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Spatial ecology studies the ultimate distributional or spatial unit occupied by a species. In a particular habitat shared by several species, each of the species is usually confined to its own microhabitat or spatial niche because two species in the same general territory cannot usually occupy the same ecological niche for any significant length of time.
In nature, organisms are neither distributed uniformly nor at random, forming instead some sort of spatial pattern. [1] This is due to various energy inputs, disturbances, and species interactions that result in spatially patchy structures or gradients. This spatial variance in the environment creates diversity in communities of organisms, as well as in the variety of the observed biological and ecological events. [1] The type of spatial arrangement present may suggest certain interactions within and between species, such as competition, predation, and reproduction. [2] On the other hand, certain spatial patterns may also rule out specific ecological theories previously thought to be true. [3]
Although spatial ecology deals with spatial patterns, it is usually based on observational data rather than on an existing model. [2] This is because nature rarely follows set expected order. To properly research a spatial pattern or population, the spatial extent to which it occurs must be detected. Ideally, this would be accomplished beforehand via a benchmark spatial survey, which would determine whether the pattern or process is on a local, regional, or global scale. This is rare in actual field research, however, due to the lack of time and funding, as well as the ever-changing nature of such widely-studied organisms such as insects and wildlife. [4] With detailed information about a species' life-stages, dynamics, demography, movement, behavior, etc., models of spatial pattern may be developed to estimate and predict events in unsampled locations. [2]
Most mathematical studies in ecology in the nineteenth century assumed a uniform distribution of living organisms in their habitat. [1] In the past quarter century, ecologists have begun to recognize the degree to which organisms respond to spatial patterns in their environment. Due to the rapid advances in computer technology in the same time period, more advanced methods of statistical data analysis have come into use. [3] Also, the repeated use of remotely sensed imagery and geographic information systems in a particular area has led to increased analysis and identification of spatial patterns over time. [4] These technologies have also increased the ability to determine how human activities have impacted animal habitat and climate change. [5] The natural world has become increasingly fragmented due to human activities; anthropogenic landscape change has had a ripple-effect impacts on wildlife populations, which are now more likely to be small, restricted in distribution, and increasingly isolated from one another. In part as a reaction to this knowledge, and partially due to increasingly sophisticated theoretical developments, ecologists began stressing the importance of spatial context in research. Spatial ecology emerged from this movement toward spatial accountability; "the progressive introduction of spatial variation and complexity into ecological analysis, including changes in spatial patterns over time". [6]
In spatial ecology, scale refers to the spatial extent of ecological processes and the spatial interpretation of the data. [7] The response of an organism or a species to the environment is particular to a specific scale, and may respond differently at a larger or smaller scale. [8] Choosing a scale that is appropriate to the ecological process in question is very important in accurately hypothesizing and determining the underlying cause. [9] [10] Most often, ecological patterns are a result of multiple ecological processes, which often operate at more than one spatial scale. [11] Through the use of such spatial statistical methods such as geostatistics and principal coordinate analysis of neighbor matrices (PCNM), one can identify spatial relationships between organisms and environmental variables at multiple scales. [8]
Spatial autocorrelation refers to the value of samples taken close to each other are more likely to have similar magnitude than by chance alone. [7] When a pair of values located at a certain distance apart are more similar than expected by chance, the spatial autocorrelation is said to be positive. When a pair of values are less similar, the spatial autocorrelation is said to be negative. It is common for values to be positively autocorrelated at shorter distances and negative autocorrelated at longer distances. [1] This is commonly known as Tobler's first law of geography, summarized as "everything is related to everything else, but nearby objects are more related than distant objects".
In ecology, there are two important sources of spatial autocorrelation, which both arise from spatial-temporal processes, such as dispersal or migration: [11]
Most ecological data exhibit some degree of spatial autocorrelation, depending on the ecological scale (spatial resolution) of interest. As the spatial arrangement of most ecological data is not random, traditional random population samples tend to overestimate the true value of a variable, or infer significant correlation where there is none. [1] This bias can be corrected through the use of geostatistics and other more statistically advanced models. Regardless of method, the sample size must be appropriate to the scale and the spatial statistical method used in order to be valid. [4]
Spatial patterns, such as the distribution of a species, are the result of either true or induced spatial autocorrelation. [7] In nature, organisms are distributed neither uniformly nor at random. The environment is spatially structured by various ecological processes, [1] which in combination with the behavioral response of species generally results in:
Theoretically, any of these structures may occur at any given scale. Due to the presence of spatial autocorrelation, in nature gradients are generally found at the global level, whereas patches represent intermediate (regional) scales, and noise at local scales. [11]
The analysis of spatial ecological patterns comprises two families of methods: [12]
Analysis of spatial trends has been used to research wildlife management, fire ecology, population ecology, disease ecology, invasive species, marine ecology, and carbon sequestration modeling using the spatial relationships and patterns to determine ecological processes and their effects on the environment. Spatial patterns have different ecosystem functioning in ecology for examples enhanced productive. [14]
The concepts of spatial ecology are fundamental to understanding the spatial dynamics of population and community ecology. The spatial heterogeneity of populations and communities plays a central role in such ecological theories as succession, adaptation, community stability, competition, predator-prey interactions, parasitism, and epidemics. [1] The rapidly expanding field of landscape ecology utilizes the basic aspects of spatial ecology in its research.[ citation needed ]
The practical use of spatial ecology concepts is essential to understanding the consequences of fragmentation and habitat loss for wildlife. Understanding the response of a species to a spatial structure provides useful information in regards to biodiversity conservation and habitat restoration. [15]
Spatial ecology modeling uses components of remote sensing and geographical information systems (GIS).[ citation needed ]
A number of statistical tests have been developed to study such relations.
Clark and Evans in 1954 [16] proposed a test based on the density and distance between organisms. Under the null hypothesis the expected distance ( re ) between the organisms (measured as the nearest neighbor's distance) with a known constant density ( ρ ) is
The difference between the observed ( ro ) and the expected ( re ) can be tested with a Z test
where N is the number of nearest neighbor measurements. For large samples Z is distributed normally. The results are usually reported in the form of a ratio: R = ( ro ) / ( re )
Pielou in 1959 devised a different statistic. [17] She considered instead of the nearest neighbors the distance between an organism and a set of pre-chosen random points within the sampling area, again assuming a constant density. If the population is randomly dispersed in the area these distances will equal the nearest neighbor distances. Let ω be the ratio between the distances from the random points and the distances calculated from the nearest neighbor calculations. The α is[ citation needed ]
where d is the constant common density and π has its usual numerical value. Values of α less than, equal to or greater than 1 indicate uniformity, randomness (a Poisson distribution) or aggregation respectively. Alpha may be tested for a significant deviation from 1 by computing the test statistic
where χ2 is distributed with 2n degrees of freedom. n here is the number of organisms sampled.
Montford in 1961 showed that when the density is estimated rather than a known constant, this version of alpha tended to overestimate the actual degree of aggregation. He provided a revised formulation which corrects this error. There is a wide range of mathematical problems related to spatial ecological models, relating to spatial patterns and processes associated with chaotic phenomena, bifurcations and instability. [18]
Landscape ecology is the science of studying and improving relationships between ecological processes in the environment and particular ecosystems. This is done within a variety of landscape scales, development spatial patterns, and organizational levels of research and policy. Concisely, landscape ecology can be described as the science of "landscape diversity" as the synergetic result of biodiversity and geodiversity.
Phylogeography is the study of the historical processes that may be responsible for the past to present geographic distributions of genealogical lineages. This is accomplished by considering the geographic distribution of individuals in light of genetics, particularly population genetics.
Macroecology is the subfield of ecology that deals with the study of relationships between organisms and their environment at large spatial scales to characterise and explain statistical patterns of abundance, distribution and diversity. The term was coined in a small monograph published in Spanish in 1971 by Guillermo Sarmiento and Maximina Monasterio, two Venezuelan researchers working in tropical savanna ecosystems and later used by James Brown of the University of New Mexico and Brian Maurer of Michigan State University in a 1989 paper in Science.
In time series analysis, the Box–Jenkins method, named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past values of a time series.
Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.
Species distribution, or speciesdispersion, is the manner in which a biological taxon is spatially arranged. The geographic limits of a particular taxon's distribution is its range, often represented as shaded areas on a map. Patterns of distribution change depending on the scale at which they are viewed, from the arrangement of individuals within a small family unit, to patterns within a population, or the distribution of the entire species as a whole (range). Species distribution is not to be confused with dispersal, which is the movement of individuals away from their region of origin or from a population center of high density.
In landscape ecology, landscape connectivity is, broadly, "the degree to which the landscape facilitates or impedes movement among resource patches". Alternatively, connectivity may be a continuous property of the landscape and independent of patches and paths. Connectivity includes both structural connectivity and functional connectivity. Functional connectivity includes actual connectivity and potential connectivity in which movement paths are estimated using the life-history data.
Spatial descriptive statistics is the intersection of spatial statistics and descriptive statistics; these methods are used for a variety of purposes in geography, particularly in quantitative data analyses involving Geographic Information Systems (GIS).
Phylogenetic autocorrelation also known as Galton's problem, after Sir Francis Galton who described it, is the problem of drawing inferences from cross-cultural data, due to the statistical phenomenon now called autocorrelation. The problem is now recognized as a general one that applies to all nonexperimental studies and to experimental design as well. It is most simply described as the problem of external dependencies in making statistical estimates when the elements sampled are not statistically independent. Asking two people in the same household whether they watch TV, for example, does not give you statistically independent answers. The sample size, n, for independent observations in this case is one, not two. Once proper adjustments are made that deal with external dependencies, then the axioms of probability theory concerning statistical independence will apply. These axioms are important for deriving measures of variance, for example, or tests of statistical significance.
Local convex hull (LoCoH) is a method for estimating size of the home range of an animal or a group of animals (e.g. a pack of wolves, a pride of lions, or herd of buffaloes), and for constructing a utilization distribution. The latter is a probability distribution that represents the probabilities of finding an animal within a given area of its home range at any point in time; or, more generally, at points in time for which the utilization distribution has been constructed. In particular, different utilization distributions can be constructed from data pertaining to particular periods of a diurnal or seasonal cycle.
A home range is the area in which an animal lives and moves on a periodic basis. It is related to the concept of an animal's territory which is the area that is actively defended. The concept of a home range was introduced by W. H. Burt in 1943. He drew maps showing where the animal had been observed at different times. An associated concept is the utilization distribution which examines where the animal is likely to be at any given time. Data for mapping a home range used to be gathered by careful observation, but nowadays, the animal is fitted with a transmission collar or similar GPS device.
In ecology, the occupancy–abundance (O–A) relationship is the relationship between the abundance of species and the size of their ranges within a region. This relationship is perhaps one of the most well-documented relationships in macroecology, and applies both intra- and interspecifically. In most cases, the O–A relationship is a positive relationship. Although an O–A relationship would be expected, given that a species colonizing a region must pass through the origin and could reach some theoretical maximum abundance and distribution, the relationship described here is somewhat more substantial, in that observed changes in range are associated with greater-than-proportional changes in abundance. Although this relationship appears to be pervasive, and has important implications for the conservation of endangered species, the mechanism(s) underlying it remain poorly understood
In spatial ecology and macroecology, scaling pattern of occupancy (SPO), also known as the area-of-occupancy (AOO) is the way in which species distribution changes across spatial scales. In physical geography and image analysis, it is similar to the modifiable areal unit problem. Simon A. Levin (1992) states that the problem of relating phenomena across scales is the central problem in biology and in all of science. Understanding the SPO is thus one central theme in ecology.
In statistics, Moran's I is a measure of spatial autocorrelation developed by Patrick Alfred Pierce Moran. Spatial autocorrelation is characterized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex than one-dimensional autocorrelation because spatial correlation is multi-dimensional and multi-directional.
Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecological populations, communities, or ecosystems will change in the future in response to environmental factors such as climate change. The goal of the approach is to provide natural resource managers with information to anticipate and respond to short and long-term climate conditions.
In macroecology and community ecology, an occupancy frequency distribution (OFD) is the distribution of the numbers of species occupying different numbers of areas. It was first reported in 1918 by the Danish botanist Christen C. Raunkiær in his study on plant communities. The OFD is also known as the species-range size distribution in literature.
A boundary problem in analysis is a phenomenon in which geographical patterns are differentiated by the shape and arrangement of boundaries that are drawn for administrative or measurement purposes. The boundary problem occurs because of the loss of neighbors in analyses that depend on the values of the neighbors. While geographic phenomena are measured and analyzed within a specific unit, identical spatial data can appear either dispersed or clustered depending on the boundary placed around the data. In analysis with point data, dispersion is evaluated as dependent of the boundary. In analysis with areal data, statistics should be interpreted based upon the boundary.
Taylor's power law is an empirical law in ecology that relates the variance of the number of individuals of a species per unit area of habitat to the corresponding mean by a power law relationship. It is named after the ecologist who first proposed it in 1961, Lionel Roy Taylor (1924–2007). Taylor's original name for this relationship was the law of the mean. The name Taylor's law was coined by Southwood in 1966.
Landscape genetics is the scientific discipline that combines population genetics and landscape ecology. It broadly encompasses any study that analyses plant or animal population genetic data in conjunction with data on the landscape features and matrix quality where the sampled population lives. This allows for the analysis of microevolutionary processes affecting the species in light of landscape spatial patterns, providing a more realistic view of how populations interact with their environments. Landscape genetics attempts to determine which landscape features are barriers to dispersal and gene flow, how human-induced landscape changes affect the evolution of populations, the source-sink dynamics of a given population, and how diseases or invasive species spread across landscapes.
