. Marquet. Enero, 2014 Santiago, Chile.
(3) “…a la luz de la teoría de la evolución, son las semejanzas y no las diversidades en estas lejanas islas las que son más difíciles de explicar… no podemos llegar a ninguna conclusión confiable en cuanto a cómo el estado actual del mundo orgánico se produjo, hasta que hayamos comprobado con cierta exactitud las leyes generales de la distribución de los seres vivos sobre la superficie de la Tierra” Alfred Russel Wallace. “Las islas y archipiélagos son, en muchos aspectos, microcosmos del resto del mundo”. Jonathan Losos y Robert Ricklefs. i.
(4) AGRADECIMIENTOS. Doy las gracias a mi tutor Pablo Marquet por su estímulo, confianza y su ayuda en el desarrollo de las ideas y los métodos que subyacen a esta tesis. En especial estoy profundamente agradecida con él por compartir conmigo su forma única de ver el mundo, a través de la cual una compleja maraña de ideas y datos se convierten en una explicación clara y novedosa. De igual manera agradezco a los miembros del comité por sus aportes y observaciones tanto en el proyecto que antecedió a este trabajo como en el documento final del mismo. Mi sincero agradecimiento a mis compañeros y profesores del doctorado por proporcionarme un entorno estimulante en el que aprender y en especial a Mauricio Lima por su apoyo en todos los aspectos que implica estar en un doctorado y a Andrés Parada y Tamara Catalán por los muchos debates estimulantes y también por el compañerismo y la risa que me han sostenido durante mis estudios de postgrado. Gracias a todos los compañeros del laboratorio que han hecho más ameno e interesante todo este proceso y siempre han estado dispuestos a colaborarme, en especial Pamela Martínez, Guillermo Espinoza, Juan Manuel Barreneche y Sebastián Abades ya que sin su ayuda parte de esta tesis no habría sido posible. A mis amigos, Gabriela Flores, Paula Giraldo, Robert Márquez, Gabriel Castaño, Renzo Vargas, Alejandra Troncoso, Andrea Najera y Jimena Guerrero mil gracias por su compañía y amistad. Quiero agradecer a muchas personas que sin conocerlas han hecho posible la realización de esta tesis, ya que su arduo trabajo ha hecho accesible al público un gran conjunto de datos sin los cuales esta tesis no se habría podido realizar. Doy las gracias al Programa de becas VRI de la Universidad Católica, la beca Conicyt para estudiantes latinoamericanos y la beca de término del Instituto de Ecología y Biodiversidad IEB por el apoyo financiero que me permitió desarrollar y completar mis estudios. Mi familia, especialmente mis padres Mercy Ospina y Jaime Valenzuela, me han brindado un apoyo incondicional durante mis estudios de posgrado, como lo han hecho en toda mi vida y por eso estoy muy agradecida con ellos. Por último, no podía tener completa esta tesis sin el apoyo incondicional y fomento de Daniel Osorio, que ha estado siempre ahí para mí y en ningún momento dudó de que pudiera hacerlo. ii.
(5) TABLA DE CONTENIDO RESUMEN GENERAL.............................................................................................................1 INTRODUCCIÓN .....................................................................................................................3 CAPÍTULO 1 .............................................................................................................................6 ON THE DETERMINANTS OF ALPHA AND BETA DIVERSITY IN INSULAR BIOTAS ..................................................................................................................................... 6 Abstract ....................................................................................................................................... 6 INTRODUCTION ...................................................................................................................... 7 METHODS ............................................................................................................................... 10 Data ....................................................................................................................................... 10 Statistical Analysis ................................................................................................................ 12 RESULTS ................................................................................................................................. 14 DISCUSSION ........................................................................................................................... 16 REFERENCES ......................................................................................................................... 20 Table Legends ........................................................................................................................... 27 Figure Legends ......................................................................................................................... 27 Supplementary material ............................................................................................................ 28 CAPÍTULO 2 ...........................................................................................................................47 SPECIES DIVERSITY UNDER A NEUTRAL COLONIZATION RULE.......................47 Abstract ..................................................................................................................................... 47 INTRODUCTION .................................................................................................................... 48 METHODS ............................................................................................................................... 49 Model .................................................................................................................................... 49 Previous work ................................................................................................................... 49 Extensions ......................................................................................................................... 51 Simulations and Model performance ................................................................................ 54 Neutral diversity in real archipelagos ............................................................................... 55 iii.
(6) RESULTS ................................................................................................................................. 56 Model Performance............................................................................................................... 56 Neutral diversity in real archipelagoes ................................................................................. 57 DISCUSSION ........................................................................................................................... 58 REFERENCES ......................................................................................................................... 61 Figure Legends ......................................................................................................................... 64 CAPÍTULO 3 ...........................................................................................................................73 THE ROLE OF SPATIAL CONFIGURATION, HETEROGENEITY AND SPECIES POOL ON SPECIES RICHNESS AND PHYLOGENETIC DIVERSITY OF INSULAR MAMMALS ............................................................................................................................ 73 Abstract ..................................................................................................................................... 73 INTRODUCTION .................................................................................................................... 74 Determinants of species richness and phylogenetic diversity .............................................. 76 Partitioning taxonomic and phylogenetic diversity in alpha and beta components .............. 78 METHODS ............................................................................................................................... 79 Data ....................................................................................................................................... 79 Statistical Analysis ................................................................................................................ 82 Bivariate analysis .............................................................................................................. 82 Structural equation model ................................................................................................. 82 RESULTS ................................................................................................................................. 84 Determinants of species richness and phylogenetic diversity .............................................. 84 Partitioning taxonomic and phylogenetic diversity in alpha and beta components .............. 85 DISCUSSION ........................................................................................................................... 86 REFERENCES ......................................................................................................................... 90 Figure Legends ......................................................................................................................... 96 Table Legends ........................................................................................................................... 97 Supplementary material ............................................................................................................ 97 CONCLUSIONES GENERALES ........................................................................................107 LITERATURA CITADA ......................................................................................................111. iv.
(7) RESUMEN GENERAL. La ecología de comunidades y la biogeografía buscan entender los procesos que determinan los patrones en la naturaleza, pero generalmente a distintas escalas espaciales y enfatizando diferentes procesos. La ecología de comunidades por lo general se ha centrado en los efectos de los procesos de selección a pequeña escala, mientras que la biogeografía se ha enfocado en los efectos de la dispersión y especiación a gran escala, aunque recientemente han convergido a una escala regional, donde se tienen en cuenta cuatro procesos generales: selección, deriva ecológica, dispersión y especiación. En este sentido, descomponer la diversidad regional (diversidad γ) en sus componentes α, que representa la diversidad local y β, que da cuenta de la variación en la composición de especies, ayuda a entender los vínculos entre las diferentes escalas espaciales y como esta influye en los patrones de diversidad. Los archipiélagos son sistemas relativamente bien estudiados para los cuales existe una considerable base empírica y teórica, están compuestos por unidades discretas (islas), cuantificables, numerosas y variables en tamaño, forma y distancia, lo cual los hace un sistema idóneo para entender cómo interactúan diferentes factores tales como: el tamaño, la heterogeneidad, la configuración espacial, el aislamiento y el pool continental, que dan cuenta de los cuatro procesos ya mencionados. En esta tesis se estudiaron las causas de los patrones de diversidad alfa, beta y gamma en sistemas insulares a dos escalas, intra archipiélagos, es decir entre islas de un mismo archipiélago (capitulo 1) e inter archipiélagos, es decir entre archipiélagos (capitulo 2 y 3). Para esto utilizamos la diversidad de mamíferos terrestres no voladores de 21 archipiélagos (155 islas) ubicados alrededor del mundo. Nuestro estudio muestra que esta nueva aproximación puede ayudar a entender mejor los procesos detrás de las relaciones entre diferentes factores y los componentes de la diversidad. A menor escala, es decir entre islas de un mismo archipiélago (capitulo 1), los factores relacionados con los procesos de selección, como el área, la diversidad de hábitats y el tamaño corporal, tienen un mayor efecto que los relacionados con dispersión (distancia al continente y distancia entre islas), mientras que a mayor escala (entre archipiélagos, capitulo 3), la dispersión cobra mayor relevancia y los patrones de diversidad pueden ser explicados 1.
(8) por los efectos conjuntos de los factores relacionados con procesos de dispersión entre las islas, que dependen de la configuración espacial y procesos de selección, relacionados con la heterogeneidad del sistema. A nivel inter-archipiélagos, bajo el modelo de colonización neutral (capítulo 2), los patrones de diversidad se vieron afectados por la tasa de migración, el tamaño y topología de los archipiélagos, lo cual nos indica que los procesos de deriva ecológica pueden generar diferentes patrones a través de los efectos de estos factores. Sin embargo, los resultados indican que los procesos no-neutrales disminuyen la riqueza de especies presentes en una metacomunidad y aumentan la diferenciación entre las comunidades locales (diversidad β, capitulo 3).. 2.
(9) INTRODUCCIÓN. Desde los inicios de la ecología, han existido dos perspectivas que se han considerado diametralmente opuestas, sobre cómo se estructuran las comunidades ecológicas: la teoría de nicho y la teoría neutral. Esta aparente contradicción es lo que se conoce como la paradoja de MacArhur (Schoener, 1983; Loreau & Mouquet, 1999), ya que su trabajo se centro tanto en el concepto de nicho para explicar la diversidad a escala local (Macarthur et al., 1967) como en la teoría de biogeografía de islas (MacArthur & Wilson, 1967). Sin embargo, al igual que como lo considero el propio MacArthur en algunos de sus trabajos (MacArthur & Levins, 1964; Horn & Arthur, 1972) la teoría de metacomunidades considera que la paradoja de MacArhur más que representar una dicotomía ilustra diferentes extremos de un continuo (Chase & Bengtsson, 2010), los cuales representan dos partes complementarias de una visión más amplia que examina como el determinismo (e.g interacciones entre las especies y el ambiente) y los procesos estocásticos (e.g dispersión) interactúan para dar cuenta de la estructura metacomunitaria (Chase, 2007). Dentro de los procesos deterministas se encuentran los procesos de selección asociados normalmente al nicho de las especies, mientras que entre los procesos estocásticos comúnmente se encuentran la deriva, la dispersión y la especiación, aunque estos dos últimos pueden tener un componente determinístico (Chase & Myers, 2011). Debido a que las dinámicas de la diversidad de especies a escala local y regional, no son independientes, descomponer la diversidad en sus componentes ayuda a entender los vínculos entre las diferentes escalas espaciales y como esta influye en los patrones de diversidad (Leibold et al., 2004). A escala local, la diversidad o riqueza de especies corresponde a la diversidad-α, la variación entre la composición de las especies entre una localidad y otra representa la diversidad-β, mientras que la diversidad regional o diversidad-γ se puede derivar de una partición multiplicativa (γ= α/β, Whittaker, 1972) o aditiva (γ= α + β, Lande, 1996). En términos generales existen cuatro procesos que pueden afectar de manera diferencial los componentes de la diversidad: 1) Procesos de selección, que son aquellos que favorecen diferentes especies en diferentes ambientes, 2) deriva ecológica o estocasticidad 3.
(10) demográfica, 3) limitación a la dispersión y 4) especiación (Vellend, 2010). En este sentido, es relevante entender el efecto y la interacción de factores que dan cuenta de estos cuatro procesos a diferentes escalas espaciales. La teoría de biogeografía de islas (MacArthur & Wilson, 1963, 1967) enfatiza la importancia del área, el aislamiento y el pool de especies como factores determinantes de la riqueza de especies a través de procesos de colonización (dispersión) y extinción estocástica (deriva). La teoría de nicho, enfatiza la importancia del área, la heterogeneidad ambiental, la energía disponible y rasgos de las especies que reflejan su auto-ecología como el tamaño corporal o nivel trófico, como factores relevantes que dan cuenta de procesos de selección. La teoría de metacomunidades abarca explícitamente la deriva, la selección y la dispersión (Holyoak et al., 2005), mientras que los estudios que tienen en cuenta las relaciones local- regional (Ricklefs & Schluter, 1993) destacan los efectos del pool de especies y la latitud, ya que de manera indirecta permiten entender los efectos de la especiación y la dispersión (Ricklefs, 1987). Sin embargo, la mayoría de los estudios se centran en los patrones. Adicionalmente, es necesario considerar que existen dos niveles de abstracción, bajo los cuales se puede analizar los patrones de diversidad. Dentro de una metacomunidad, se puede analizar la variación en la riqueza de especies entre comunidades (diversidad α, componente sobre el que se enfocan la mayoría de los trabajos) y la variación en la composición de la comunidad entre los sitios (Legendre et al., 2005), mientras que el análisis de la variación en la diversidad beta entre los grupos de sitios (Legendre et al., 2005; Tuomisto & Ruokolainen, 2006) y de la diversidad regional, solo se puede llevar a cabo comparando metacomunidades (nivel al cual existen pocos estudios observacionales, Logue et al., 2011). En este sentido, los archipiélagos, que son un conjunto de islas generalmente de un mismo origen geológico, son un buen sistema de estudio, debido a su carácter discreto y variable en términos de tamaño, forma y aislamiento, sumado al hecho de ser sistemas relativamente bien estudiados para los cuales existe una base empírica y teórica considerable (Whittaker & Fernández-Palacios, 2007; Lomolino & Brown, 2009), además que permite el estudio de los patrones de diversidad a nivel intra e inter archipiélagos. Como grupo de estudio se escogió a los mamíferos, ya que son un grupo bien estudiado, cuya taxonomía y sistemática está relativamente bien definida y se conocen sus relaciones de escalamiento alométrico (Damuth, 1981). 4.
(11) En esta tesis se estudiaron las causas de los patrones de diversidad alfa, beta y gamma en sistemas insulares. Para esto utilizamos la diversidad de mamíferos terrestres no voladores de 21 archipiélagos (155 islas) ubicados alrededor del mundo. En primer lugar, evaluamos los efectos de la capacidad de carga, el aislamiento y el tamaño corporal en los patrones de diversidad dentro de los archipiélagos, identificando las diferencias en los tamaños de efecto de cada variable a través de un meta-análisis, con el fin de poder evaluar la generalidad y la validez de los patrones observados (Capitulo 1). En segundo lugar, analizamos los patrones de diversidad entre los archipiélagos, ya que esto permite entender cómo se estructuran los ensambles de especies a una mayor escala, en este sentido, son pocos los antecedentes dentro de la teoría insular (pero ver Patterson & Atmar 1986, Schoener 1976). Para esto evaluamos cómo los componentes básicos de un modelo de colonización neutral: procesos de dispersión asociados a la conectividad y procesos de deriva asociados al tamaño de la metacomunidad, determinan la diversidad de los archipiélagos (diversidad- γ) y sus componentes α y β, además determinamos bajo que dominio nuestro modelo puede adaptarse eficazmente a los datos de un mundo no neutral (Capitulo 2). Adicionalmente, analizamos como la heterogeneidad, la configuración espacial, el tamaño, la latitud, el pool de especies y aislamiento de los archipiélagos afectan la partición de la diversidad a través de modelos de ecuaciones estructuradas que permiten entender los efectos directos e indirectos de los factores y así determinar cuáles son los procesos involucrados en la estructuración metacomunitaria (Capitulo 3). Los resultados de esta tesis permiten identificar los factores y procesos involucrados en la formación de los patrones de diversidad de mamíferos insulares a nivel intra e inter archipiélagos. Como lo señalaron MacArthur & Wilson (1963), entender la dinámica de la distribución de las especies en los archipiélagos puede ayudar a comprender la variación en el tamaño y la variedad distribución ecológica de los taxones a nivel continental (por ejemplo, Brown, 1995; Gaston, 2009) y nuestro conocimiento en general sobre la biogeografía y ecología de comunidades, ayudando de igual manera a entender los sistemas antrópicamente modificados (por ejemplo, Terborgh, 1974; Laurance, 2010).. 5.
(12) CAPÍTULO 1 ON THE DETERMINANTS OF ALPHA AND BETA DIVERSITY IN INSULAR BIOTAS. Abstract Understanding the processes that determine the number and identity of species in local communities remains a vexing problem and a major goal in both community ecology and biogeography. Islands biotas have historically been used to test simple hypotheses on the role of different factors in affecting changes in species numbers. However, as yet we do not have an overall agreement on what are the important factors driving the observed changes. In this study we perform a meta-analysis based on mammalian species inhabiting 19 archipelagoes across the world. Unlike previous studies we focus on two diversity components (alpha and beta diversity) and distinguished between the spatial and nestedness component in beta diversity. For both the alpha and beta components of diversity we analyzed the relative importance of area, habitat diversity and productivity, which are related to the capacity of islands to sustain species, and two measures of isolation (geographical distances between islands and distance to the nearest mainland). Since body size affects species incidence across archipelagoes we repeated the analysis for species in different body size classes (quartiles). Each relationship was characterized by two effect sizes, strength (correlation coefficient) and the slope. Our analysis shows that alpha diversity increases with island area and habitat diversity. Island productivity, however, had no significant effect. The spatial component of beta diversity decreases with increased body mass and with decreases in the distance between islands, while the nestedness component increases with increased body mass, islands area and habitat diversity.. 6.
(13) INTRODUCTION Understanding the processes that determine the number and identity of species in local communities remains a vexing problem and a major goal in both community ecology and biogeography (Hortal et al., 2012). Ever since MacArthur and Wilson (1967) and Diamond (1975) island biotas have had a prominent role as study systems, due to its discrete nature and quantifiable variation in size and isolation, which allows an the role easy quantification of dispersal and resource availability upon species richness. This is reflected in that a substantial amount of theoretical and empirical work is currently available for insular communities (Whittaker & Fernández-Palacios, 2007; Lomolino & Brown, 2009) and the proliferation of alternative hypotheses to explain diversity patterns in islands (e.g., Connor & Mccoy, 1979; Kalmar & Currie, 2006; Whittaker et al., 2008). Many authors have postulated that the number of species in a given community depends on the processes that affect the availability of limited resources for consumers (Brown, 1981; Wright, 1983; Ernest & Brown, 2001; Hubbell, 2001; Monte-luna et al., 2004), and have used factors such as the area, habitat heterogeneity and available energy, which are related to the variety and availability of resources in islands, as predictors of their richness (Wright, 1983; Currie & Fritz, 1993; Rosenzweig, 1995; Whittaker & Fernández-Palacios, 2007). The mechanisms that support a role for these variables in affecting richness are: (i) area affects susceptibility to extinction; as the area of the island increases so does the amount of resources and population size, thus reducing the probability of local population extinction (MacArthur & Wilson, 1967), hence higher richness should be expected in larger islands (ii) the habitat heterogeneity allows greater possibilities for niche partitioning and therefore a larger number of species that can coexist (Williams, 1964; MacArthur & Wilson, 1967; Triantis et al., 2003), and (iii) the availability of resources, which can be measured in terms of energy or productivity, would increase the number of individuals and species that a given island could sustain (Wright, 1983). Similarly island isolation is an important determinant of species richness (MacArthur & Wilson, 1967), as it affects colonization and extinction (MacArthur & Wilson, 1967; Brown & Kodric-Brown, 1977) and the positive effect it has on speciation rates (Heaney, 2000). Thus factors related to isolation such as the distance to the mainland and the distance between islands can be important in determining species richness. However, the number of species in a 7.
(14) local community or insular biota can provide limited information on the processes shaping community assembly, and a closer look at the factors that account for variation in species composition or beta diversity (Whittaker, 1972; Condit et al., 2002; Myers et al., 2013) can be important. Beta diversity (β), the same as species richness, is influenced by ecological processes that determine the distribution of species, including niche differentiation, competition and dispersal and spatial characteristics of the physical environment where in which these processes occur (Nekola et al., 1999; Koleff et al., 2003). β-diversity can be generated by loss and replacement of species or a combination of the two (Baselga, 2010) and therefore, it can be divided in two components; the nestedness-resultant (Bnes) and the spatial species turnover (Bsim) (Harrison et al., 1992; Baselga, 2007). The nestedness component of β-diversity is high when the identity of species found in depauperate sites tend to be a subset of the species found in sites with greater richness (Wright & Reeves, 1992; Ulrich & Gotelli, 2007), which is a result of a non-random process of species loss (Patterson & Atmar, 1986; Cutler, 1994; Gaston & Blackburn, 2000), or addition due to differential colonization (Kadmon, 1995; Lomolino, 1996) or a nested distribution of habitats (Simberloff & Martin, 1991). On the other hand, spatial turnover reflects the fact that some species are replaced by other species as a result of changes in the environment or because of spatial and historical constraints (Qian et al., 2005) or due to stochasticity. The spatial species turnover component emphasize changes in species composition independent of changes in richness, while the nested component quantifies the addition or loss of species that affect richness among sites. Overall, environmental dissimilarity and geographical distance are the two most important factors in explaining beta diversity (Harrison et al., 1992; Nekola et al., 1999). The change in the composition of species across environmental gradients is a function of the difference between habitats and is mainly explained by species sorting, whereby, different environments favors different species so that the better competitor on a given resource outcompetes other species and ‘wins’ on that resource (Huston, 1999; Chase & Leibold, 2002; Leibold & Holyoak, 2004; Davies et al., 2009). The effect of geographic distance, on the other hand, is explained in large part by differences in the biogeographic history and dispersal ability of species (Harrison et al., 1992; Condit et al., 2002; Chase, 2003). Most of these studies, however, have been done in continuous systems, where the role of isolation would be 8.
(15) difficult to see, whereas for island systems it has been reported that beta diversity is regulated by the distance and area in birds (Guerrero et al., 2005; Fattorini, 2010) or only by the distance between islands for invertebrates and reptiles (Hausdorf & Hennig, 2005; Dapporto et al., 2007; Fattorini, 2010). Traits such as body size and species dispersal ability could have a detectable effect on insular diversity patterns, the same as in continuum systems (Meiri & Thomas, 2007; Soininen et al., 2007). For example, the data reported for mammalian dispersal distances and dispersal ability are inversely related to body size and (e.g., 10- 150 km for large mammals vs. 410 km for small, see Whittaker & Fernández-Palacios, 2007). Further, since there is a negative relationship between abundance and body size (Damuth, 1981; Peters, 1986) and that the same amount of resources can support few large or many small due to the positive scaling between body size and the rate of resource consumption and the energetic equivalence it entails (Damuth, 1981; Calder, 1984; Peters, 1986; Marquet et al., 1995; Ernest & Brown, 2001; White et al., 2007), it is expected that the distribution of body sizes differ between high species richness and low richness areas, due to metabolic as well as community processes that regulate the assembly of communities (Brown & Nicoletto, 1991; Marquet & Cofré, 1999). According to the hypotheses proposed by Brown and Nicoletto (1991), across continental mammals assemblages, competitive exclusion among medium size species, differential extinction of large species and specialization of the smaller ones drive changes in species composition, hence beta diversity across spatial scales. Similarly, Marquet and Taper (1993) suggest that in insular systems, the smallest and the largest species would tend to change the most as landmass area changes due to differential extinction, such that the smallest islands would tend to have a nested subset of the species present in the larger ones. In this sense, extreme size species would show high beta diversity and may only be present in islands with high carrying capacity, while according to Brown and Nicoletto (1991) the modal size species would also show a high spatial turnover. Further, because of competitive exclusion modal size species would also show high turnover the same as small ones due to specialization on energetically rich resources (Brown & Nicoletto, 1991). Finally, in addition to factors mentioned above (i.e., resource availability and variety, isolation and body size), the history of insularization, or how a particular system came into existence (i.e. the island geologic history), may be very important in affecting diversity 9.
(16) patterns as the relative importance of extinction and colonization vary depending on the island´s origin; continental, oceanic or barrier (e.g., Whittaker & Fernández-Palacios, 2007; Lomolino & Brown, 2009; Weigelt & Kreft, 2012). In this contribution, we evaluated the effects of carrying capacity, isolation and body size upon diversity patterns, identifying the differences in effect sizes for each variable through a meta-analysis. In particular we test the following hypotheses:. 1. As it has been commonly observed we expect that species richness increases with factors associated to island carrying capacity and decreases with those related to isolation. Further, because the diversity of oceanic islands arises largely by colonization and endemic radiation, while in landbridge islands assemblages may be shaped largely by extinctions, and can be in a non-equilibrium state, we expect differential effects of carrying capacity and isolation factors across island type. 2. Carrying capacity (total NPP and number of Habitat types) have a larger effect size than area (see Wylie & Currie, 1993). 3. Changes in island carrying capacity affect beta diversity, specifically through the component of nestedness-resultant. In this sense, the effect should be stronger in land-bridges islands. 4. Geographical distances between islands affect beta diversity, specifically through the component of spatial turnover. 5. There is a nonlinear effect of body size on beta diversity with a maximum at intermediate sizes for spatial turnover component and an opposite pattern for nestedness-resultant.. METHODS Data To assess the effects of carrying capacity, isolation and body size distribution in the diversity of insular mammals, we compiled presence-absence data of species on islands by carrying out a literature review including only recent, terrestrial, non-volant mammal fauna found in 243 islands, spread over 17 archipelagos around the world (Table 1). The total area of the sampled islands within each archipelago corresponds in all cases more than 80% of total area. With 10.
(17) these data, we calculated alpha diversity as the number of species per island and total beta diversity (βsor) between each pair of islands within archipelagoes using Sorensen´s index (Baselga, 2010). The contribution of spatial turnover (βsim) was measured with the Simpson index (Koleff et al., 2003) and beta diversity due to nestedness-resultant (βnes), was measured as the difference between βsor and βsim (Baselga, 2010). These measures do not overestimate the fraction of total dissimilarity can be attributable to richness differences and evaluate nesting patterns considering both on paired overlap and matrix filling (Baselga, 2012). To determine the carrying capacity of the islands, we use three measures: total area of the island, total net primary productivity (NPP) and the choros measure proposed by Triantis et al. (2003), choros (K) arises as the result of the multiplication of the area of the region with the number of the different habitat types present on the region (K = H*A), where H is the number of habitats and A is the total area of the region. Subsequently, we analyzed the effect of average net primary productivity and the number of habitats. The isolation of each island was determined using two measurements, the closest distance to the mainland and the average minimum distance to other islands of the archipelago. To measure the effect of body size, we divided the distribution of all insular mammals (transformed to log2) in four groups using the body size quartiles (< 32.44, 32.45-162.02, 162.03-2288.2, >2288.2 g). To determine the spatial location, area and isolation of islands we used the GSHHS-A Global Self-consistent, Hierarchical,. High-resolution. Shore. line. Data. Base. version. 2.1.. (http://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html). The NPP was calculated from the MODIS GPP / NPP (Zhao & Running, 2010), and the number of habitats from the GlobCover 2009 (Global Land Cover Map), using the software ArcGIS 10. Body mass was obtained from the PanTHERIA database (Jones et al., 2009) otherwise we used the midpoint of the range of body size given in Walkers Mammals of the World (Novak & Novak, 1999). Additional data were compiled from other sources (Kays & Wilson, 2009; Okie & Brown, 2009; Alviola et al., 2011; Heaney et al., 2011; Rickart & Heaney, 2011). For species for which we could not find any published measurement of body mass (n= 57), we relied on the fact that phylogenetically close species tend to be similar in size (Smith et al., 2003) and used the geometric mean body mass of the closest phylogenetic relative for which information on body mass was available (Table S1). To assess the effect of island origin on species diversity we recognize two main types of geological origins: land-bridge and oceanic islands. Land-bridge or continental 11.
(18) islands are either part of the continental shelf or were once connected to continental landmasses but became isolated from it. Oceanic islands are mainly of volcanic origin and have arisen as newly formed land from the sea floor. We classify the archipelagos considering the origin of most of the islands following Heaney (1986); we defined whether an archipelago was ‘land-bridge’ or ‘oceanic’, based on the ocean depth separating it from a continental land mass and using as a threshold a depth of 120 m; below that depth we considered an island as landbridge and oceanic otherwise. Statistical Analysis We conducted linear regressions between species richness and the explanatory variables (area, NPP, NPP/area, number of habitats and choros) (log transformed) for each of the 17 archipelagos. For the analysis of beta diversity, we used distance or dissimilarity matrices, the dissimilarity matrix of environmental variables (area, PNN and habitat) was computed as the Mahalanobis distance between each pair of islands (Orlóci, 1978) for isolation variables we used Euclidean distance matrices. The values of the respective distances and beta diversity components were used to perform linear regressions. To calculate the effect sizes for the metaanalysis we considered the correlation coefficient (r), slope (standardized between -1 and 1, b) and the error of the slope. We measured two effect sizes that reflect different aspects of the relationships, the strength and steepness. The strength quantifies the amount of variation in diversity with respect to the explanatory variables, and defined as Fisher's Z transformation of the correlation coefficient (rz), weighted by the sampling variance (Rosenberg et al., 2000). The steepness indicates how quickly diversity changes with respect to the variable of interest, and is reflected in the slope (b) with SEb as variance estimate (Hillebrand et al., 2001). We used a weighted meta-analysis on rz and b (Rosenberg et al., 2000; Hillebrand et al., 2001) to calculate the grand mean effect sizes, and their 95% confidence intervals (CIs) using the bootstrapping procedure in MetaWin 2.0 (Rosenberg et al., 2000). We calculated the Q statistic and its significance (using 9999 randomizations) to assess if the studies have very heterogeneous effect sizes, which would imply that the average effect does not adequately represent the set of studies, We complemented our Q estimates with reports of the I2 index, which can be interpreted as the percentage of total variability in a set of effect sizes because of true 12.
(19) heterogeneity, that is, between-study (or between-comparison) variability. For instance, I2 = 50 means that half of the total variability among effect sizes is caused not by sampling error but by true heterogeneity between studies or comparisons. To assess the effect of islands type, we used the random-effects model for categorical data, an algorithm referred to as the mixed effects model (Gurevitch & Hedges, 1999). This model is analogous to ANOVA and is based on the more realistic assumption that a given class of studies shares a common effect and that random variation among studies exists (Gurevitch & Hedges, 1993; Rosenberg et al., 2000). Under this approach, heterogeneity of results across comparisons or studies (i.e. the amount of variation in r-scores) was estimated by the Q statistic, a measure that partitions total heterogeneity into variance explained by the model (QM) and residual error not explained by the model (Qe; i.e. Qt = Qb + Qe; Rosenberg et al., 2000). This partitioning is analogous to F in ANOVA tests (Rosenberg et al., 2000). Both Qb and Qe were tested against an X2distribution (alpha= 0.05). A statistically significant Qb implies that here are differences among cumulative effect sizes for the groups; statistically significant values of Qe imply that there is heterogeneity among effect sizes not explained by the model (Rosenberg et al., 2000). Upon detecting statistically significant heterogeneity we considered the bootstrapped 95% confidence interval linked to each effect size to determine which categories were different. To determine the effect of body size on alpha diversity, we calculated the average values and the standard deviation in the richness of each body size quartile standardized by the total number of species. We calculated effect sizes using the Hedges’ d, in which effect size corresponds to the standardized mean difference d (Gurevitch & Hedges, 1993). To obtain the standard mean difference (d), we divided the difference between group means by the pooled standard deviation (SD) for all pair-wise comparisons of body size classes. To determine the effect on beta diversity, we perform the same procedure, but calculating the mean and standard deviation for each of the measures of beta diversity. We used Comprehensive MetaWin 2.0 for all calculations (mean effect size, confidence interval and Q statistic). For each weighted mean r and Hedges´d, we calculated the fail-safe number of studies, the number of additional ‘negative’ studies (studies in which the intervention effect was zero) that would be needed to increase the P value for the meta-analysis to above 0.05 (Rosenthal, 1991). The larger the failsafe number of studies, the greater our confidence in that the observed results are a reliable estimate of the true effect is high (Rosenberg et al., 2000). In general, effect sizes of 0.20, 13.
(20) 0.50, and 0.80 are thought to represent weak, moderate, and strong effects, respectively (Rosenberg et al., 2000). We use the model developed in the chapter 2 as a null model against which compare empirical patterns in alpha and beta diversity. Comparisons were made through a test of Wilcoxon paired test for each of the archipelagos. To determine the effects of the factors of interest we repeated the meta-analysis to model results.. RESULTS We found an overall significance of carrying capacity and alpha diversity relationship (Fig 2), although more than one third of the original studies did not show significant relationships. Significant differences, were detected between the five measures of carrying capacity: area, total NPP, choros, NPP/area and number of habitats in the strength (Qb = 6.93, df = 4, 80, P = 0.003) and slope (Qb =53.99, df = 4, 80, P = 0.001) because of the effect of NPP/area. The effect size for area, total NPP, choros and number of habitats was strongly positive and statistically significant, while the effect of mean NPP was negative and no significant (Fig 2). The size effect of measures of carrying capacity (area and number of habitats) on alpha diversity did not differ across island types (Table 2). Significantly positive effects were recorded for both land-bridge and oceanic islands for all measures. Isolation also had a significant effect upon alpha diversity (Fig 2), but this was weak and negative. Both isolation measures, distance to the mainland (rz = -0.27 and b = -0.05) and distance among islands (rz = -0.25, b = -0.05) had similar effects upon alpha diversity (Qb = 0.03, df = 1, 32, P = 0.87 and Qb = 2.32, df = 1, 32, P = 0.87 respectively). Also, the effect size of isolation respect to the mainland on alpha diversity did not differ across island type, although the effect was positive but not significant for oceanic islands (Table 2). Regarding the effect of body size upon alpha diversity no significant differences were found between the first quartile and the other quartiles and between the third and fourth quartile. In contrast, significant reductions were found in third and fourth quartile compared with the second quartile with moderate and strong effect sizes (d2-3 = -0.42 and d2-4 = 0.97; Fig 3).. 14.
(21) The effect size of carrying capacity on beta diversity (Bsor) did not differ across capacity measures for strength (Zr) or slope (b) (Qb = 3.72, d.f. = 2, 48, P = 0.08; Qb = 0.78, d.f. = 2, 48, P = 0.14; respectively Fig. 4). Carrying capacity has a significant positive effect for both strength and slope, but higher for Zr (0.15) than for the slope (0.02). Significantly positive effects were recorded for the differences in area and for differences in the number of habitats, but not for mean NPP (Fig. 5). This result is due to the effect of differences in area and habitat number in the nestedness-resultant component (Bnes; Fig 5). The effect size of delta-area or delta-habitat on nestedness-resultant diversity did not differ across island type, although the effect of delta-habitat is greater in land-bridge islands (Table 2). Isolation affect positively and significantly beta diversity (Zr= 0.19; b= 0.03) principally through spatial turnover component (Bsim; Zr= 0.14; b= 0.02; Fig 4). The effect size did not differ between differences in distance to mainland and inter-island distances (Qb = 0.59, d.f. = 2, 32, P = 0.28; Qb = 0.08, d.f. = 2, 32, P = 0.08; for Zr an b respectively), but the effect of distance inter-islands is higher than distance to mainland (Fig 4). Body size has opposite effects on turnover (Bsim) and nestedness-resultant (Bnes) components of beta diversity. For Bsim, significant differences were found between the first quartile and the other quartiles (greater diversity in the first quartile), whereas, for the fourth quartile BNES increases significantly with respect to the other (Fig 5). Regarding the analysis of bias, for both alpha and beta, values of I2 ranged from 0% to 4% in the analysis of carrying capacity and isolation, indicating that no exist large variation in the size of the effect. However, body size analysis showed I2 values between 0% and 52% indicating that the variation in the effect size in across factor-level categories exists and needs to be explained (Table S2, S3). The fail-safe number of studies was higher for alpha than for beta. In most cases, the neutral model overestimates alpha diversity, finding significant differences in 10 of 17 archipelagos (Table 3). Moreover, the model underestimates beta diversity, finding significant differences in 14 of 17 archipelagos. However, in the neutral model as well as empirical data, carrying capacity measures have the same effect on alpha diversity, while the distance to the mainland showed significant differences (Figure 6). To beta diversity, significant differences in effect size of the area and the distance between islands is observed (Figure 6). 15.
(22) DISCUSSION Species diversity is governed by multiple processes operating at different spatial and temporal scales (Brown & Lomolino, 2000; Gaston & Blackburn, 2000; Whittaker et al., 2001). Ever since (MacArthur & Wilson, 1963) area and isolation have been thought to be among the main factors affecting species richness in island systems, With time, however, other variables, have been added to the list of potential drivers of alpha diversity in insular system, such as productivity and complexity of habitats (Williams, 1964; Wright, 1983), and as yet we do not have a clear picture on their relative importance. In this contribution we used a meta-analytic framework to disentangle the relative contribution of several potential drivers of alpha diversity (area, NPP, NPP/area, choros and number of habitats). Our results show that effects of carrying capacity measures upon changes in the mammal species richness vary statistically. Choros showed slightly higher effect sizes while total productivity (NPP) showed smaller effects. Similarly, the numbers of habitats present a significant effect on species richness but lower than the effect of area, while NPP / area had no significant effect. In this sense, the nonflying mammal species richness is determined primarily by an area per se effect and by the interrelationship between the area and habitats. Species-energy theory (Wright, 1983), is based on the resource requirements of the species and the production of resources in the islands, in this sense, the NPP /area represents general resources for herbivorous mammals and indirectly resources for higher trophic levels. Our results show that there is no effect of NPP/are upon alpha diversity. Other researchers working in continuous systems, however, have found an effect of NPP / area in global mammals richness (Waide et al., 1999; Luck, 2007), similarly, Kalmar and Currie (2006) found a significant effect of temperature (used as a proxy for productivity) on the richness of birds. Thus the he lack of correlation within islands could reflect an interaction between area and / or isolation effects and NPP / area, such that the most productive islands tend to be smaller or more isolated than the most productive ones. To test for this hypothesis we looked for associations between island area, isolation and NPP/area, finding a positive and significant effect between isolation and the NPP / area 0.27 (95% CI: 0.04 – 0.53) and a not significant effect of area (0.17; 95% CI: -0.01-0.42). This suggests that the relationship between NPP / area and richness of mammals is affected by dispersal limitation. As predicted by the theory of island biogeography (MacArthur & Wilson, 1967), 16.
(23) there is a negative effect of isolation, no matter how it is measured as distance from the mainland or average distance between islands, according to reports from Kalmar and Currie for island birds. Beta diversity was also affected by island carrying capacity as reflected in the nestedness-resultant component of beta diversity and measured as area and number of habitats, implying that changes in habitat and area have a significant effect on the differential gain and loss of species. Further, the slope observed between habitat changes either number of habitats and Bnes, indicates that species extinction are susceptible to both area and habitat loss. Because there is a positive relationship between the area and the number of habitats, large mammals are the most affected by these changes. Isolation, on the other hand, affected beta diversity in different ways. Distance to the continent, while having a significant and positive effect on beta diversity, it had similar impact on the nestedness-resultant and species replacement or turnover component of beta diversity. Inter-island distance, however, affected mostly the turnover component. This is an interesting result that can be explained by considering that the replacement of species associated with geographic distance is explained largely by biogeographic history and the dispersal capability of species (Harrison et al., 1992; Condit et al., 2002; Chase, 2003). For example, general climatic gradients could affect a compositional change between more distant locations, and therefore can influence Bsim. Greater spatial turnover of species may be due to a steeper gradient, that strongly influences the distribution of species, especially in land bridge island systems because the underlying spatial heterogeneity of the landscape prior to isolation and greater area (Tscharntke et al., 2012). Moreover, given that the distance between islands has a significant effect upon Bsim, but the effects of distance to the continent are not significant, we conclude that the majority of colonization is determined by exchanges between the islands, indicating that the colonization especially in oceanic islands is of a stepping stone type (Fattorini, 2010). However, our analysis cannot distinguish between differential colonization and spatial heterogeneity. Our data partially support the hypothesis that median body size have higher. alpha. diversity, due its high frequencies of island occurrences (Okie & Brown, 2009), because even the smallest mammals (<32.5) are not significantly different from other quartiles, the mammalian body size between 32.5 and 162 g have higher alpha diversity than mammals of larger body sizes. Mammals within this body size range have low beta diversity in terms of 17.
(24) Bnes as Bsim, which may be a result of good dispersion capability, coupled with small energetic requirements and generality in resource (Brown & Nicoletto, 1991; Whittaker & Fernández-Palacios, 2007; Okie & Brown, 2009). The positive and significant effect of maximum body size range with Bnes, together with the low alpha diversity of this size range, indicates a differential extinction of the species, which may be an indirect effect of area because, as has been reported by Marquet and Taper (1998) and confirmed by other studies (Okie & Brown, 2009; Millien & Gonzalez, 2011) and as shown by our data (rz = 0.73, b = 0.49) there is a positive relationship between the maximum size and the area of the islands, as a result of habitat requirements, trophic status and likelihood of extinction of species (Brown et al., 1993; Marquet & Taper, 1998). The effects of body size distribution on species turnover are contrary to those of βnes, in this case, the smaller species have high spatial turnover, which may be associated to the existence of strong restrictions associated to metabolic requirements such as space, food and other environmental conditions (Brown & Maurer, 1986; Marquet & Taper, 1998; Okie & Brown, 2009). This can lead to high competitive exclusion exists within this range of body size, that include modal size of all insular mammals studied (32.35g), agreeing with the findings of Brown and Nicoletto (1991) for mammals in North America, although its modal value is a little higher (45g), which added to the high dispersal capabilities in relation to large mammals (Whittaker & Fernández-Palacios, 2007) can account for the observed pattern. Island type is considered as a strong determinant of species diversity, although, this factor is a problematic variable. It is collinear with other variables (e. g. area, isolation) thus limiting biological inference. It is likely that this is one of the reasons why we did not find a significant effect of island type. On land-bridge islands, we expected greater effect of carrying capacity because many assemblages represent ‘relaxation’ faunas as result of selective or random extinction, but extinction may also play an important role in oceanic islands. Assemblages on oceanic islands are likely to be shaped predominantly by colonization and endemic speciation, determined by isolation, although in land-bridge islands the distance to the mainland can affect the probability of recolonization and speciation of the species. Therefore regardless of geological origin, large and remote islands have endemic species associated with a high diversity (e.g Luzon and Mindanao in Philippines and Borneo and Java. 18.
(25) in Sunda shelf), although these features are more common in oceanic islands, which may explain the tendency to a positive effect of distance in this type of islands. The results provided here describes the patterns of alpha and beta diversity for nonflying mammals in various archipelagos, which allows to establish whether factors influencing diversity patterns are consistent across scales and geographical context. Alpha diversity is primarily determined by a carrying capacity effect mediated by area and habitat, as seen in the model neutral. While for beta diversity species body size is the factor that has a larger effect, but it has a reverse effect in both beta components. According to our results beta diversity is likely affected by differential extinction accounting for the importance of the nestednessresultant component, while mass effects and historical factors affect spatial turnover and are mostly associated to distance effects. For these reasons, we found differences in the effect size of the area and the distance between islands between the observed values and expected under a neutral model. The data analyzed in this study, have an intrinsic bias, because with the exception of bats, generally native mammals are not a feature of most isolated oceanic islands (Whittaker & Fernández-Palacios, 2007). Similarly, there is a bias with respect to the geographical location of the islands studied. There are few studies in the southern hemisphere. However, bias analysis indicate that there is little unexplained variance within the analysis performed and for most of the correlations fail-safe number is relatively high. With regard to the strength of effect sizes, we found that alpha diversity depends mainly on the islands intrinsic factors (carrying capacity measures), while beta diversity has a strong relationship with body size. So it would be interesting to examine in future studies whether beta diversity may be influenced by other species traits such as trophic level and dispersal ability.. 19.
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(33) Table Legends Table 1. General characteristics of the studied archipelagos. It shows the mean and standard deviation for each variable. Table 2. Comparisons between the types of islands (Statistical Qb) for the relationship of variables studied with alpha and beta diversity. It also presents mean effect size, strength (rz) with 95% confidence intervals, fail-safe number and I2 index Table 3. Comparisons between observed and expected alpha and beta diversity under neutral model. Table 4. Differences in mean effect size (95% confidence intervals) strength rz for observed and expected alpha and beta diversity under neutral model. Figure Legends Figure 1. Geographical location of the islands considered in the study Figure 2. Mean effect size (95% confidence intervals) strength rz (a,c) and slope b (b,d) for alpha diversity. a-b, Effect sizes for carrying capacity measures: Area, total productivity (totalNPP), mean productivity, choros and number of habitats.c-d, Effect sizes for isolation measures: distance to the mainland and mean inter islands distance. Figure 3. Effect size (Hedge´s d) for alpha diversity in relation to body mass, considering its distribution quartiles. Figure 4. Mean effect size (95% confidence intervals) strength rz (a,c) and slope b (b,d) for beta diversity (Bsor, black squares) and its components; nestedness-resultant (Bnes, triangles) and turnover (Bsim, circles). a-b, Effect sizes for carrying capacity measures: Area, total productivity (total-NPP), mean productivity, choros and number of habitats.c-d, Effect sizes for isolation measures: distance to the mainland and mean inter islands distance. Figure 5. Effect size (Hedge´s d) components of beta diversity, turnover (circles) and nestedness-resultant (triangles) in relation to body mass, considering its distribution quartiles. 27.
(34) Supplementary material Table S1. Mean body size for all species.. Table S1. Mean effect size, strength (rz) with 95% confidence intervals, heterogeneity analysis, fail-safe number and I2 index for variables determinants of alpha and beta diversity. Table S2. Mean effect size, slope (b) with 95% confidence intervals, heterogeneity analysis, fail-safe number and I2 index for variables determinants of alpha and beta diversity. 28.
(35) Table 1.. 29.
(36) Table 2.. Diversity. Variable Area. Alpha. Number of habitats. Distance to Mainland. Δ Area Bnes Δ Number of habitats. Mean Effect (Zr). LCI. UCI. All. 0.93. 0.68. 1.17. Landbridge. 0.97. 0.62. 1.34. Oceanic. 0.87. 0.45. 1.21. All. 0.79. 0.68. 0.92. Landbridge. 0.82. 0.66. 1.00. Oceanic. 0.74. 0.65. 0.86. All. -0.27. -0.49. -0.05. Landbridge. -0.44. -0.69. -0.18. Oceanic. 0.02. -0.29. 0.25. All. 0.30. 0.16. 0.47. Landbridge. 0.30. 0.07. 0.55. Oceanic. 0.30. 0.23. 0.36. All. 0.36. 0.21. 0.53. Landbridge. 0.40. 0.16. 0.59. Oceanic. 0.30. 0.18. 0.57. Island type. 30. Fail-safe number. I2 (%). 18.85 1, 15 0.28. 233.80. 0.05. 14.55 1, 15 0.56. 539.50. -0.24. 12.74 1, 15 0.69. 20.70. -0.41. 14.09 1, 15 0.59. 65.70. -0.28. 15.06 1, 15 0.52. 90.10. -0.20. Qb. df. P.
(37) Table 3. Alpha diversity Archipelago. Observed Expected. Beta diversity (Bsor). W. P. W. P. Adriatic. 6.5. 12.9. 105. 0.000. Observed Expected 0.3. 0.3. 2761. 0.008. Alexander. 7.0. 48.6. 299. < 0.001. 0.5. 0.2. 34400. < 0.001. Phillipines. 6.0. 167.1. 861. < 0.001. 0.8. 0.2. 335500 < 0.001. Eolie. 3.0. 7.1. 28. 0.015. 0.1. 0.2. 191. 0.007. Napolitan. 5.0. 1.7. 10. 0.120. 0.3. 0.0. 21. 0.030. Ponziane. 2.0. 2.1. 9. 0.810. 0.3. 0.1. 45. 0.080. Sardinian. 3.0. 4.3. 40. 0.530. 0.5. 0.2. 1453. < 0.001. Tremiti. 2.5. 1.6. 10. 0.120. 0.3. 0.0. 20. 0.063. Tuscan. 4.0. 4.7. 11. 1.000. 0.4. 0.1. 217. < 0.001. Japan. 8.5. 110.4. 78. 0.001. 0.7. 0.1. 2211. < 0.001. Kuril. 5.5. 36.1. 36. 0.008. 0.8. 0.2. 404. < 0.001. Lake Huron. 2.0. 1.2. 188. 0.009. 0.5. 0.7. 17090. < 0.001. Maine. 4.0. 2.0. 240. 0.001. 0.5. 0.8. 26530. < 0.001. Mar de Cortez. 2.0. 7.9. 507. < 0.001. 1.0. 0.9. 118400 < 0.001. Sunda Shelf. 18.0. 231.8. 99. 0.002. 0.7. 0.4. Texas. 16.0. 8.2. 17. 0.218. 0.4. Virginia. 3.0. 3.3. 34. 0.558. 0.5. 31. 3198. < 0.001. 0.2. 75. 0.422. 0.4. 722. 0.020.
(38) Table 4.. Diversity Variable Area Total NPP Choros Alpha Mean NPP Habitats number Mainland distance Inter-islands distance Area Mean NPP Bsor Habitats number Mainland distance Inter-islands distance. Effect size 0.94 0.88 0.96 0.00 0.79 -0.26 -0.25 0.17 0.05 0.24 0.15 0.23. Observed LCI UCI 0.68 1.18 0.63 1.12 0.74 1.20 -0.21 0.19 0.68 0.91 -0.47 -0.05 -0.45 -0.07 0.07 0.31 -0.02 0.13 0.13 0.40 0.09 0.21 0.08 0.34. 32. Expected Effect size LCI 0.88 0.64 0.89 0.67 0.84 0.61 0.30 0.11 0.57 0.39 0.36 0.06 -0.53 -0.69 0.00 -0.05 0.18 0.10 0.14 0.03 0.13 0.04 0.57 0.42. UCI Q 1.13 0.126 1.11 0.002 1.10 0.497 0.55 3.867 0.72 4.199 0.69 10.865 -0.41 4.498 0.07 2.791 0.30 1.671 0.30 0.994 0.28 0.043 0.73 11.104. P 0.723 0.967 0.481 0.050 0.050 0.001 0.034 0.048 0.196 0.319 0.835 0.001.
(39) Figure 1.. 33.
(40) Figure 2.. 34.
(41) Figure 3.. 35.
(42) Figure 4.. 36.
(43) Figure 5.. 37.
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