982 research outputs found
Statistical mechanics of ecosystem assembly
We introduce a toy model of ecosystem assembly for which we are able to map
out all assembly pathways generated by external invasions. The model allows to
display the whole phase space in the form of an assembly graph whose nodes are
communities of species and whose directed links are transitions between them
induced by invasions. We characterize the process as a finite Markov chain and
prove that it exhibits a unique set of recurrent states (the endstate of the
process), which is therefore resistant to invasions. This also shows that the
endstate is independent on the assembly history. The model shares all features
with standard assembly models reported in the literature, with the advantage
that all observables can be computed in an exact manner.Comment: Accepted for publication in Physical Review Letter
Global protected area impacts
Protected areas (PAs) dominate conservation efforts. They will probably play a role in future climate policies too, as global payments may reward local reductions of loss of natural land cover. We estimate the impact of PAs on natural land cover within each of 147 countries by comparing outcomes inside PAs with outcomes outside. We use ‘matching’ (or ‘apples to apples’) for land characteristics to control for the fact that PAs very often are non-randomly distributed across their national landscapes. Protection tends towards land that, if unprotected, is less likely than average to be cleared. For 75 per cent of countries, we find protection does reduce conversion of natural land cover. However, for approximately 80 per cent of countries, our global results also confirm (following smaller-scale studies) that controlling for land characteristics reduces estimated impact by half or more. This shows the importance of controlling for at least a few key land characteristics. Further, we show that impacts vary considerably within a country (i.e. across a landscape): protection achieves less on lands far from roads, far from cities and on steeper slopes. Thus, while planners are, of course, constrained by other conservation priorities and costs, they could target higher impacts to earn more global payments for reduced deforestation
Modeling the evolution of weighted networks
We present a general model for the growth of weighted networks in which the
structural growth is coupled with the edges' weight dynamical evolution. The
model is based on a simple weight-driven dynamics and a weights' reinforcement
mechanism coupled to the local network growth. That coupling can be generalized
in order to include the effect of additional randomness and non-linearities
which can be present in real-world networks. The model generates weighted
graphs exhibiting the statistical properties observed in several real-world
systems. In particular, the model yields a non-trivial time evolution of
vertices properties and scale-free behavior with exponents depending on the
microscopic parameters characterizing the coupling rules. Very interestingly,
the generated graphs spontaneously achieve a complex hierarchical architecture
characterized by clustering and connectivity correlations varying as a function
of the vertices' degree
Weighted evolving networks: coupling topology and weights dynamics
We propose a model for the growth of weighted networks that couples the
establishment of new edges and vertices and the weights' dynamical evolution.
The model is based on a simple weight-driven dynamics and generates networks
exhibiting the statistical properties observed in several real-world systems.
In particular, the model yields a non-trivial time evolution of vertices'
properties and scale-free behavior for the weight, strength and degree
distributions.Comment: 4 pages, 4 figure
Search in weighted complex networks
We study trade-offs presented by local search algorithms in complex networks
which are heterogeneous in edge weights and node degree. We show that search
based on a network measure, local betweenness centrality (LBC), utilizes the
heterogeneity of both node degrees and edge weights to perform the best in
scale-free weighted networks. The search based on LBC is universal and performs
well in a large class of complex networks.Comment: 14 pages, 5 figures, 4 tables, minor changes, added a referenc
Analytical solution of a model for complex food webs
We investigate numerically and analytically a recently proposed model for
food webs [Nature {\bf 404}, 180 (2000)] in the limit of large web sizes and
sparse interaction matrices. We obtain analytical expressions for several
quantities with ecological interest, in particular the probability
distributions for the number of prey and the number of predators. We find that
these distributions have fast-decaying exponential and Gaussian tails,
respectively. We also find that our analytical expressions are robust to
changes in the details of the model.Comment: 4 pages (RevTeX). Final versio
Quantifying the connectivity of a network: The network correlation function method
Networks are useful for describing systems of interacting objects, where the
nodes represent the objects and the edges represent the interactions between
them. The applications include chemical and metabolic systems, food webs as
well as social networks. Lately, it was found that many of these networks
display some common topological features, such as high clustering, small
average path length (small world networks) and a power-law degree distribution
(scale free networks). The topological features of a network are commonly
related to the network's functionality. However, the topology alone does not
account for the nature of the interactions in the network and their strength.
Here we introduce a method for evaluating the correlations between pairs of
nodes in the network. These correlations depend both on the topology and on the
functionality of the network. A network with high connectivity displays strong
correlations between its interacting nodes and thus features small-world
functionality. We quantify the correlations between all pairs of nodes in the
network, and express them as matrix elements in the correlation matrix. From
this information one can plot the correlation function for the network and to
extract the correlation length. The connectivity of a network is then defined
as the ratio between this correlation length and the average path length of the
network. Using this method we distinguish between a topological small world and
a functional small world, where the latter is characterized by long range
correlations and high connectivity. Clearly, networks which share the same
topology, may have different connectivities, based on the nature and strength
of their interactions. The method is demonstrated on metabolic networks, but
can be readily generalized to other types of networks.Comment: 10 figure
Birds, Montane forest, State of Rio de Janeiro, Southeastern Brazil
Field surveys in montane Atlantic forest of Rio de Janeiro state, Brazil, provided a list of 82 bird species in four sitesvisited. Our protocol relied on standardized use of mist nets and observations. The birds recorded include 40 Atlanticforest endemics, three globally and two nationally Vulnerable species, and two regionally Endangered species. Data onspecies elevation are included and discussed. This work enhances baseline knowledge of these species to assist futurestudies in these poorly understood, but biologically important areas
Empirical study on clique-degree distribution of networks
The community structure and motif-modular-network hierarchy are of great
importance for understanding the relationship between structures and functions.
In this paper, we investigate the distribution of clique-degree, which is an
extension of degree and can be used to measure the density of cliques in
networks. The empirical studies indicate the extensive existence of power-law
clique-degree distributions in various real networks, and the power-law
exponent decreases with the increasing of clique size.Comment: 9 figures, 4 page
Lifting the veil: richness measurements fail to detect systematic biodiversity change over three decades
While there is widespread recognition of human involvement in biodiversity loss globally, at smaller spatial extents, the effects are less clear. One reason is that local effects are obscured by the use of summary biodiversity variables, such as species richness, that provide only limited insight into complex biodiversity change. Here, we use 30 yr of invertebrate data from a metacommunity of 10 streams in Wales, UK, combined with regional surveys, to examine temporal changes in multiple biodiversity measures at local, metacommunity, and regional scales. There was no change in taxonomic or functional a-diversity and spatial b-diversity metrics at any scale over the 30-yr time series, suggesting a relative stasis in the system and no evidence for on-going homogenization. However, temporal changes in mean species composition were evident. Two independent approaches to estimate species niche breadth showed that compositional changes were associated with a systematic decline in mean community specialization. Estimates of species-specific local extinction and immigration probabilities suggested that this decline was linked to lower recolonization rates of specialists, rather than greater local extinction rates. Our results reveal the need for caution in implying stasis from patterns in a-diversity and spatial b-diversity measures that might mask non-random biodiversity changes over time. We also show how different but complementary approaches to estimate niche breadth and functional distinctness of species can reveal long-term trends in community homogenization likely to be important to conservation and ecosystem function
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