Finding the Missing Link between Landscape Structure and Population Dynamics: A Spatially Explicit Perspective

Wiegand, Thorsten; Moloney, Kirk A.; Naves, Javier; Knauer, Felix

doi:10.1086/303272

Cited by 225 publications

(199 citation statements)

References 44 publications

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“…Another measure is the ''ring statistic'' (Wiegand et al 1999). Its basic idea is to place rings with radius r around each cell of a given habitat type 1 (e.g., cells with goodquality habitat) and calculate the mean density of cells within these rings that are of habitat type 2 (e.g., cells with bad habitat).…”

Section: Measures Based On the Amount Of Habitat In The Landscapementioning

confidence: 99%

“…Placing horizontal planes at two (one positive and one negative) elevations along the elevational gradient within the three-dimensional maps produces three elevational zones in the landscape: high, intermediate, and low. High elevations are then associated with one type of habitat (e.g., good habitat), low elevations with another type of habitat, and intermediate elevations with the matrix (Wiegand et al 1999). …”

Section: Modeling Approachesmentioning

confidence: 99%

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Connectivity measures: a review

2008

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One of the central problems in contemporary ecology and conservation biology is the drastic change of landscapes induced by anthropogenic activities, resulting in habitat loss and fragmentation. For many wild living species, local extinctions of fragmented populations are common and recolonization is critical for regional survival. Successful recolonization depends on the availability of dispersing individuals and the degree of landscape connectivity. The obvious implications of landscape connectivity for conservation biology have led to a proliferation of connectivity measures. However, general relationships between landscape connectivity and landscape structure are lacking, and so are the relationships between different connectivity metrics. Consequently, there is a need to develop landscape metrics that more accurately characterize the landscape with an emphasis on the underlying processes. Here we review various definitions of landscape connectivity, explain their mathematical connotations, and make some unifying conclusions and suggestions for future research.

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Section: Measures Based On the Amount Of Habitat In The Landscapementioning

confidence: 99%

Section: Modeling Approachesmentioning

confidence: 99%

Connectivity measures: a review

2008

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“…Los resultados obtenidos en Tragamón ilustran el método de corrección del sesgo debido a la heterogeneidad del patrón, en este caso debido a la existencia de un área de estudio con bordes irregulares que implica una densidad nula de puntos en determinadas regiones del plano. Otro programa que incluye la opción de detectar y delimitar áreas con un patrón homogéneo, y realizar el análisis K en parcelas irregulares es «Programita» (Wiegand et al, 1999;Wiegand y Moloney, 2004) (Tabla 1). Este programa, además de estimar la función K efectúa el análisis de la función O, alternativa a la K que no realiza el recuento de puntos vecinos dentro de círculos, sino de anillos situados a intervalos de distancia regulares.…”

Section: Análisis Univariantes En Una Parcela Irregularunclassified

Spatial analysis techniques applied in forest ecology: point pattern analyses

Rozas

Camarero

2005

For. syst.

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IntroducciónLos métodos cuantitativos de análisis espacial tienen como objetivo la detección y descripción de patrones de distribución espacial, permitiendo evaluar hipótesis sobre los procesos ecológicos que han causado el patrón observado. La heterogeneidad espacial derivada de perturbaciones que eliminan árboles dominantes en el dosel forestal, puede condicionar la regeneración, la estructura de las poblaciones y la coexistencia de las especies (Turner y Franz, 1985; He et ResumenEn este trabajo se revisan algunos métodos modernos de análisis uni-y bivariante de los patrones de puntos utilizados habitualmente en ecología. Se aplicaron el análisis refinado de la distancia al vecino más próximo, la función K de Ripley y la técnica SADIE para analizar patrones de puntos simulados y reales. Un patrón aleatorio, tres patrones en agregados de 3, 5 y 10 m de radio, y tres patrones regulares con distancias de inhibición de 2, 4 y 6 m, fueron simulados para comparar la eficiencia de los diferentes métodos. Los patrones reales proceden de dos bosques caducifolios del litoral cantábrico y un bosque de coníferas en la Sierra de Cebollera-Urbión. Las tres técnicas permitieron diferenciar entre un patrón univariante aleatorio, agregado y regular. El análisis del vecino más próximo y la función K reflejaron las distancias de inhibición en los patrones regulares, pero solo la función K permitió detectar el tamaño de los agregados. Los individuos juveniles mostraron un patrón en agregados, mientras que los adultos presentaron un patrón aleatorio. Respecto a los análisis bivariantes, se obtuvieron evidencias de atracción y repulsión espacial entre algunos patrones simulados, a pesar de que estos son procesos independientes. Los patrones reales mostraron repulsión espacial entre los individuos juveniles y adultos, así como atracción entre juveniles de tamaños diferentes. Los resultados se interpretaron en relación con la bibliografía disponible sobre ecología forestal. Se discute la importancia de aplicar métodos apropiados para la corrección del efecto de borde y la utilización de modelos nulos alternativos al de aleatoriedad espacial.Palabras clave: patrón espacial, vecino más próximo, K de Ripley, SADIE, modelos nulos. Abstract Spatial analysis techniques applied in forest ecology: point pattern analysesSeveral methods for analyzing uni-and bivariate point patterns, commonly used in ecology, are reviewed in this work. The refined nearest neighbor method, the Ripley's K function and the SADIE technique were used to analyze simulated and real spatial point patterns. A random pattern, three clumped patterns with radius of 3, 5 and 10 m, and three random patterns with inhibition distances of 2, 4 and 6 m, were simulated to check the efficiency of the methods. Real patterns were measured in two deciduous forests in the Cantabrian lowlands and a coniferous forest in the Cebollera-Urbión Range. All techniques were able to distinguish between random, clumped and regular patterns. Nearest neighbor method and K function displayed inh...

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“…a bird living in the forested vegetation), and thereby also exhibit aggregated spatial structures. In this regard, Hiebeler [12] proposed a model of habitat structure based on a parameter of density, p, the proportion of suitable sites in the lattice, and a parameter of aggregation, q, the probability to find a suitable site nearby another suitable one. A crucial ingredient of the model is the algorithm that constructs habitat maps with target parameters (p, q).…”

Section: Introductionmentioning

confidence: 99%

“…Our demonstration is based on a comparison of the properties of the Hiebeler model with the Ising model, and on an assessment of the sensitivity of these properties to algorithmic choices. In Section II, we formulate in physical terms the nearest-neighbor model of aggregation introduced on ecological grounds by Hiebeler [12]. Section III is devoted to the analysis of the Hiebeler model in the light of statistical physics and percolation theory.…”

Section: Introductionmentioning

confidence: 99%

Correlated percolation models of structured habitat in ecology

Huth

Lesne

Munoz³

et al. 2014

Physica A: Statistical Mechanics and its Applications

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Percolation offers acknowledged models of random media when the relevant medium characteristics can be described as a binary feature. However, when considering habitat modeling in ecology, a natural constraint comes from nearest-neighbor correlations between the suitable/unsuitable states of the spatial units forming the habitat. Such constraints are also relevant in the physics of aggregation where underlying processes may lead to a form of correlated percolation. However, in ecology, the processes leading to habitat correlations are in general not known or very complex. As proposed by Hiebeler [Ecology 81, 1629[Ecology 81, (2000], these correlations can be captured in a lattice model by an observable aggregation parameter q, supplementing the density p of suitable sites. We investigate this model as an instance of correlated percolation. We analyze the phase diagram of the percolation transition and compute the cluster size distribution, the pair-connectedness function C(r) and the correlation function g(r). We find that while g(r) displays a power-law decrease associated with long-range correlations in a wide domain of parameter values, critical properties are compatible with the universality class of uncorrelated percolation. We contrast the correlation structures obtained respectively for the correlated percolation model and for the Ising model, and show that the diversity of habitat configurations generated by the Hiebeler model is richer than the archetypal Ising model. We also find that emergent structural properties are peculiar to the implemented algorithm, leading to questioning the notion of a well-defined model of aggregated habitat. We conclude that the choice of model and algorithm have strong consequences on what insights ecological studies can get using such models of species habitat.

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Finding the Missing Link between Landscape Structure and Population Dynamics: A Spatially Explicit Perspective

Cited by 225 publications

References 44 publications

Connectivity measures: a review

Connectivity measures: a review

Spatial analysis techniques applied in forest ecology: point pattern analyses

Correlated percolation models of structured habitat in ecology

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