CN113342911A - Landscape connectivity evaluation method considering network time-space dynamic relation - Google Patents
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Abstract
The invention provides a landscape connectivity evaluation method considering network time-space dynamic relation. The invention integrates multi-source ecological and geographic data, evaluates the habitat network connectivity under the dynamic landscape based on network analysis and time flow inference, and comprises the construction of overlapping analysis and classification of two-stage habitat data, time interaction inference of various habitats, space interaction inference of the habitats, habitat internal communication indexes, habitat direct communication and indirect communication indexes considering the foot stone effect. The invention provides important technical support and decision basis for conservation ecology and ecological civilization construction, and is beneficial to scientifically evaluating the influence of various land utilization change scenes on the surrounding natural habitat network so as to promote sustainable land utilization development.
Description
Technical Field
The invention relates to the field of landscape ecology and ecological safety pattern, and mainly relates to landscape connectivity evaluation of a habitat network under a dynamic landscape.
Background
China has attracted attention in the aspects of industrialization and urbanization. However, rapid and extensive city expansion also brings a series of ecological environment problems, such as loss and fragmentation of natural habitat, and poses important threats to the ecological safety pattern in China. Researches show that the connectivity of the natural habitat is reasonably improved, the survival capability of the population and species is obviously improved, and the method plays an important role in improving the biodiversity and maintaining the stability of an ecosystem. Under the background, the influence of land utilization change on landscape connectivity is evaluated, so that the method is not only beneficial to strengthening the protection of easily-dangerous species, but also beneficial to comparison and selection of a land planning scheme and ecological civilization construction, and the development of the human-ground relationship to an ecological friendly type and a sustainable type is promoted.
The current methods for landscape connectivity assessment can be roughly divided into the following categories: (1) the method comprises the steps of (1) a structure connectivity evaluation method based on a spatial structure of a habitat, (2) a function connectivity evaluation method considering the spatial structure of the habitat and species diffusion behaviors at the same time, (3) a composite population capacity method based on a composite population theory, and (4) a network analysis method. However, the existing landscape connectivity evaluation method has certain defects and limitations, which are mainly expressed as follows:
(1) there is a lack of consideration for habitat plaque interactions in the time dimension. Most of the existing researches are based on a spatial and landscape snapshot type evaluation mode, namely, only network construction is carried out on landscape patterns at two moments, and the influence of environmental changes (such as city expansion and farmland expansion) on the habitat of a certain species can be evaluated by comparing the connectivity changes of the landscape patterns and the network construction. Such methods only consider interactions of habitat patches in the spatial dimension brought about by species diffusion, and ignore interactions between patches that may exist in the temporal dimension, thus possibly causing erroneous estimates of changes in landscape network connectivity.
(2) There is a lack of consideration for cross-proxy diffusion of populations. Most of the existing researches assume that the connection strength between plaques and the distance between plaques are in a negative exponential form, and the gradient of a negative exponential function is determined by the diffusion capacity of the species. This assumption is more applicable to the probability of diffusion of a single diffusing individual, and does not provide a good measure of the probability of diffusion of the entire population within a patch. More importantly, the method ignores the possibility that a subpopulation that spreads from a plaque and colonizes intermediate plaques, and that offspring continue to spread from the intermediate plaque, thereby potentially underestimating the impact of long-range spread on the landscape connectivity pattern.
In general, due to the lack of consideration on interaction of time dimensions and cross-era long-distance diffusion, the evaluation result often cannot truly reflect the actual connectivity pattern of the habitat network, and thus the scientific and powerful support cannot be well provided for the construction of the conservation ecology and the ecological safety pattern.
Disclosure of Invention
The invention provides a time-space dynamic method considering the network, and solves the problems that in the prior art, a landscape connectivity model cannot carry out quantitative processing on dynamic connection of the network in a time dimension and cannot consider the long-distance spread of a population across generations.
The invention provides a landscape connectivity evaluation method considering network time-space dynamic relation, which comprises the following steps:
step 1, acquiring land utilization data of a research area at two different periods, existing animal species directory of the area, population scale and diffusion capacity information of the area;
step 2, extracting ecological land patch distribution (such as forest land patches, grassland patches, wetland patches and the like) of the region at two moments based on the land utilization data, and marking the types of habitat patches (including a loss type, a stable type and a new type) through superposition analysis and comparison;
step 3, based on the plaque types and the population scale, calculating the diffusion probability between plaques in two dimensions of time and space according to the diffusion nuclear function provided by the invention;
step 4, calculating intra-plaque connectivity based on the plaque area;
step 5, calculating direct connectivity among the patches and indirect connectivity considering the foot stone effect based on the attributes, types and spatial distribution of the patches;
and 6, summing the results of the steps 4 to 5 to obtain the time-space connectivity under the total dynamic landscape.
Further, step 1 comprises the following substeps:
land use grid data for a research area at two different times is first obtained. The land utilization raster data takes the pixels as basic units, and divides the space range of the project area into raster images of R rows and C columns; each grid pixel on the grid image represents a unit area in geographic space, and the numerical value of each grid pixel represents the land utilization type of the corresponding unit area. According to the local species directory, determining species to be protected or researched, investigating the spatial distribution condition of the population, and searching data on the network to acquire the diffusion capability information of the species.
Further, step 2 comprises the following substeps:
after land utilization grid data are obtained, habitat patches of the species to be researched are extracted. For example, if the species of interest is a forest mammal, forest plaques should be extracted. The actual steps are that the grid map is opened by ArcGIS software, the grid data is converted into a vector file by using a conversion tool in a GIS toolbox, and then the map spots with the land type of forest land are selected and exported according to the map spot attributes.
After the data of the two-stage forest land patch is obtained, the patch after superposition analysis is marked as a loss type, a stable type and a new type by using a superposition analysis tool of an ArcGIS software tool box.
Further, step 3 comprises the following substeps:
and obtaining the diffusion probability of the species population among the plaques based on time flow inference and space flow inference according to the plaque type and the population scale. Wherein the probability of diffusion of a single diffusing individual in the spatial dimension between plaques is calculated by the following negative exponential function:
in the formula, Pij,kRepresents the probability that individual k has spread between patches i and j; beta characterizes the diffusion capacity of a species, usually the reciprocal of the maximum diffusion capacity of a species; dijRepresenting the distance between patches i and j.
And for the population distributed in each plaque, the diffusion probability among the plaques is calculated by the binomial distribution of the individual diffusion probability:
in the formula, PijRepresenting the probability individuals that a fraction of individuals from the population in plaque i can successfully spread and colonize plaque j; n is a radical ofiRepresenting the size of the population in the plaque i; δ represents the diffusible sub-population fraction in plaque i; k is a radical ofjRepresents the minimum number of individuals required to colonize plaque j; the other variables have the same meanings as above. In order to take account of the availability of data and the operability of calculation, the invention simplifies the minimum number of individuals required by the population to colonize other plaques into a constant, which is marked as alpha. The above formula can be rewritten as:
then, the intensity of interaction (P) is generated between the patches in the time dimensionij tReference) is based on temporal flow inference of blob type, resulting in a total of three classes of interaction strengths: may occur (value 1), may not occur (value 0), and may or may not occur (value 0.5). The details of the interaction strength inference method for a specific time dimension are given in the section "detailed implementation methods" below.
Finally, the spatial dimension is diffused by the probability Pij sProbability of interaction with time dimension Pij tMultiplying to obtain the total inter-plaque diffusion probability:
Pij＝Pij s×Pij t
further, step 4 comprises the following substeps:
calculating the area of the plaque by utilizing ArcGIS software and taking the area as the attribute of the plaque; and calculating intra-plaque connectivity within the landscape by the following formula:
in the formula, PCintraRepresents the internal connectivity in the landscape; n represents the number of patches in the landscape; a. theiArea representing "stable" plaque i; a. thelRepresenting the area of the study area.
Further, step 5 comprises the following substeps:
calculating inter-plaque direct connectivity of the dynamic landscape by:
in the formula, PCdirectThe direct connectivity between the spots in the landscape is shown, and the other variables have the same meanings as above.
Meanwhile, the indirect connectivity of the time-space stepping stone effect is considered in the dynamic landscape by the following formula:
in the formula, PCstepShowing indirect connectivity between plaques within the landscape, Pij *And the maximum diffusion probability corresponding to the shortest distance between the plaques i and j through the network is represented, and the other variables have the same meanings as above.
Further, step 6, by summing the intra-plaque connectivity index, the inter-plaque direct connectivity index and the inter-plaque indirect connectivity index in the landscape (i.e. summing the results of steps 4 to 5), obtains the time-space connectivity of the population of the research species in the dynamic landscape within a certain time span, which is expressed by the following formula:
PC＝PCintra+PCdirect+PCstep。
according to the landscape connectivity evaluation method provided by the invention, the population monitoring and GIS space analysis technology is introduced into the landscape connectivity evaluation field, and the connectivity simulation of the dynamic landscape for species populations in a certain time period is realized by utilizing network dynamic analysis and time flow inference. The technical scheme of the invention at least has the following technical effects or advantages: firstly, acquiring land utilization data of a project area at two different time points and population monitoring data of a species to be researched; obtaining dynamic change of plaques of habitats where species live based on superposition analysis in GIS spatial analysis; marking the plaques after superposition analysis into different types; inferring a likelihood of interaction in a time dimension between different types of blobs based on the blob type information; based on the space position information of the patches, calculating the distance between every two patches by utilizing GIS software and calculating the possibility of interaction between the patches in the space dimension; then, on the basis of a network analysis theory, according to the double interaction effect of time and space among the plaques, the intra-plaque connectivity of the plaques due to the self attributes, the direct connectivity of the plaques in the time-space dimension and the indirect connectivity of the plaques provided by the stepping stone are respectively calculated; and finally, by adding the three parts of connectivity, landscape connectivity evaluation considering landscape dynamic connection in the time-space dimension is obtained, so that the evaluation result of the landscape connectivity is more scientific and instructive.
Drawings
Fig. 1 is a schematic diagram of landscape connectivity for species populations, allowing for network dynamic association, where diagram (a): landscape pattern at time t 1; FIG. (b): landscape pattern at time t 2; FIG. (c): inter-plaque space-time interaction of landscape dynamic is considered; FIG. (d): inter-plaque spatial-temporal interactions for species populations that take into account landscape dynamics;
FIG. 2 is a schematic flow chart of a landscape connectivity assessment method according to the present invention;
FIG. 3 is a simulated landscape network structure in a research case provided by the present invention; in the figure, each node corresponds to a patch, and the size of the node corresponds to the size of the area of the patch and also corresponds to the size of the population size carried by the patch. The node colors represent the blob types. The connection lines between the nodes (namely the edges of the network) represent the spatial connection between the nodes, and the length of the edges represents the distance between the plaques.
FIG. 4 is a comparison of the difference between a landscape connectivity assessment provided by the present invention and a traditional landscape connectivity assessment under the diffusion capability of different species; the three component occupancy of connectivity (internal connectivity, direct connectivity and indirect connectivity) varies with species diffusion distance. The left graph is a traditional landscape connectivity evaluation result, and the right graph is a landscape connectivity evaluation result which is provided by the invention and aims at the population and considers the dynamic relation of the time space and the space of the network.
FIG. 5 is a graph illustrating the variation of different diffusing sub-population ratios and the minimum number of individuals required to colonize other plaques for a landscape connectivity assessment provided by the present invention; in the technical scheme provided by the invention, the proportion of three components in the dynamic landscape connectivity (internal connectivity, direct connectivity and indirect connectivity) varies with the diffusion distance of species, the proportion of diffusion population (each row) and the colonization ability (namely the minimum number of individuals required for colonization of other plaques; each column).
Detailed Description
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
For the sake of understanding, the theoretical basis of the invention is first explained:
network analysis
Network analysis is widely used for landscape connectivity assessment. In a landscape, a blob may be considered a network node, while its blob attributes, such as blob size or habitat quality, may be considered node sizes. The spatial relationship between patches is formed by the movement or diffusion of species between patches due to foraging, mate seeking, nesting, etc., and thus may be considered as edges between nodes. Thus, landscapes can be abstracted into a network of habitats, and various indicators and analysis tools in the network can be utilized to explore landscape patterns and processes, and their effects and effects on various levels of biological tissue. Analogy to network connectivity, landscape connectivity is a powerful tool for measuring overall performance of habitat networks, and plays a crucial role in protecting biodiversity and stabilizing ecosystems.
Second, time flow inference
When using a traditional landscape connectivity model to evaluate the impact of land use changes on the habitat network, landscape networks are typically constructed separately for landscapes at a previous time (t1) and a subsequent time (t2), and then the connectivity changes of the two networks are compared. This just ignores possible interactions between blobs in the time dimension. By comparing the patterns of habitat plaques at two moments, each plaque can be classified into three types of 'loss type', 'stable type' and 'new type'. In the traditional landscape connectivity model, the landscape modeling at the time t1 only possibly comprises two types of 'loss type' and 'stable type', and the landscape at the time t2 only comprises two types of 'stable type' and 'new type'. The following is illustrated by way of example in the accompanying FIG. 1: at time t1, the landscape has two blobs i and k and they are connected to each other (panel a). At the time t2, due to other possible factors such as land utilization change and the like, original patches in the landscape disappear, and patches j are newly added at positions farther away from the patches i; there is no connectivity between patches i and j because the distance between patches i and j is too large to exceed the diffusion capacity of the species (panel b). Therefore, conventional landscape connectivity assessment often concludes that environmental changes from time t1 to t2 cause the habitat network to lose landscape connectivity altogether. However, connectivity still exists between blobs i and j from both the temporal and spatial dimensions. This is because diffusing individuals can diffuse from plaque i to k while plaque k is still present in the landscape, and then diffuse from k to j again before k eventually disappears (panel c). Therefore, connectivity between patches i and j still exists from a temporal and spatial perspective.
From whether there is overlap in the time periods in which two blobs exist in the landscape, we can infer the amount of likelihood that a temporal interaction occurs between the two blobs. For example, "efflorescent" plaque must overlap with "stable" plaque for a period of time, since "efflorescent" plaque is only effused after some time intermediate t1 and t2, whereas "stable" plaque remains in the landscape from t1 to t2 and thus the probability of temporal interaction between the two types of plaque is 1. Similarly, there must be temporal interaction between "newly added" plaque and "stable" plaque. However, for the "lost" and "new" plaque, due to the relationship between the sampling time interval and the time granularity, the specific lost/new time point of the "lost" and "new" plaque cannot be accurately known, and it cannot be determined whether there is a time period overlap between them, so that the time interaction between the "lost" and "new" plaque may occur or not, and the time-space interaction probability is set to 0.5.
The edges can also be divided into two types, namely a necessary edge and an auxiliary edge according to the types of nodes at two ends of the edges (see fig. 1, c and d, wherein a solid line represents the necessary edge and a dotted line represents the auxiliary edge). The necessary side is that the individual can spread from a patch i with habitat to a patch j with habitat at another moment only by the necessary side; the auxiliary side is a side through which the diffused individuals cannot be successfully transported from the plaque i with habitat to the plaque j with habitat at the next moment, but a path of successful transportation may be formed through the series connection of a plurality of auxiliary sides and necessary sides. For example, the connection from "stable" to "shed" plaques must be through the secondary edge, since the endpoint "shed" does not have a habitat at time t 2. To construct a successful spatio-temporal path, two requirements are required: (1) the path comprises at least one necessary edge; (2) the start node of the path needs to have a habitat at time t1, and the end node also has a habitat at time t 2. For example, "loss- > stable- > new-addition" can constitute a successful spatiotemporal channel, and both connections are necessary. "Stable- > loss-type- > newly added" may also constitute a successful channel. Wherein the first side is an auxiliary side and the second side is a necessary side, and the probability of the second side occurring is only 0.5, because the existence time of the 'runoff type' plaque and the 'new-added' plaque in the landscape may or may not overlap. Conversely, "stable- > new- > attrition" does not constitute a successful pathway because "attrition" plaques do not have a habitat on which the species lives at time t 2.
The invention takes a simulated dynamic landscape as a case to research the connectivity change of the dynamic landscape of different species under the colonization ability (namely alpha value), the diffusion ability (namely beta value) and the diffusion tendency (namely delta value) and the difference of the connectivity change with the traditional landscape. The following describes a specific implementation of the present invention (see fig. 2).
Step 1, land utilization data of a research area at two different periods, existing animal species lists of the area, population size distribution and diffusion capacity information of the area are obtained. The specific process is as follows:
(1) collecting land use grid data (denoted as raster _ t1 and raster _ t2, respectively) covering project areas at two different points in time from a natural resource management department; assuming that the land utilization grid data divides the whole project area into R rows and C columns, each pixel on the grid represents an n m x n m area in the geographic space (n represents the spatial resolution of the grid data). The value of the grid pixel indicates the land utilization type of the place, for example, the cultivated land is marked as 1, the forest land is marked as 2, and the like.
(2) Collecting a catalogue of forest mammal protective species in the area from an ecological environment management department, and acquiring population scale data of the species in the area.
And 2, extracting the ecological land patch distribution of the region at two moments based on the land utilization data, and marking the types of the habitat patches through superposition analysis and comparison. The specific process is as follows:
(1) with the ArcGIS software, the raster _ t1 and raster _ t2 raster data are converted into vector data (denoted land _ t1 and land _ t2, respectively) using a "raster to vector" tool in the "conversion tool". After the conversion is completed, by the "select by attribute" tool, a blob with woodland type (i.e., a blob with a value equal to 2) is selected and exported as a shapefile format file to extract the woodland blobs at two time points (denoted as forest _ t1 and forest _ t2, respectively).
(2) Open the attribute table for the forest _ t1 file, add a column of attributes "t 1" and assign the column to 1. Similarly, the attribute table of the forest _ t2 file is opened, a column of attribute "t 2" is added and assigned the column as 1. And performing superposition analysis on forest _ t1 and forest _ t2 by using a Union tool in superposition analysis to obtain a new forest file (denoted as forest _ all). Open the attribute table of forest _ all and add the new character type field "type", then label the blob type according to the attributes of the "t 1" and "t 2" columns. Wherein, the plaque with a value in the column "t 1" and a null value in the column "t 2" is labeled as "lost type", "the plaque with a null value in the column" t1 "and a value in the column" t2 "is labeled as" new type ", and the plaque with a value in both the columns" t1 "and" t2 "is labeled as" stable type ".
(3) All Forest patches in the Forest _ all file are converted into point files (marked as node files) through an element point conversion tool in ArcGIS software. And automatically inputting the attributes such as the patch number, the patch area, the patch type and the like into an attribute table of the point file. Meanwhile, a column of short integer field 'boosting' is added to the node file, and the area population scale is distributed according to the area ratio of the plaque corresponding to the node. For example, if the population size of an area is 1000 individuals, the area of the plaque i is 10 hectares, and the area of all plaques is 100 hectares, then the population size of the plaque i is 1000 × 100 (10/100). The actual landscape is distributed as shown in figure 3. In the figure, each node corresponds to a patch, and the size of the node corresponds to the size of the area of the patch and also corresponds to the size of the population size carried by the patch. The node colors represent the blob types. Wherein, light green represents new increase type, pink represents loss type, and light blue represents stable type. The connection lines between the nodes (namely the edges of the network) represent the spatial connection between the nodes, and the length of the edges represents the distance between the plaques.
And 3, calculating the diffusion probability between the patches in two dimensions of time and space according to the diffusion kernel function provided by the invention based on the patch type and the population scale. The specific process is as follows:
(1) the spatial diffusion probability of a single diffusion individual among plaques is firstly calculated through a negative exponential function:
in the formula, Pij,kRepresents the probability that individual k has spread between patches i and j; beta characterizes the diffusion capacity of a species, usually the reciprocal of the maximum diffusion capacity of a species; dijRepresenting the distance between patches i and j.
(2) Secondly, for the population distributed within each plaque, their probability of diffusion between the plaques is calculated by a binomial distribution of the individual probability of diffusion:
in the formula, PijRepresenting the probability individuals that a fraction of individuals from the population in plaque i can successfully spread and colonize plaque j; n is a radical ofiRepresenting the size of the population in the plaque i; δ represents the diffusible sub-population fraction in plaque i; α represents the minimum number of individuals required to colonize other plaques; the other variables have the same meanings as above.
(3) And then, performing time flow inference based on the patch type and the inter-patch connection type, and deducing the probability size of time interaction between the patches, wherein the probability size is divided into three classes of possible occurrence (value 1), impossible occurrence (value 0) and possible occurrence or non-occurrence (value 0.5). The assignment is specifically performed in the following manner:
TABLE 1 time interaction inference of habitat patches within dynamic landscape
(4) Finally, the spatial dimension is diffused by the probability Pij sProbability of interaction with time dimension Pij tMultiplying to obtain the total inter-plaque diffusion probability:
Pij＝Pij s×Pij t
and 4, calculating intra-plaque connectivity based on the plaque area. The method comprises the following specific steps:
(1) opening a node file attribute table, selecting stable plaques in the node file attribute table, and calculating the sum of squares of all the plaque areas;
(2) the sum of the squares is divided by the square of the area of the region to obtain the internal connectivity provided by the area of the habitat itself in the area under investigation.
And 5, calculating direct connectivity among the patches and indirect connectivity considering the foot stone effect based on the attributes, types and spatial distribution of the patches. The specific operation flow is as follows:
(1) calculating the distance between the connected nodes as shown in the figure 3 through a distance analysis tool in ArcGIS software space analysis;
(2) inputting the distance file into a Conefor software, and calculating the shortest distance between nodes which are not directly connected based on a Dijkstra algorithm;
(3) calculating inter-plaque direct connectivity of the dynamic landscape by:
in the formula, PCdirectThe direct connectivity between the spots in the landscape is shown, and the other variables have the same meanings as above.
(4) Calculating the indirect connectivity of the time-space stepping stone effect in the dynamic landscape by the following formula:
in the formula, PCstepShowing indirect connectivity between plaques within the landscape, Pij *And the maximum diffusion probability corresponding to the shortest distance between the plaques i and j through the network is represented, and the other variables have the same meanings as above.
And 6, obtaining the time-space connectivity of the research species population in the dynamic landscape within a certain time span by adding the intra-plaque connectivity index, the inter-plaque direct connectivity index and the inter-plaque indirect connectivity index in the landscape (namely adding the results of the steps 4 to 5), and expressing the time-space connectivity by the following formula:
PC＝PCintra+PCdirect+PCstep
in order to illustrate the implementation effect of the technical scheme, a certain dynamic landscape is selected as a research case. In this case, the area of the study area is about 108 square kilometers. Based on the interpretation of the two-phase remote sensing satellite images, the region has 12 plaques of forest habitats at the first phase time point and 11 plaques at the second phase. And collecting corresponding species lists of the project areas, and finding that the daytime diffusion capacity of the species in the areas is mostly concentrated between 500 meters and 2500 meters. The lowest number of individuals (i.e. the alpha value) required for species in this region to colonize other plaques is distributed between 6 and 10, given the species' competitive and reproductive differences, thus setting alpha to 6, 8, 10 three groups. In addition, the proportion of the population tending to spread among species populations is assumed to be three, 0.25, 0.50 and 0.75. According to the data processing method designed by the invention, firstly, the plaques are subjected to superposition analysis, 9 stable plaques, 3 erosion plaques and 2 newly-added plaques exist in the region in a research period, and the plaques are connected in a Gabriel graph mode (see attached figure 3). Then, the connectivity evaluation is carried out on the region and the species by using a traditional landscape connectivity model method and the dynamic landscape connectivity model provided by the invention, and the evaluation results are compared. In comparison, although the proportion of the stepping stone effect is improved along with the enhancement of the diffusion distance of the species, the traditional landscape connectivity model greatly underestimates the space stepping stone effect, and the highest proportion is about 37.5% when the diffusion distance of the species reaches 2500 m. On the other hand, after the space-time interaction among the patches and the cross-proxy propagation of the population are considered, the highest proportion corresponding to the foot stone effect can reach nearly 80% (see figure 4). Furthermore, by studying the effect of different diffusion capacities, diffusion population ratios and population colonization capacities on landscape connectivity components, it can be seen that when the diffusion capacity is weak (e.g., less than 1000 meters), more diffusive populations can better exploit the stepping stone effect than less diffusive populations. When the diffusion capacity and the diffusion ratio of the population are fixed, the species with strong colonization capacity can better utilize the pedal stone effect (see figure 5).
In summary, the invention is intended to integrate ecological monitoring, GIS spatial analysis technology, network analysis and time flow inference, and based on two-stage dynamic landscape land utilization data, a dynamic network method is used to model the dynamic landscape connectivity of the habitat network in the region, thereby realizing the prediction of two dimensions of time and space on the influence of land utilization change on the habitat network.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to examples, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (7)
1. A landscape connectivity assessment method considering network time-space dynamic association is characterized by comprising the following steps:
step 1, acquiring land utilization data of a research area at two different periods, existing animal species directory of the area, population scale and diffusion capacity information of the area;
step 2, extracting the ecological land patch distribution of the region at two moments based on the land utilization data, and marking the types of habitat patches including a loss type, a stable type and a new type through superposition analysis and comparison;
step 3, calculating the diffusion probability between the patches in two dimensions of time and space according to the diffusion nuclear function based on the patch type and the population scale;
step 4, calculating intra-plaque connectivity PC based on plaque areaintra；
Step 5, calculating the direct connectivity PC between the patches based on the attributes, types and spatial distribution of the patchesdirectAnd indirect connectivity PC taking into account the stepping stone effectstep；
And step 5, summing the results of the step 4 and the step 5 to obtain the time-space connectivity under the total dynamic landscape.
2. The landscape connectivity assessment method allowing for network time-space dynamic association according to claim 1, wherein: the specific implementation manner of the step 1 is as follows;
firstly, acquiring land utilization grid data of a research area at two different periods; the land utilization raster data takes the pixels as basic units, and divides the space range of the project area into raster images of R rows and C columns; each grid pixel on the grid image represents a unit area in the geographic space, and the numerical value of each grid pixel represents the land utilization type of the corresponding unit area; according to the local species directory, determining species to be protected or researched, investigating the spatial distribution condition of the population, and searching data on the network to acquire the diffusion capability information of the species.
3. The landscape connectivity assessment method allowing for network time-space dynamic association according to claim 1, wherein: the specific implementation manner of the step 2 is as follows;
after land utilization grid data are obtained, aiming at species to be researched, habitat patches of the species to be researched are extracted, specifically, a grid map is opened by ArcGIS software, grid data are converted into vector files by a conversion tool in a GIS toolbox, and then the patches are selected and exported according to the attributes of the patches; the ecological land pattern spot types include: woodland patches, grass patches, wetland patches;
after the data of the two-stage forest land patch is obtained, the patch after superposition analysis is marked as a loss type, a stable type and a new type by using a superposition analysis tool of an ArcGIS software tool box.
4. The landscape connectivity assessment method allowing for network time-space dynamic association according to claim 1, wherein: the step 3 includes the following substeps;
(31) the spatial diffusion probability of a single diffusion individual among plaques is firstly calculated through a negative exponential function:
in the formula, Pij,kRepresents the probability that individual k has spread between patches i and j; beta characterizes the diffusion capacity of the species; dijRepresents the distance between patches i and j;
(2) secondly, for the population distributed within each plaque, their probability of diffusion between the plaques is calculated by a binomial distribution of the individual probability of diffusion:
in the formula, PijRepresenting the probability individuals that a fraction of individuals from the population in plaque i can successfully spread and colonize plaque j; n is a radical ofiRepresenting the size of the population in the plaque i; δ represents the diffusible sub-population fraction in plaque i; α represents the minimum number of individuals required to colonize other plaques;
(3) then, a time flow is deduced based on the plaque type and the connection type between plaques, the probability of time interaction between plaques is deduced, and the probability is calculated through Pij tRepresentation, divided into three categories: it may happen that value 1 is assigned; impossible to occur, value 0 is assigned; may or may not occur, with a value of 0.5;
(4) finally, the spatial dimension is diffused by the probability Pij sProbability of interaction with time dimension Pij tMultiplying to obtain the total inter-plaque diffusion probability:
Pij＝Pij s×Pij t。
5. the landscape connectivity assessment method allowing for network time-space dynamic association according to claim 1, wherein: the specific implementation manner of the step 4 is as follows;
calculating the area of the plaque by utilizing ArcGIS software and taking the area as the attribute of the plaque; and calculating intra-plaque connectivity within the landscape by the following formula:
in the formula, PCintraRepresents the internal connectivity in the landscape; n represents the number of patches in the landscape; a. theiArea representing "stable" plaque i; a. thelRepresenting the area of the study area.
6. The landscape connectivity assessment method allowing for network time-space dynamic association according to claim 1, wherein the step 5 comprises the following sub-steps:
(1) calculating the distance between the connected nodes by using a distance analysis tool in ArcGIS software space analysis;
(2) inputting the distance file into a Conefor software, and calculating the shortest distance between nodes which are not directly connected based on a Dijkstra algorithm;
(3) calculating inter-plaque direct connectivity of the dynamic landscape by:
in the formula, PCdirectRepresenting the direct connectivity among the patches in the landscape, and n representing the number of the patches in the landscape; a. theiDenotes the area of the "Stable" plaque i, AjArea representing "stable" plaque j; a. thelDenotes the area of the region of investigation, PijRepresenting the probability of inter-plaque diffusion;
(4) calculating the indirect connectivity of the time-space stepping stone effect in the dynamic landscape by the following formula:
in the formula, PCstepShowing indirect connectivity between plaques within the landscape, Pij *Representing the maximum probability of diffusion between patches i and j corresponding to the shortest distance through the network.
7. The landscape connectivity assessment method allowing for network time-space dynamic association according to claim 1, wherein: the specific implementation manner of the step 6 is as follows;
and (3) obtaining the time-space connectivity of the population of the research species in the dynamic landscape within a certain time span by adding the intra-plaque connectivity index, the inter-plaque direct connectivity index and the inter-plaque indirect connectivity index in the landscape, and expressing the connectivity by the following formula:
PC＝PCintra+PCdirect+PCstep。
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