The the concept of nested species assemblages and its utility for understanding effects of habitat fragmentation

Basic Appl. Ecol. 2, 87–95 (2001) © Urban & Fischer Verlag http://www.urbanfischer.de/journals/baecol

Basic and Applied Ecology

The the concept of nested species assemblages and its utility for understanding effects of habitat fragmentation Jörg U. Ganzhorn*, Barbara Eisenbeiß Zoologisches Institut und Zoologisches Museum, Hamburg University, Hamburg, Germany

Received September 20, 2000 . Accepted November 2, 2000

Abstract The concept of nestedness represents a null model that measures the order in presence-absence matrices of species in different assemblages (Patterson & Atmar 1986, 2000). Based on the assumptions of the model, consequences of fragmentation and the vulnerability of species to habitat change can be quantified. Different species assemblages are considered to be nested perfectly, if subsets of one or more species are lost at a time as communities decline in species number and none of the species lost from the richer communities reappears in one of the species poor communities once it was lost. Nestedness is most prevalent in habitat fragments (islands) derived from a once continuous system with a common species pool that became isolated. The presence-absence matrix of species at given sites can be ordered in a way to maximize regularity. Then, the order of sites and species reflects habitat suitability of the sites and the vulnerability of a given species to fragmentation. This concept of nestedness is illustrated and used 1. to derive estimates of minimum viable populations of lemurs in forest fragments of Madagascar; 2. to learn, at which size a forest fagment is perceived as “forest” by Malagasy reptiles; 3. to test whether the system of protected areas within the city of Hamburg represents a network for the local avifauna with species exchange or whether the single protected areas represent islands in the urban matrix; 4. to link life history traits to the vulnerability of species to the urban environment and to develop hypotheses on the causes underlying the evolution of nested patterns. The analysis of the urban system revealed that the protected areas within the city limits of Hamburg are perceived by birds as islands in an urban matrix. “Green” links between the areas do not act as corridors that allow free movement of all species. The vulnerability of any given bird species to fragmentation of woodland habitat within the city depends on its ability to use the urban matrix between the protected areas and seems to be related to the investments in egg-shell material. Species with high requirements for egg-shells disappear earlier from the community. Analyses of nested patterns can provide insights in biodiversity processes and valuable recommendations for decision making where longterm data are not available. The main values of the analyses of nested patterns, however, is to develop hypotheses that can then be tested with more detailed studies. Anwendung des Konzepts geschachtelter Gemeinschaften im Naturschutz Das Konzept geschachtelter Artengemeinschaften repräsentiert ein Nullmodell mit dessen Hilfe Konsequenzen von Fragmentation auf die Biodiversität und die Anfälligkeit von Arten auf Veränderungen ihres Lebensraums quantifiziert werden können (Patterson & Atmar 1986, 2000). *Corresponding author: Jörg U. Ganzhorn, Zoologisches Institut und Zoologisches Museum, Hamburg University, MartinLuther-King Platz 3, D - 20146 Hamburg, Germany; Phone: ++49-40-4 28 38 42 24, Fax: ++49-40-4 28 38 59 80, E-mail: [email protected]

1439-1791/01/02/01-087 $ 15.00/0

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Ganzhorn and Eisenbeiß Artengemeinschaften sind vollständig geschachtelt, wenn bei abnehmender Artenzahl eine oder mehrere Arten aus den Gemeinschaften verschwinden und keine dieser Arten in artenreicheren Gemeinschaften wieder auftritt. Schachtelung tritt vor allem in fragmentierten Lebensräumen auf, wenn die Fragmente nicht “de novo” entstanden sind und besiedelt werden mussten, sondern aus einem ehemals zusammenhängenden gleichförmigen Lebensraum mit einheitlichem Artenbestand hervorgegangen sind. Arten und die Fragmente werden in einer 1/0 Matrix so angeordnet, dass die Matrix sukzessive von links oben nach rechts unten durch vorhandene Arten aufgefüllt wird. Gebiete werden in Reihen und Arten in Spalten aufgeführt. Die Anordnung der Gebiete und Arten reflektiert dann ein Maß für die Eignung der Gebiete und die Anfälligkeit der Arten gegenüber Fragmentation. Dieses Konzept wird illustriert und genutzt um: 1. die Mindestgröße überlebensfähiger Populationen von Lemuren in Madagaskar zu bestimmen; 2. festzustellen, ab welcher Größe Waldreste von Reptilien Madagaskars als “Wald” erkannt werden; 3. zu überprüfen, ob das System von Schutzgebieten im Stadtgebiet Hamburgs für Vögel ein Verbundsystem oder isolierte Lebensräume darstellt; 4. zu analysieren, welche Eigenschaften (life history traits) bewirken, dass einige Vogelarten anfälliger gegenüber Fragmentation von Waldlebensräumen im Stadtgebiet sind als andere. Die Analyse der Vogelgemeinschaften im Stadtgebiet Hamburgs ergab, dass die Schutzgebiete von den Vögeln Inselcharakter haben. Grünachsen agieren nicht als Korridore, die freien Austausch aller Arten erlauben. Die Anfälligkeit von Vogelarten in Wäldern oder Waldrändern ist umgekehrt proportional zu ihrer Fähigkeit, die die Schutzgebiete umgebende Stadt zu nutzen und zu der Masse an Material, das sie in Eischalen investieren müssen. Arten mit großen Gelegen sind eher in kleinen Gebieten im Stadtzentrum vertreten als Arten mit kleinen Gelegen. Analyse der Schachtelung von Gemeinschaften kann ein wertvolles Werkzeug zur Unterstützung von Entscheidungsfindungen im Naturschutz sein, wenn langfristige Datenreihen oder detaillierte Untersuchungen nicht verfügbar sind. Die hauptsächliche Bedeutung dieser Analysen liegt aber darin, fokussierte Hypothesen zu entwickeln, die dann überprüft werden können. Key words: Nested pattern – minimum viable populations – urban ecology – fragmentation – lemurs – reptiles – birds – phylogenetic contrasts

Introduction Understanding patterns in the distribution and abundance and the underlying factors that generate these patters are prime goals of ecology (Krebs 1994). In order to distinguish species associations assembled at random from communities structured by limiting constraints, appropriate null models have to be formulated (Colwell & Winkler 1984; Gotelli & Graves 1996; Gotelli 2000). The sometimes rather academic debate over the appropriate assumptions of these models fostered insecurity about their validity and the usefulness of null models in applied conservation biology. Yet, defining a goal in quantitative terms and being able to quantify deviations from it in a statistically rigorous manner will increase in importance also in applied conservation biology as conflicts of interest between economy and ecology will augment. At present, fragmentation of suitable habitats by human action is one of the most prevalent impacts acting upon and modifying natural species assemBasic Appl. Ecol. 2, 1 (2001)

blages (e.g. Noss & Csuti 1997; Brokaw 1998; Ranta et al. 1999; Bolger et al. 2000; Lomolino & Perault 2000). Since there are many more species on earth than we can possibly study, it is important to arrive at some generalized understanding what makes certain species more vulnerable to fragmentation than others. From a community perspective, this can be approached on a functional level (e.g. Kruess & Tscharntke 1994; Steffen-Dewenter & Tscharntke 1999). On a species level, the question can be addressed by analyzing live history traits in a macroecological (e.g. Brown 1995; Böhning-Gaese 1997; Davies et al. 2000) or comparative approach (Harvey & Pagel 1991). The questions associated with fragmentation studies are therefore: 1. Does fragmentation result in predictable patterns in the composition of the resulting species assemblages, thus allowing to rank species according to their vulnerability to fragmentation? 2. Which species are most sensitive to fragmentation? 3. Which life history traits or combinations thereof are related to different degrees of vulnerability?

Nested patterns in conservation biology

For situations where some kind of fragmentation has played a major role for the composition of communities, the concept of nestedness has proved useful for describing the present situation and to develop hypotheses on how the status quo came about (Patterson & Atmar 1986, 2000; Patterson 1987, 1990; Patterson & Brown 1991; Cutler 1991; Kadmon 1995; Ganzhorn 1998). In the present paper we will review the concept of nested species assemblages as proposed by Patterson & Atmar (1986) and developed further by Cutler (1991) and Atmar & Patterson (1993; Patterson & Atmar 2000). In the second part insights from analyses of nestedness will be linked to life history traits to arrive at a new level of generalized understanding about limiting factors that might be relevant to questions in conservation biology.

Nested species assemblages Within a given region different communities are likely to contain different numbers of species. Often communities of lower species numbers are subsets of more diverse assemblages that do not contain species that are not present in the more species-rich communities. In perfectly nested subsets one or more species are lost at a time as communities decline in species number and none of the species lost from the richer communities reappears in one of the species poor communities once it was lost. Nested subsets have been described for all kinds of communities (e.g., Patterson & Atmar 1986; Lomolino et al. 1989; Cutler 1991; Wright & Reeves 1992; Langrand & Wilmé 1997; Kadmon 1995; Ganzhorn 1998; Patterson 1987, 1990; Cook 1995; Wright et al. 1998). A prerequisite for nestedness is that communities share a common biogeographic history, live in similar contemporary environments and have hierarchical niche relationships (Patterson & Brown 1991).

Evolution of nested subsets In principle, nested subsets can come about by two different processes: species-specific colonization and/or dispersal potential and species-specific vulnerability to extinction (Patterson & Atmar 1986). If nested subsets are generated via colonization, species colonize sites at various distances from a common source that contains all the species available. Very mobile species or species with high dispersal capacities will cover longer distances than less mobile species. All else being equal this results in nested subsets. Similarities between sites in their species composition (called “species similarities”) will show a strong negative relation to the distance between sites (Fig. 1A).

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Nested subsets can also be generated through extinction. If a region with a more or less uniform species pool is subject to change that results in some sort of fragmentation (pysical fragmentation, increased habitat variability due to environmental change of whatever sort), species will drop out of the local communities according to descending susceptibility to habitat change. The resulting local communities will then represent nested subsets of the original regional community. In contrast to the previous scenario, coefficients of species similarities are independent of the distance between sites (Fig. 1B). Under the colonization model species similarities are only negatively correlated with the distance between sites when the colonization is unidirectional from one source habitat. In cases where areas are colonized in various directions from a common species pool the species similarity indices may not be correlated negatively with the distance between sites but with the distance of the sites and the source area (Fig. 1D). Other scenarios under the colonization assumption include different colonization capacities of different species not only in relation to distances but also in relation to environmental gradients (altitude, humidity, etc.), resulting in discontinuities in the species presence/absence matrix (e.g., Ganzhorn 1998). In reality, nestedness occurs more frequently in habitat fragments subject to faunal relaxation, i.e. where species originating from a larger species pool face different probabilities to go extinct. This is the case in landbridge islands or habitats fragmented by human activities. Oceanic islands that originated “de novo” and that were colonized from a mainland pool of species are much less likely to exhibit nested species assemblages (Patterson & Atmar 1986; Wright et al. 1998), though effects of isolation and different dispersal abilities interact (Patterson 1990; Kadmon 1995).

Test for nestedness: the null models In their first version, Paterson & Atmar (1986) proposed two models to test whether different communities with different numbers of species show nested structure or not. For this, communities were ordered in a presence-absence matrix. Species were listed in columns, site were listed as rows. The most speciesrich community was listed in the first row. The community with the least species was listed last. All other spcies were arranged between the two extremes in a way that maximized nestedness. (Note that the data shown in Tables 1–3 were transposed to have sites in columns and species in rows. This differs from the original matrix used to calculate the degree of nestedness. The data were transposed to fit them on the Basic Appl. Ecol. 2, 1 (2001)

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A: Colonization

B: Faunal Relaxation

C: Colonization and/or Extinction

D: Multidirectional Colonization from a Source

E: Colonization from a Source along an Environmental Gradient

Basic Appl. Ecol. 2, 1 (2001)

page). Deviations from perfect nestedness were calculated as follows: 1. the community with the lowest species richness in which species i occurs was determined; 2. all the richer faunas listed in the rows above were examined for the presence of species i; 3. the number of absences of species i in the richer faunas was counted; 4. the counts were summed across all species (Patterson & Atmar 1986). In a perfectly nested set of communities this index of nestedness (number of holes in the matrix) would equal “0”. This case is represented by the communities pictured in Figure 1. Deviation from nestedness are characterized by values > 0. In their fhe first null models the same number of communities with the same number of species were generated by Monte Carlo simulations. Once a complete matrix had been generated by a Monte Carlo simulation, the index of nestedness was calculated as listed above. This procedure was replicated e.g. for 1000 times. The indices of nestedness based on the Monte Carlo simulations were then characterized by their mean and standard deviation. The probability that the index found in the empirical sample occurs in communities assembled at random could then be calculated based on z-transfromations, t-tests, or other statistics. In these models all species had equal probability of occuring at any of the sites included in the analysis (RANDOM0) or the probability that a given species was drawn in the Monte Carlo simulation was proportional to its occurence in the real communities (RANDOM1). In a later version the null model was refined to account for various shortcomings of the previous models though the principle remained similar (Atmar & Patterson 1993). The main advantages of the new model is that the measure of nestedness is standardized by the number of species and sites and therefore it is independent of the size of the presence-absence matrix. In addition different presences or absences of different species are weighed differently depending on how unexpected their occurence or absence is at a

Fig. 1. Possible ways to nested patterns in species assemblages; different letters represent different species (modified from Ganzhorn 1999). Numbers below the figures are Jaccard’s indices between sites. A: Nested patterns due to colonization is based on dispersal or migration capacity and unrelated to fragment size. B: Nested pattern due to faunal relaxation (extinction) in fragments of decreasing size. C: Nested pattern due to faunal relaxation and/or different colonization potential. D: Nested pattern but no distance effect of species similarities between sites due to multidirectional colonization. E: Discontinuities in nested pattern due to differential colonization potential along an environmental gradient.

Nested patterns in conservation biology

given site. In this model the index of nestedness is replaced by an index of “unexpectedness” that is linked to entropy as a measure of energy inherent in disorder. Since disorder of a system can be related to temperature, the new index of nestedness was defined as the “temperature” (“T”) of the system. Low temperatures represent highly ordered states, i.e. nested subsets, while high temperatures represent “disordered” matices with many unexpected occurences or absences of species. The refined model has been described in detail by Atmar & Patterson (1993) and Patterson & Atmar (2000). It can be downloaded for free from .

Applications of the concept of nestedness to conservation questions Empirical evaluation of the vulnerability of species and defining minimum viable populations Analysis of nested species assemblages in habitat fragments can be used to assess the size of minimum viable populations. This has been applied to lemurs in Madagascar. Lemur comunities in forest fragments are organized as almost perfectly nested subsets (Ganzhorn 1994; Ganzhorn et al. 1999a, 2000; Table 1). This strong pattern allows to define the size threshold of fragments at which a given species will be lost in a defined period of time with high degree of confidence. Since average densities of different lemur species are known (Ganzhorn et al. 1999b), we can calculate the number of animals that are likely to have inhabited the fragments at the time of isolation. Based on this analysis of empirical data the size of a lemur population that is able to survive in a forest fragment for 20–40 years is about 35–40 adult animals. When we enter this population size into the allometric regression between body mass, density and sur-

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face requirements, we realize that none of the littoral forest fragments of Madagascar and only a handful of forest blocks of the dry deciduous forest are still large enough to maintain the larger lemur species for the next 20–40 years (Ganzhorn et al. 2000). Here, analyses of nestedness provided a simple but powerful statistical basis for far reaching conclusions on the future of one of the earth’s biodiversity hotspots (Myers et al. 2000). Perception of animals: when is a forest a forest? In a longterm monitoring program on the distribution of amphibians and reptiles in littoral forest fragments of Madagascar, Ramanamajato (2000) demonstrated very clear nested patterns in amphibian and reptile assemblages in these fragments. When he distingushed ubiquitous and forest species, he found that the communities of small forests consisted only of generalized species. Forest dependent species appeared only in forests above a certain size class. According to his data, forest dependent amphibians require forests of more than 41 ha while the first forest dependent reptile species turned up in fragments of 20 ha (illustrated for reptiles in Table 2). In his dataset on reptiles there seem to be two thresholds with respect to fragment size. The first is at about 20 ha when the first set of forst species are found. The second threshold seems to be around and slightly above 200 ha where another set of new reptile species occurs. In this example, analyses of nestedness is probably the most elegant way to make the pattern comprehensible and to illustrate that the pattern is not due to simple species-area relationships but to site and species-specific characteristics that render certain species more vulnerable to fragmentation than others. As in the analyses above, the niceties of this analysis is that vulnerability can be quantified and linked to species or site characteristics.

Table 1. Lemur species in different sized evergreen forests of Madagascar. Mspp. = Microcebus sp., Cspp = Cheirogaleus spp.; Mc = Mirza coquereli, Pf = Phaner furcifer, Lsp. = Lepilemur spp., Ef = Eulemur fulvus, Er = Eulemur rubriventer, Vv = Varecia variegata; Hg = Hapalemur griseus; Al = Avahi laniger; Pspp = Propithecus spp., Ii = Indri indri; Dm = Daubentonia madagascariensis (from Ganzhorn et al. 2000). Size [ha]

>>

>>

>>

675

457

221

206

188

152

42

41

28

20

Mspp Cspp Hg Al Ef Lspp Dm Pspp Er Vv Ii

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+ + + + + + + +

+ + + + + + +

+ + + + + +

+ + + + +

+ + + + +

+ + + + +

+ + + + +

+ + + + +

+ +

+ + +

+ +

+ +

+

>> indicates forest blocs of several thousand hectars without precise boundaries.

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Table 2. Distribution of reptiles within fragments of littoral forest in southern Madagascar. Specialisation: Forest (F) – Species restricted to continuous tracts of disturbed or intact forest; Generalist (G) – Species that use forest and open habitats (from Ramanamanjato 2000). Fragment size [ha]

457

295

244

221

212

206

191

41

28

20

10

Specialisation

Mabuya elegans Mimophis mahfalensis Hemidactylus mercatorius Phelsuma modesta Chamaeleo lateralis Chamaeleo oustaleti Liopholidophis lateralis Mabuya gravenhorsti Madagascarophis colubrinus Phelsuma lineata Amphiglossus melanopleura Geckolepis maculata Phelsuma quadriocellata Liophidium torquatus Lygodactylus tolampyae Typhlops arenarius Amphiglossus macrocercus Ithycyphus oursi Amphiglossus ornaticeps Boa dumerili Boa manditra Leioheterodon madagascariensis Liophidium rhodogaster Liophidium vaillanti Lycodryas gaimardi Amphiglossus melanurus Langaha madagascariensis Phelsuma antanosy Pseudoxyrhopus kely Amphiglossus astrolabi Brookesia nasus Chamaeleo nasuta Ithycyphus goudoti Liopholidophis stumpffi Liopholidophis sexlineatus Zonosaurus aenus Zonosaurus maximus Lycodryas arctifasciatus Lycodryas betsileanus Lygodactylus miops Uroplatus sikorae

1 1 1 1 1 1 1 1 1 1 1 1 1 1

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1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1

1 1 1 1 1

1 1

1

1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

G G G G G G G G G G F F F F F F F F F G F G F G F F F F F A F F F F F G G F F F F

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

1 1 1 1 1 1

1

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1 1

1 1

1 1 1 1 1

1 1

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1

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1 1

1 1 1 1

SLOSS debate and evaluating habitat connectivity: the case of the city of Hamburg Some time ago there was substantial debate whether biodiversity is preserved better by protecting a single large or several small sites (SLOSS debate; reviewed e.g. by Hunter 1996). This discussion has become obsolete for most parts of the world where we simply have to preserve whatever is left. Nevertheless, analyses of nestedness can clearly provide guidance, how different taxa, ecosystems or biospheres respond to fragmentation and how as many species as possible can be preserved in regions under increasing human Basic Appl. Ecol. 2, 1 (2001)

1

1

1

1 1 1 1 1 1 1 1

pressure. This question is of importance also to urban planners and will be illustrated below. According to official policy (Umweltbehörde Hamburg 1989), the fast growing city of Hamburg intends to preserve as much of the original biodiversity within their city boundaries as possible. This is attempted by a network of protected areas and green axes or corridors that are supposed to link different protected areas and to provide a matrix that is suitable for the exchange of individuals (Fig. 2). Analyses of nestedness provides a simple tool to test whether this concept is operational. For species for which this network of protected areas, parks and

Nested patterns in conservation biology

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Fig. 2. Location of study sites and other green areas within the city limits of Hamburg (from Umweltbehörde Hamburg 1997). Dark shaded areas represent parks including forests and woodland. Lightly shaded areas represent water.

green corridors works, we would expect random distribution of species within a given habitat type and no structured pattern. Rather the species should turn up in the samples as if drawn at random from a uniform species pool. For groups for which the network is not operational, we expect nested patterns. These hypotheses were tested with data compiled by Eisenbeiß (2000) from published and unpublished survey reports of the avifauna at selected sites within the city limits of Hamburg, Germany. These results were condensed into presence-absence matrices. For the present analysis ubiquitous species and species related to forest and forest edge were used. Combining

the two groups of species into a single matrix indicates a highly nested structure of the avifauna in habitat fragments within the city limits (T = 17.63, p < 0.001; Table 3). When the matrix is subdivided and presence-absences of ubiquitous (i.e. generalist species occurring in allkinds of terrestrial habitats) and forest/forest edge species are analyzed separately, the matrix for ubiquitous species does not show any nested structure (T = 15.98, not significant; “T” refers to “Temperature” of the matrix as a measure of disorder or enthropy; it is unrelated to “t” of the t-tests; see Atmar & Patterson 1993; Patterson & Atmar 2000). BasicalBasic Appl. Ecol. 2, 1 (2001)

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G M P N O C 1 K H K L D

Rhee

D B

Eppendorfer Moor

M I E

Flotbektal

G

Volksdorfer Teichwiesen

N O F H A

Rodebeker Quellental

E P

Reit

C F

u u u u u u u u u u u u u u f u f f f f u f f f f u f f f f f f f f f f f f f f f f f

Raakmoor

A B L

Höltigbaum

Turdus merula Parus caeruleus Fringilla coelebs Prunella modularis Parus major Sylvia atricapilla Turdus philomelos Troglodytes troglodytes Phylloscopus collybita Sturnus vulgaris Phylloscopus trochilus Muscicapa striata Erithacus rubecula Sylvia borin Certhia brachydactyla Turdzs viscivorus Hippolais icterina Sylvia communis Aegithalos caudatus Coccothraustes coccothraustes Carduelis chloris Phylloscopus sibilatrix Sitta europaea Parus palustris Parus montanus Passer montanus Sylvia curruca Emberiza citrinella Pyrrhzla pyrrhula Anthustruvuakus Oriolus oriolus Ficedula hypoleuca Luscinia megarhynchos Locustella fluviatilis Parus ater Carduelis carduelis Regulus regulus Certhia familiaris Lanius collurio Regulus ignicapillus Loxia curvirostra Serinus serinus Parus cristatus

Boberger Niederung

Size [ha]

Duvenstedter Brook

Occurrence

Species pairs

Table 3. Occurrence of passerine bird species (without Corvidae) in selected areas within the city limits of Hamburg. Sites and species were ordered to maximize the order according to the assumptions of nestedness. Species pairs: Same letters indicate pairs used in pairwise comparisons to account for phylogenetic effects. Occurrence: u = ubiquitous species; f = species of forest, forest edge and woodland.

780

350

260

18

48

47

39

7

15

18

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

1 1 1 1

1

1 1 1 1

1 1 1

1 1 1 1 1 1 1

1 1 1

1 1

1 1 1 1 1

1 1

1 1 1 1

1 1 1

1 1 1 1

1

1 1 1 1

1 1

1 1 1 1

1 1 1

1 1

1

ly any of these species can occur anywhere and the sample of species that are recorded as being present in any one of the fragments does not deviate from samples drawn from random distributions. For this group of species it seems to be a matter of chance whether a Basic Appl. Ecol. 2, 1 (2001)

1 1

1

species is encountered at a given site or not. In contrast, forest species and species of the forest edge showed nested subsets that differed highly significantly from randomness (T = 21.93, p < 0.001). Thus, the nested structure of bird communities in forested pro-

Nested patterns in conservation biology

tected areas within the city limits of Hamburg is due to the distribution of these forest and forest edge species. Since different species drop out in a predictable sequence, the present system of protected areas and corridors does not act as a network that can simulate continuous forest cover. Rather, the protected areas within the city limits of Hamburg represent islands of forest in an urban matrix that is unsuitable for the forest dependent species. As mentionned above, nestedness occurs more frequently in habitat fragments subject to faunal relaxation, i.e. where species originating from a larger species pool face different probabilities of extinction. Islands that are colonized from a mainland pool of species are much less likely to exhibit nested species assemblages (Patterson & Atmar 1986; Wright et al. 1998). The high degree of nestedness of the avifauna in forest remnants of Hamburg therefore indicates that the present distribution of bird species is more likely to have been generated by selective extinction of sensitive species from a common species pool than by colonization from the surrounding environment. Generating hypotheses on the causes for vulnerability The concept of nestedness describes a situation and not a process. Yet, the assumptions used to pack the matrix and rank sites and species in the presence-absence matrix allows to derive hypotheses which factors might be associated with the habitat suitability of the different sites and with the vulnerability of the different species. Site characteristics: Before assessing factors related to the vulnerability of species, it is important to understand the criteria that led to the ranking of the different sites in the matrix. According to the assumption of the model, the species and sites are arranged in a way to maximally pack the matrix. Maximal packing is achieved when the measure of unexpected species presences and absences is minimized. This means that the ranking of sites is defined by patterns in the species distribution rather than by our man-made perception of what habitat characteristics might be important for the animals in question. Sites on the left site of the matrix are the most suitable for the set of species considered since they can house most species. As we go from left to right, the suitability of the sites to accomodate large species numbers declines. The present model of nestedness considers extinction and colonization as possible factors contributing to different species composition at different sites. Extinction is likely to be linked to the size of the fragments while colonization is a function of the distance to the source of colonization. In the study of Hamburg’s avifauna these two factors are highly correlat-

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Table 4. Spearman’s correlation coefficients between the position of study sites in the data matrix, the size of study sites and their distance to the next forest larger than 100 ha. N = 10 for all comparisons. ** p = 0.01; *** p = 0.001. Distance to nearest forest Position of the site in the data matrix Number of species Fragment size

0.84*** –0.82** –0.72**

Fragment size

Number of species

–0.84***

–0.98***

0.78**

ed. (Table 4). Fragments at the periphery are larger than fragments in the center of the city. Since large tracts of forest that could serve as sources for colonizing individuals are located mainly at the periphery, the distance of the study sites to the nearest block of forest larger than 100 ha is inversely related to fragment size. Small fragments are located towards the center of town, further away from potential colonization areas than larger fragments. Multiple regression on the log-transformed variables was used to separate the effects of the size of a fragment and its distance to the nearest possible source of colonization. According to this analysis, the position of a site in the matrix was related significantly to the size of the study site but not to its distance to the nearest larger bloc of forest (Position of site in the matrix = –1.67 * log Size + 12.17; R2 = 0.73, p = 0.017; the residuals did not deviate from a normal distribution). Thus, the size of the forest is more important for the nested pattern of the avifauna in the isolated protected areas within the city limits of Hamburg than the distance to the nearest possible source of colonization. This follows classical island biogeography where the number of species decreases as the areas considered decrease in size (for central Europe see e.g. Reichholf 1980; Banse & Bezzel 1984). For future studies, however, the distance effect reflecting an urban gradient from the periphery to the center of town should not be ruled out (see Denys & Schmidt 1998). This is illustrated by the fact that, despite a significant correlation between the number of species present and the size of the site, the sites in the maximally packed matrix are not ranked according to their size (Table 3). Furthermore, the correlation coefficient between the number of species and the distance to the nearest larger forest block is higher than the coefficient between species number and the size of the site (Table 4). Though the correlation coefficient between the number of species present at a site and the rank of the site in the packed matrix is very high, the differing correlation coefficients indicate that nestedness and number of species represent different information that need to be addressed separately. Unfortunately, the Basic Appl. Ecol. 2, 1 (2001)

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design of the present study does not allow to separate the potential impact of size and distance of fragments. Species characteristics: The previous analyses indicated that the pattern in bird communities in the protected areas of Hamburg is a consequence of habitat fragmentation. Given this result, the order of species in the maximally packed matrix can be considered a measure indicating the vulnerability of any given species to extinction. Species on top of the matrix have a low rank that indicates low vulnerability. While species at the end of the matrix are characterized by a high rank that reflects high vulnerability to fragmentation. One of the goals of this paper was to describe patterns beyond the taxonomic level that would allow to identify the characteristics or set of characteristics that make any given taxon more vulnerable to fragmentation than others. In order to arrive at this more generalized level of interpretation, life history traits were compiled for the species used in the analyses. The traits used are: Body mass, Maximum age, Age at first breeding, Maximum density in natural habitats, Isolation effect of buildings (breeding in buildings = 1, gardens and open woodlands [Schrebergärten] = 2, forest = 3), Clutch size, Number of broods per year, Fresh mass of one egg, Mass of one egg-shell, and Height of nest location. Data were taken from Bezzel (1993) and Glutz von Blotzheim & Bauer (1985–1997). Using these variables, the following variables were calculated to quantify investments in reproduction: 1. Relative investment per egg mass: Mass of one egg/log Body mass; 2. Relative investment per egg-shell: Mass of the shell of one egg/log Body mass; 3. Total mass of fresh egg material per year: Fresh egg mass of one egg * Clutch size * Number of broods; 4. Total mass of egg-shells per year: Mass of the shell of one egg * Clutch size * Number of broods; 5. Total mass of egg-shell per fresh egg mass per year: Mass of the shell of one egg * Clutch size * Number of broods/Fresh mass of one egg; 6. Relative investment in egg mass per year: Fresh mass of one egg * Clutch size * Number of broods/log Body mass; 7. Relative investment in egg-shells per year: Mass of the shell of one egg * Clutch size * Number of broods/log Body mass. Nest sites (hole, open nests) and migration habits (year round resident or migratory) were analyzed separately as nominal data. The continuous variables were used in multiple regression analysis using the position of the bird species in the nested matrix as dependent variable that indicates vulnerability to fragmentation. A model was Basic Appl. Ecol. 2, 1 (2001)

Table 5. Multiple regression analysis using the position of the species in the matrix as dependent variable and life history traits as independent variables. N = 41 species. Independent variable

t

p

Isolation Clutch size Relative investment per egg-shell Total mass of egg-shells per fresh egg mass Total mass of egg-shells per year Relative investment in egg-shells per year

6.49 –2.20 1.65 2.35 1.16 –1.85

<< 0.001 0.029 0.108 0.024 0.253 0.072

chosen to maximize R2 (Winstat 1991; Table 5). According to the multiple regression model, vulnerability to fragmentation is related primarily to the fact whether or not the species can use the anthropogenic urban matrix as breeding habitat. Clutch size scores negatively to vulnerability. Species with large clutches are less affected by fragmentation than species with smaller clutches. Vulnerability is then positively related to the egg-shell requirements per unit egg mass. Species requiring more shell material per unit egg mass are more vulnerable to fragmentation in an urban context than other species. None of the other variables were significant in the present model. This model explains some 68% of the variability in the position of the species in the presence-absence matrix. Though the original values of the dependent variable deviate significantly from a normality, the residuals do not differ from a normal distribution. In single factor analyses, the migratory status and nest location (both nominal variables) were not related to the rank of the species in the maximally packed matrix. Phylogenetic contrasts So far each bird species entered the analysis as an independent unit. This approach may introduce errors due to phylogenetic inertia. Some life history traits might have evolved in a taxon (such as species combined in a genus) in response to very different constraints in a very different environmental context. Assuming that this genus would be species-rich and would be positionned at a certain location in the nested data matrix due to different characteristics, not considered in the analyses, these life history traits would be represented disproportionally in the posthoc correlation analyses and could possibly produce false interpretations. In the present dataset there is no significant relationship between bird families and the rank of the species in the maximally packed matrix (Kruskal-Wallis analysis of variance: p > 0.05). Nevertheless, the results could be interpreted with much more confidence, if phylogenetic relationships were

Nested patterns in conservation biology

incorporated. Various methods have been proposed to account for these phylogenetic relationships, such as phylogenetic contrasts (Harvey & Pagel 1991; Purvis & Webster 1999) or nested or hierarchical analyses of variance (Ricklefs & Starck 1996). The phylogentic contrasts require an hierarchical phylogeny of the species in question. Hierarchical analyses of variance need sufficiently large numbers of units within each higher taxonomic level to allow statistically meaningful analyses of variance. An elegant and simple solution to circumvent the controversies over the appropriate comparative methods but still to account for phylogenetic inertia in the analysis of life history traits has been proposed by Møller & Briskie (1995). They proposed to pair sister taxa and compare the differences in the values of life history traits between these pairs in relation to some dependent variable using a Wilcoxon matched-pairs signed-ranks test. In the present example, species of the same genus or of the same family but different ranks in the nested matrix were paired. The second species of each pair had a higher rank in the packed matrix. When a species could be matched with several other species, the second species was drawn at random. Using these pairs, pairwise comparisons were run for the three factors that contributed significantly to the multiple regression model listed in Table 5. In single factor analyses, the second species of each pair (i.e. the species lower down in the matrix, reflecting higher vulnerability to fragmentation) had significantly higher values of “Isolation” (one-tailed Wilcoxon tests: z = 2.58, p = 0.005). The other life history traits (“Clutch size” and the “Total mass of egg-shells per fresh egg mass”), that were significant in the multiple regression, model did not differ in these pairwise comparisons significantly. Based on this analysis species, that are capable to use the matrix around the fragments are less vulnerable to fragmentation than other species. The present analysis could not answer the question which life history traits may be linked to the fact that some species can use the matrix surrounding the forest fragments better than others, though some factors associated with the production of egg-shells seem to be involved.

Test of the conclusions derived from the nested pattern of the bird communities within the city of Hamburg The null models underlying the analyses of nestedness are based on patterns but do not analyse the processes that lead to these patterns. Yet, processes can be infered from the observed patterns. Studying processes is much more time consuming because they extend

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over time while patterns represent the situation at a given point in time. Thus, it would be highly desireable to be able to predict processes from patterns. In the case of the bird communities in woodlands within the city limits of Hamburg, we concluded, based on the significant nestedness of the different communities, that the different protected areas within the city limits are not connected effectively to act as a network of suitable habitat for woodland species. Rather the different areas resemble islands with limited possibilities for recolonization once a species went extinct. One prediction that follows from this hypothesis is that species at the bottom of the presence/absence matrix sorted to maximize nestedness (such as listed in Table 3) are the species that are more likely to go extinct from a pool of species in the process of faunal relaxation once the possibilities to exchange individuals between suitable areas has been limited. Thus, if the observed pattern of nestedness came about by differential extinction of fragmentation sensitive species, the species at the bottom of Table 3 should have declined on a large scale within the city limits of Hamburg since fragmentation has been enforced through the city construction during the last decades. This prediction can be tested with datasets on the longterm population dynamics of breeding birds in Hamburg (Mitschke et al. 2000). Mitschke et al. (2000) compared population densities of birds in 15 census plots within the city of Hamburg documented between 1961–1975 and again at the same 15 plots between 1990–1998. The number of plots where any given species increased minus the numberof plots where the species decreased between the two inventories provides a measure of the longterm population dynamics of the species in question. This measurement of the longterm population dynamics could be derived for 16 bird species listed by Mitschke et al. (2000) and in Table 3 of the present paper. The rank position of these 16 bird species in Table 3 is correlated significantly with the number of plots where any given species increased minus the numberof plots where the species decreased between the two inventories listed (rs = –0.48, p = 0.029, n = 16; Fig. 3). The significant correlation supports the hypothesis that processes can be derived from nested patterns. Yet, the correlation coefficient and the significance level is not very high. This is primarily due to the fact that four of the widespread species at the top of the matrix showed substantial population declines over the last few decades, even though they are still widespread. These species are Turdus philomelos, Phylloscopus trochilus, Muscicapa striata, and Hippolais icterina. The decline of these species ought to have other causes than the decline of species positionned further down in the matrix (see also Simberloff & Martin 1991). Basic Appl. Ecol. 2, 1 (2001)

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Ganzhorn and Eisenbeiß obscure reports. The Gesellschaft für ökologische Planung, the Naturschutzbund Deutschland, and the Bund für Umwelt- und Naturschutz provided additional data. Financial support for assessing the role of corridors and protected areas in the city of Hamburg is provided by the Hanseatische Natur- und Umweltinitiative.

References

Fig. 3. Relationship between the position of bird species in the species presence/absence matrix arranged to maximize nestedness (Table 3) and changes in population densities in 15 permenent plot in Hamburg over 20–30 years (data from Mitschke et al. 2000). Changes in population densities were recorded as the number of plots where any given species increased minus the number of plots where the species decreased between the two inventories.

Nevertheless, analyses of nested patterns can provide valuable approximations for situations where longterm data are not available or where there is not enough time for longterm studies before any decision for conservation action has to be taken. Thus, the value of analyses of nested pattern seems primarily to develop hypotheses that then have to be tested with more detailed studies (e.g., Junker-Bornholdt & Schmidt 2000; Schwarz & Flade 2000; Witt 2000).

Conclusions Analyses of nested subsets describe states of species assemblages. The analyses do not assess processes. But processes can be inferred from patterns characterizing these states. One of the main advantages of this concept lies in its capacity to order large datasets of species assemblages in a way that ranks species according to their vulnerability to fragmentation and sites according to their suitability for large species numbers. The null model allows then to quantify probabilities of deviation from random extinction, and greatly facilitates the formulation of hypotheses to study the causalities (environmental or life history characteristics) that contributed to the observed patterns in species distributions. Acknowledgements. We thank Dr. Dube, Dr. K. Wehrmann and the staff of the Umweltbehörde Hamburg for their support in logistic issues and locating sometimes

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