diff --git a/_freeze/index/execute-results/docx.json b/_freeze/index/execute-results/docx.json index 305e9ed..7642aee 100644 --- a/_freeze/index/execute-results/docx.json +++ b/_freeze/index/execute-results/docx.json @@ -1,8 +1,8 @@ { - "hash": "312150588fede004ece65c6bd6961011", + "hash": "c83ac3df83ccf61f191c40c51592d744", "result": { "engine": "jupyter", - "markdown": "---\ntitle: Omnomnomnivores\nauthor:\n - name: Tanya Strydom\n orcid: 0000-0001-6067-1349\n corresponding: false\n email: t.strydom@sheffield.ac.uk\n roles:\n - Investigation\n - Project administration\n - Software\n - Visualization\n affiliations:\n - Curvenote\n - name: Timothée Poisot\n orcid: 0000-0002-0735-5184\n corresponding: true\n email: timothee.poisot@umontreal.ca\n roles: []\n affiliations:\n - Université de Montreal\n - Québec Centre for Biodiversity Sciences\nkeywords:\n - wombling\n - spatial networks\nabstract: |\n In September 2021, a significant jump in seismic activity on the island of La Palma (Canary Islands, Spain) signaled the start of a volcanic crisis that still continues at the time of writing. Earthquake data is continually collected and published by the Instituto Geográphico Nacional (IGN). ...\nplain-language-summary: |\n Earthquake data for the island of La Palma from the September 2021 eruption is found ...\nkey-points:\n - A web scraping script was developed to pull data from the Instituto Geogràphico Nacional into a machine-readable form for analysis\n - Earthquake events on La Palma are consistent with the presence of both mantle and crustal reservoirs.\ndate: last-modified\nbibliography: references.bib\ncitation:\n container-title: Earth and Space Science\nnumber-sections: true\n---\n\n:::{#217fbe80 .cell .markdown}\n## Introduction\n\n\n## Data & Methods {#sec-data-methods}\n\n### Metacommunity model\n\nThe metacommunity model developed by @Thompson2017Dispersala is a good starting point to use for this 'case study' as it allows us some flexibility with how we want to parameterise the system. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores') and is a collection of modified Lotka–Volterra equations and (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches and looks as follows:\n\n$$\nX_{ij}(t+1)=X_{ij}(t)exp\\left[C_{i} + \\sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\\right]+I_{ij}(t)-X_{ij}(t)a_{i}\n$$ {#eq-metacomm}\n\nWhere $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.01 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is drawn from a uniform distribution with the parameters for the interaction pairs listed in @tbl-interaction_strength, the values drawn from the uniform distribution are scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair.\n\n\n| Interacting pair | Range of uniform distribution|\n|---------------------|:----------------------------:|\n| Plant-plant | -1.0 -- 0.00 |\n| Plant-herbivore | 0.0 -- 0.10 |\n| Plant-carnivore | 0.0 |\n| Herbivore-plant | -0.3 -- 0.00 |\n| Herbivore-herbivore | -0.2-- -0.15 |\n| Herbivore-carnivore | 0.0 -- 0.08 |\n| Carnivore-plant | 0.0 |\n| Carnivore-herbivore | -0.1 -- 0.00 |\n| Carnivore-carnivore | -0.1 -- 0.00 |\n\n: Intervals used for the uniform distribution from which interaction strengths values are drawn from for the different types of species pair interactions. Note this is represent the effect of species type 1 on species type 2 *i.e.,* herbivore-plant represents the effect of a herbivore species on a plant species {#tbl-interaction_strength}\n\n$A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: \n\n$$\n\\hat{A}_{ij}(t)=h\\times\\frac{1}{\\sigma\\sqrt{2\\pi}}\\exp-\\frac{1}{2}\\left(\\frac{E_{j}(t)-H_{i}}{\\sigma}\\right)^2\n$$\n\n$$\nA_{ij}(t)= \\hat{A}_{ij}(t) - max(\\hat{A})\n$$ {#eq-metacomm_env}\n\nSpecies environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to 300), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\\sigma$ is the standard deviation (set to 50).\n\n$I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows:\n\n$$\nI_{ij}(t)=\\sum_{l=j}^{M}a_iX_{il}(t)exp(-Ld_{jl})\n$$ {#eq-metacomm_imm}\n\nWhere $ai$ is the proportion of the population of species $i$ that disperses at each time step, the dispersal rate is drawn from a normal distribution ($\\mu$ = 0.1, $\\sigma$ = 0.025) for each species. The abundance of immigrants to patch $j$ from all other patches is governed by where $d_{jl}$ is the geographic distance between patches $j$ and $l$, and $L$ (the strength of the exponential decrease in dispersal with distance), which is also drawn from a normal distribution for each species. The parameters used for $L$ are trophic level dependant and are show in @tbl-interaction_decay\n\n| Trophic level | $\\mu$ | $\\sigma$ |\n|---------------|:-----:|:--------:|\n| Plant | 0.3 | 0.075 |\n| Herbivore | 0.2 | 0.050 |\n| Carnivore | 0.1 | 0.025 |\n\n: Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay}\n\n### Generating networks\n\nMore info on the baking process and the various connectivity stuff and whatnot\n\n### Spatial wombling\n\nBroadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\\theta$) of change. This is done by using first-order partial derivative ($\\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indiaction how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\\theta$) of the slope. \n\n$$\nm = \\sqrt{\\frac{\\partial f(x,y)}{\\partial x}^2 + \\frac{\\partial f(x,y)}{\\partial y}^2}\n$${#eq-womble}\n\nThe spatial wombling analyses were done using `SpatialBoundaries.jl` [@Strydom2023Spatialboundariesa]. The docuemntation provides a more detailed breakdown of the underlying methodology.\n\n## Conclusion\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n:::\n\n", + "markdown": "---\ntitle: Omnomnomnivores\nauthor:\n - name: Tanya Strydom\n orcid: 0000-0001-6067-1349\n corresponding: false\n email: t.strydom@sheffield.ac.uk\n roles:\n - Investigation\n - Software\n - Visualization\n affiliations:\n - Uum??\n - name: Timothée Poisot\n orcid: 0000-0002-0735-5184\n corresponding: true\n email: timothee.poisot@umontreal.ca\n roles: []\n affiliations:\n - Université de Montreal\n - Québec Centre for Biodiversity Sciences\nkeywords:\n - wombling\n - spatial networks\nabstract: |\n TODO\ndate: last-modified\nbibliography: references.bib\ncitation:\n container-title: Earth and Space Science\nnumber-sections: true\n---\n\n:::{#ffcfc37a .cell .markdown}\n## Introduction\n\n\n## Data & Methods {#sec-data-methods}\n\n### Metacommunity model\n\nThe model used broadly follows the metacommunity model developed by @thompsonDispersalGovernsReorganization2017. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores'), and is essentially a collection of modified Lotka–Volterra equations, this (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches in the landscape and looks as follows:\n\n$$\nX_{ij}(t+1)=X_{ij}(t)exp\\left[C_{i} + \\sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\\right]+I_{ij}(t)-X_{ij}(t)a_{i}\n$$ {#eq-metacomm}\n\nWhere $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.001 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is determined by the trophic level of each species and is drawn from a uniform distribution. The ranges for each combination of species pairs listed in @tbl-interaction_strength, the values that are drawn from the uniform distribution are then scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair.\n\n\n| Interacting pair | Range of uniform distribution|\n|---------------------|:----------------------------:|\n| Plant-plant | -1.0 -- 0.00 |\n| Plant-herbivore | 0.0 -- 0.10 |\n| Plant-carnivore | 0.0 |\n| Herbivore-plant | -0.3 -- 0.00 |\n| Herbivore-herbivore | -0.2 -- -0.15 |\n| Herbivore-carnivore | 0.0 -- 0.08 |\n| Carnivore-plant | 0.0 |\n| Carnivore-herbivore | -0.1 -- 0.00 |\n| Carnivore-carnivore | -0.1 -- 0.00 |\n\n: Intervals used for the uniform distribution from which interaction strengths values are drawn from for the different types of species pair interactions. Note this is represent the effect of species type 1 on species type 2 *i.e.,* herbivore-plant represents the effect of a herbivore species on a plant species {#tbl-interaction_strength}\n\n$A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: \n\n$$\nA_{ij}(t)=\\left(h\\times\\frac{1}{\\sigma\\sqrt{2\\pi}}\\right)\\times\\left(e^{-\\left(E_{j}(t)-H_{i}\\right)^2/{2\\sigma}^2}-1\\right)\n$$ {#eq-metacomm_env}\n\nWhere the species environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to **50**), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\\sigma$ is the standard deviation (set to **50**).\n\n$I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows:\n\n$$\nI_{ij}(t)=\\sum_{l=j}^{M}a_iX_{il}(t)exp(-Ld_{jl})\n$$ {#eq-metacomm_imm}\n\nWhere $ai$ is the proportion of the population of species $i$ that disperses at each time step, the dispersal rate is drawn from a normal distribution ($\\mu$ = 0.1, $\\sigma$ = 0.025) for each species. The abundance of immigrants to patch $j$ from all other patches is governed by where $d_{jl}$ is the geographic distance between patches $j$ and $l$, and $L$ (the strength of the exponential decrease in dispersal with distance), which is also drawn from a normal distribution for each species. The parameters used for $L$ are trophic level dependant and are show in @tbl-interaction_decay\n\n| Trophic level | $\\mu$ | $\\sigma$ |\n|---------------|:-----:|:--------:|\n| Plant | 0.3 | 0.075 |\n| Herbivore | 0.2 | 0.050 |\n| Carnivore | 0.1 | 0.025 |\n\n: Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay}\n\n### Generating networks\n\nIn order to create a final community state the species are allowed to persist for a total of 2000 generations. These generations are broken down into three 'phases' the first is the 'proofing' phase where the environment is uniform throughout the landscape (meaning that all species are at their environmental optimum) for 500 generations. After this the environment is 'heated' incrementally until it reaches its 'final state', the environmental optimum of each species is also adjusted as the environmental values begin to change. This occurs over a period of 1 000 generations. The landscape is then held stable for a further 500 generations until an equilibrium is reached. The final state of the landscape is predetermined and is defined by the diamond-square algorithm (this produces fractals with variable spatial autocorrelation) which is generated using `NeutralLandscapes.jl` [@catchenEcoJuliaNeutralLandscapesJl2023], here we vary the degree of landscape heterogeneity by **TODO**.\n\n| Parameter | Value |\n|---------------|:-----:|\n| $S$ | 100 |\n| $M$ | 26*26 |\n| $E_{initial}$ | 40 |\n| $A_{initial}$ | 0.01 |\n\n:Starting parameters for the model. {#tbl-model_params}\n\n### Spatial wombling\n\nBroadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\\theta$) of change. This is done by using first-order partial derivative ($\\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indication how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\\theta$) of the slope. \n\n$$\nm = \\sqrt{\\frac{\\partial f(x,y)}{\\partial x}^2 + \\frac{\\partial f(x,y)}{\\partial y}^2}\n$${#eq-womble}\n\nThe spatial wombling analyses were done using `SpatialBoundaries.jl` [@strydomSpatialBoundariesJlEdge2023]. The documentation provides a more detailed breakdown of the underlying methodology.\n\n## Conclusion\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n:::\n\n", "supporting": [ "index_files/figure-docx" ], diff --git a/_freeze/index/execute-results/html.json b/_freeze/index/execute-results/html.json index a33b23f..ececb50 100644 --- a/_freeze/index/execute-results/html.json +++ b/_freeze/index/execute-results/html.json @@ -1,8 +1,8 @@ { - "hash": "312150588fede004ece65c6bd6961011", + "hash": "c83ac3df83ccf61f191c40c51592d744", "result": { "engine": "jupyter", - "markdown": "---\ntitle: Omnomnomnivores\nauthor:\n - name: Tanya Strydom\n orcid: 0000-0001-6067-1349\n corresponding: false\n email: t.strydom@sheffield.ac.uk\n roles:\n - Investigation\n - Project administration\n - Software\n - Visualization\n affiliations:\n - Curvenote\n - name: Timothée Poisot\n orcid: 0000-0002-0735-5184\n corresponding: true\n email: timothee.poisot@umontreal.ca\n roles: []\n affiliations:\n - Université de Montreal\n - Québec Centre for Biodiversity Sciences\nkeywords:\n - wombling\n - spatial networks\nabstract: |\n In September 2021, a significant jump in seismic activity on the island of La Palma (Canary Islands, Spain) signaled the start of a volcanic crisis that still continues at the time of writing. Earthquake data is continually collected and published by the Instituto Geográphico Nacional (IGN). ...\nplain-language-summary: |\n Earthquake data for the island of La Palma from the September 2021 eruption is found ...\nkey-points:\n - A web scraping script was developed to pull data from the Instituto Geogràphico Nacional into a machine-readable form for analysis\n - Earthquake events on La Palma are consistent with the presence of both mantle and crustal reservoirs.\ndate: last-modified\nbibliography: references.bib\ncitation:\n container-title: Earth and Space Science\nnumber-sections: true\n---\n\n:::{#23f5e0d2 .cell .markdown}\n## Introduction\n\n\n## Data & Methods {#sec-data-methods}\n\n### Metacommunity model\n\nThe metacommunity model developed by @Thompson2017Dispersala is a good starting point to use for this 'case study' as it allows us some flexibility with how we want to parameterise the system. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores') and is a collection of modified Lotka–Volterra equations and (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches and looks as follows:\n\n$$\nX_{ij}(t+1)=X_{ij}(t)exp\\left[C_{i} + \\sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\\right]+I_{ij}(t)-X_{ij}(t)a_{i}\n$$ {#eq-metacomm}\n\nWhere $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.01 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is drawn from a uniform distribution with the parameters for the interaction pairs listed in @tbl-interaction_strength, the values drawn from the uniform distribution are scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair.\n\n\n| Interacting pair | Range of uniform distribution|\n|---------------------|:----------------------------:|\n| Plant-plant | -1.0 -- 0.00 |\n| Plant-herbivore | 0.0 -- 0.10 |\n| Plant-carnivore | 0.0 |\n| Herbivore-plant | -0.3 -- 0.00 |\n| Herbivore-herbivore | -0.2-- -0.15 |\n| Herbivore-carnivore | 0.0 -- 0.08 |\n| Carnivore-plant | 0.0 |\n| Carnivore-herbivore | -0.1 -- 0.00 |\n| Carnivore-carnivore | -0.1 -- 0.00 |\n\n: Intervals used for the uniform distribution from which interaction strengths values are drawn from for the different types of species pair interactions. Note this is represent the effect of species type 1 on species type 2 *i.e.,* herbivore-plant represents the effect of a herbivore species on a plant species {#tbl-interaction_strength}\n\n$A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: \n\n$$\n\\hat{A}_{ij}(t)=h\\times\\frac{1}{\\sigma\\sqrt{2\\pi}}\\exp-\\frac{1}{2}\\left(\\frac{E_{j}(t)-H_{i}}{\\sigma}\\right)^2\n$$\n\n$$\nA_{ij}(t)= \\hat{A}_{ij}(t) - max(\\hat{A})\n$$ {#eq-metacomm_env}\n\nSpecies environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to 300), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\\sigma$ is the standard deviation (set to 50).\n\n$I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows:\n\n$$\nI_{ij}(t)=\\sum_{l=j}^{M}a_iX_{il}(t)exp(-Ld_{jl})\n$$ {#eq-metacomm_imm}\n\nWhere $ai$ is the proportion of the population of species $i$ that disperses at each time step, the dispersal rate is drawn from a normal distribution ($\\mu$ = 0.1, $\\sigma$ = 0.025) for each species. The abundance of immigrants to patch $j$ from all other patches is governed by where $d_{jl}$ is the geographic distance between patches $j$ and $l$, and $L$ (the strength of the exponential decrease in dispersal with distance), which is also drawn from a normal distribution for each species. The parameters used for $L$ are trophic level dependant and are show in @tbl-interaction_decay\n\n| Trophic level | $\\mu$ | $\\sigma$ |\n|---------------|:-----:|:--------:|\n| Plant | 0.3 | 0.075 |\n| Herbivore | 0.2 | 0.050 |\n| Carnivore | 0.1 | 0.025 |\n\n: Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay}\n\n### Generating networks\n\nMore info on the baking process and the various connectivity stuff and whatnot\n\n### Spatial wombling\n\nBroadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\\theta$) of change. This is done by using first-order partial derivative ($\\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indiaction how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\\theta$) of the slope. \n\n$$\nm = \\sqrt{\\frac{\\partial f(x,y)}{\\partial x}^2 + \\frac{\\partial f(x,y)}{\\partial y}^2}\n$${#eq-womble}\n\nThe spatial wombling analyses were done using `SpatialBoundaries.jl` [@Strydom2023Spatialboundariesa]. The docuemntation provides a more detailed breakdown of the underlying methodology.\n\n## Conclusion\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n:::\n\n", + "markdown": "---\ntitle: Omnomnomnivores\nauthor:\n - name: Tanya Strydom\n orcid: 0000-0001-6067-1349\n corresponding: false\n email: t.strydom@sheffield.ac.uk\n roles:\n - Investigation\n - Software\n - Visualization\n affiliations:\n - Uum??\n - name: Timothée Poisot\n orcid: 0000-0002-0735-5184\n corresponding: true\n email: timothee.poisot@umontreal.ca\n roles: []\n affiliations:\n - Université de Montreal\n - Québec Centre for Biodiversity Sciences\nkeywords:\n - wombling\n - spatial networks\nabstract: |\n TODO\ndate: last-modified\nbibliography: references.bib\ncitation:\n container-title: Earth and Space Science\nnumber-sections: true\n---\n\n:::{#c91aaf8b .cell .markdown}\n## Introduction\n\n\n## Data & Methods {#sec-data-methods}\n\n### Metacommunity model\n\nThe model used broadly follows the metacommunity model developed by @thompsonDispersalGovernsReorganization2017. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores'), and is essentially a collection of modified Lotka–Volterra equations, this (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches in the landscape and looks as follows:\n\n$$\nX_{ij}(t+1)=X_{ij}(t)exp\\left[C_{i} + \\sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\\right]+I_{ij}(t)-X_{ij}(t)a_{i}\n$$ {#eq-metacomm}\n\nWhere $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.001 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is determined by the trophic level of each species and is drawn from a uniform distribution. The ranges for each combination of species pairs listed in @tbl-interaction_strength, the values that are drawn from the uniform distribution are then scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair.\n\n\n| Interacting pair | Range of uniform distribution|\n|---------------------|:----------------------------:|\n| Plant-plant | -1.0 -- 0.00 |\n| Plant-herbivore | 0.0 -- 0.10 |\n| Plant-carnivore | 0.0 |\n| Herbivore-plant | -0.3 -- 0.00 |\n| Herbivore-herbivore | -0.2 -- -0.15 |\n| Herbivore-carnivore | 0.0 -- 0.08 |\n| Carnivore-plant | 0.0 |\n| Carnivore-herbivore | -0.1 -- 0.00 |\n| Carnivore-carnivore | -0.1 -- 0.00 |\n\n: Intervals used for the uniform distribution from which interaction strengths values are drawn from for the different types of species pair interactions. Note this is represent the effect of species type 1 on species type 2 *i.e.,* herbivore-plant represents the effect of a herbivore species on a plant species {#tbl-interaction_strength}\n\n$A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: \n\n$$\nA_{ij}(t)=\\left(h\\times\\frac{1}{\\sigma\\sqrt{2\\pi}}\\right)\\times\\left(e^{-\\left(E_{j}(t)-H_{i}\\right)^2/{2\\sigma}^2}-1\\right)\n$$ {#eq-metacomm_env}\n\nWhere the species environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to **50**), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\\sigma$ is the standard deviation (set to **50**).\n\n$I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows:\n\n$$\nI_{ij}(t)=\\sum_{l=j}^{M}a_iX_{il}(t)exp(-Ld_{jl})\n$$ {#eq-metacomm_imm}\n\nWhere $ai$ is the proportion of the population of species $i$ that disperses at each time step, the dispersal rate is drawn from a normal distribution ($\\mu$ = 0.1, $\\sigma$ = 0.025) for each species. The abundance of immigrants to patch $j$ from all other patches is governed by where $d_{jl}$ is the geographic distance between patches $j$ and $l$, and $L$ (the strength of the exponential decrease in dispersal with distance), which is also drawn from a normal distribution for each species. The parameters used for $L$ are trophic level dependant and are show in @tbl-interaction_decay\n\n| Trophic level | $\\mu$ | $\\sigma$ |\n|---------------|:-----:|:--------:|\n| Plant | 0.3 | 0.075 |\n| Herbivore | 0.2 | 0.050 |\n| Carnivore | 0.1 | 0.025 |\n\n: Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay}\n\n### Generating networks\n\nIn order to create a final community state the species are allowed to persist for a total of 2000 generations. These generations are broken down into three 'phases' the first is the 'proofing' phase where the environment is uniform throughout the landscape (meaning that all species are at their environmental optimum) for 500 generations. After this the environment is 'heated' incrementally until it reaches its 'final state', the environmental optimum of each species is also adjusted as the environmental values begin to change. This occurs over a period of 1 000 generations. The landscape is then held stable for a further 500 generations until an equilibrium is reached. The final state of the landscape is predetermined and is defined by the diamond-square algorithm (this produces fractals with variable spatial autocorrelation) which is generated using `NeutralLandscapes.jl` [@catchenEcoJuliaNeutralLandscapesJl2023], here we vary the degree of landscape heterogeneity by **TODO**.\n\n| Parameter | Value |\n|---------------|:-----:|\n| $S$ | 100 |\n| $M$ | 26*26 |\n| $E_{initial}$ | 40 |\n| $A_{initial}$ | 0.01 |\n\n:Starting parameters for the model. {#tbl-model_params}\n\n### Spatial wombling\n\nBroadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\\theta$) of change. This is done by using first-order partial derivative ($\\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indication how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\\theta$) of the slope. \n\n$$\nm = \\sqrt{\\frac{\\partial f(x,y)}{\\partial x}^2 + \\frac{\\partial f(x,y)}{\\partial y}^2}\n$${#eq-womble}\n\nThe spatial wombling analyses were done using `SpatialBoundaries.jl` [@strydomSpatialBoundariesJlEdge2023]. The documentation provides a more detailed breakdown of the underlying methodology.\n\n## Conclusion\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n:::\n\n", "supporting": [ "index_files/figure-html" ], diff --git a/_freeze/index/execute-results/tex.json b/_freeze/index/execute-results/tex.json index 2f67496..440557e 100644 --- a/_freeze/index/execute-results/tex.json +++ b/_freeze/index/execute-results/tex.json @@ -1,8 +1,8 @@ { - "hash": "312150588fede004ece65c6bd6961011", + "hash": "c83ac3df83ccf61f191c40c51592d744", "result": { "engine": "jupyter", - "markdown": "---\ntitle: Omnomnomnivores\nauthor:\n - name: Tanya Strydom\n orcid: 0000-0001-6067-1349\n corresponding: false\n email: t.strydom@sheffield.ac.uk\n roles:\n - Investigation\n - Project administration\n - Software\n - Visualization\n affiliations:\n - Curvenote\n - name: Timothée Poisot\n orcid: 0000-0002-0735-5184\n corresponding: true\n email: timothee.poisot@umontreal.ca\n roles: []\n affiliations:\n - Université de Montreal\n - Québec Centre for Biodiversity Sciences\nkeywords:\n - wombling\n - spatial networks\nabstract: |\n In September 2021, a significant jump in seismic activity on the island of La Palma (Canary Islands, Spain) signaled the start of a volcanic crisis that still continues at the time of writing. Earthquake data is continually collected and published by the Instituto Geográphico Nacional (IGN). ...\nplain-language-summary: |\n Earthquake data for the island of La Palma from the September 2021 eruption is found ...\nkey-points:\n - A web scraping script was developed to pull data from the Instituto Geogràphico Nacional into a machine-readable form for analysis\n - Earthquake events on La Palma are consistent with the presence of both mantle and crustal reservoirs.\ndate: last-modified\nbibliography: references.bib\ncitation:\n container-title: Earth and Space Science\nnumber-sections: true\n---\n\n:::{#3c9ffcbf .cell .markdown}\n## Introduction\n\n\n## Data & Methods {#sec-data-methods}\n\n### Metacommunity model\n\nThe metacommunity model developed by @Thompson2017Dispersala is a good starting point to use for this 'case study' as it allows us some flexibility with how we want to parameterise the system. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores') and is a collection of modified Lotka–Volterra equations and (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches and looks as follows:\n\n$$\nX_{ij}(t+1)=X_{ij}(t)exp\\left[C_{i} + \\sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\\right]+I_{ij}(t)-X_{ij}(t)a_{i}\n$$ {#eq-metacomm}\n\nWhere $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.01 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is drawn from a uniform distribution with the parameters for the interaction pairs listed in @tbl-interaction_strength, the values drawn from the uniform distribution are scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair.\n\n\n| Interacting pair | Range of uniform distribution|\n|---------------------|:----------------------------:|\n| Plant-plant | -1.0 -- 0.00 |\n| Plant-herbivore | 0.0 -- 0.10 |\n| Plant-carnivore | 0.0 |\n| Herbivore-plant | -0.3 -- 0.00 |\n| Herbivore-herbivore | -0.2-- -0.15 |\n| Herbivore-carnivore | 0.0 -- 0.08 |\n| Carnivore-plant | 0.0 |\n| Carnivore-herbivore | -0.1 -- 0.00 |\n| Carnivore-carnivore | -0.1 -- 0.00 |\n\n: Intervals used for the uniform distribution from which interaction strengths values are drawn from for the different types of species pair interactions. Note this is represent the effect of species type 1 on species type 2 *i.e.,* herbivore-plant represents the effect of a herbivore species on a plant species {#tbl-interaction_strength}\n\n$A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: \n\n$$\n\\hat{A}_{ij}(t)=h\\times\\frac{1}{\\sigma\\sqrt{2\\pi}}\\exp-\\frac{1}{2}\\left(\\frac{E_{j}(t)-H_{i}}{\\sigma}\\right)^2\n$$\n\n$$\nA_{ij}(t)= \\hat{A}_{ij}(t) - max(\\hat{A})\n$$ {#eq-metacomm_env}\n\nSpecies environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to 300), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\\sigma$ is the standard deviation (set to 50).\n\n$I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows:\n\n$$\nI_{ij}(t)=\\sum_{l=j}^{M}a_iX_{il}(t)exp(-Ld_{jl})\n$$ {#eq-metacomm_imm}\n\nWhere $ai$ is the proportion of the population of species $i$ that disperses at each time step, the dispersal rate is drawn from a normal distribution ($\\mu$ = 0.1, $\\sigma$ = 0.025) for each species. The abundance of immigrants to patch $j$ from all other patches is governed by where $d_{jl}$ is the geographic distance between patches $j$ and $l$, and $L$ (the strength of the exponential decrease in dispersal with distance), which is also drawn from a normal distribution for each species. The parameters used for $L$ are trophic level dependant and are show in @tbl-interaction_decay\n\n| Trophic level | $\\mu$ | $\\sigma$ |\n|---------------|:-----:|:--------:|\n| Plant | 0.3 | 0.075 |\n| Herbivore | 0.2 | 0.050 |\n| Carnivore | 0.1 | 0.025 |\n\n: Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay}\n\n### Generating networks\n\nMore info on the baking process and the various connectivity stuff and whatnot\n\n### Spatial wombling\n\nBroadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\\theta$) of change. This is done by using first-order partial derivative ($\\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indiaction how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\\theta$) of the slope. \n\n$$\nm = \\sqrt{\\frac{\\partial f(x,y)}{\\partial x}^2 + \\frac{\\partial f(x,y)}{\\partial y}^2}\n$${#eq-womble}\n\nThe spatial wombling analyses were done using `SpatialBoundaries.jl` [@Strydom2023Spatialboundariesa]. The docuemntation provides a more detailed breakdown of the underlying methodology.\n\n## Conclusion\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n:::\n\n", + "markdown": "---\ntitle: Omnomnomnivores\nauthor:\n - name: Tanya Strydom\n orcid: 0000-0001-6067-1349\n corresponding: false\n email: t.strydom@sheffield.ac.uk\n roles:\n - Investigation\n - Software\n - Visualization\n affiliations:\n - Uum??\n - name: Timothée Poisot\n orcid: 0000-0002-0735-5184\n corresponding: true\n email: timothee.poisot@umontreal.ca\n roles: []\n affiliations:\n - Université de Montreal\n - Québec Centre for Biodiversity Sciences\nkeywords:\n - wombling\n - spatial networks\nabstract: |\n TODO\ndate: last-modified\nbibliography: references.bib\ncitation:\n container-title: Earth and Space Science\nnumber-sections: true\n---\n\n:::{#c6ee54b1 .cell .markdown}\n## Introduction\n\n\n## Data & Methods {#sec-data-methods}\n\n### Metacommunity model\n\nThe model used broadly follows the metacommunity model developed by @thompsonDispersalGovernsReorganization2017. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores'), and is essentially a collection of modified Lotka–Volterra equations, this (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches in the landscape and looks as follows:\n\n$$\nX_{ij}(t+1)=X_{ij}(t)exp\\left[C_{i} + \\sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\\right]+I_{ij}(t)-X_{ij}(t)a_{i}\n$$ {#eq-metacomm}\n\nWhere $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.001 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is determined by the trophic level of each species and is drawn from a uniform distribution. The ranges for each combination of species pairs listed in @tbl-interaction_strength, the values that are drawn from the uniform distribution are then scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair.\n\n\n| Interacting pair | Range of uniform distribution|\n|---------------------|:----------------------------:|\n| Plant-plant | -1.0 -- 0.00 |\n| Plant-herbivore | 0.0 -- 0.10 |\n| Plant-carnivore | 0.0 |\n| Herbivore-plant | -0.3 -- 0.00 |\n| Herbivore-herbivore | -0.2 -- -0.15 |\n| Herbivore-carnivore | 0.0 -- 0.08 |\n| Carnivore-plant | 0.0 |\n| Carnivore-herbivore | -0.1 -- 0.00 |\n| Carnivore-carnivore | -0.1 -- 0.00 |\n\n: Intervals used for the uniform distribution from which interaction strengths values are drawn from for the different types of species pair interactions. Note this is represent the effect of species type 1 on species type 2 *i.e.,* herbivore-plant represents the effect of a herbivore species on a plant species {#tbl-interaction_strength}\n\n$A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: \n\n$$\nA_{ij}(t)=\\left(h\\times\\frac{1}{\\sigma\\sqrt{2\\pi}}\\right)\\times\\left(e^{-\\left(E_{j}(t)-H_{i}\\right)^2/{2\\sigma}^2}-1\\right)\n$$ {#eq-metacomm_env}\n\nWhere the species environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to **50**), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\\sigma$ is the standard deviation (set to **50**).\n\n$I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows:\n\n$$\nI_{ij}(t)=\\sum_{l=j}^{M}a_iX_{il}(t)exp(-Ld_{jl})\n$$ {#eq-metacomm_imm}\n\nWhere $ai$ is the proportion of the population of species $i$ that disperses at each time step, the dispersal rate is drawn from a normal distribution ($\\mu$ = 0.1, $\\sigma$ = 0.025) for each species. The abundance of immigrants to patch $j$ from all other patches is governed by where $d_{jl}$ is the geographic distance between patches $j$ and $l$, and $L$ (the strength of the exponential decrease in dispersal with distance), which is also drawn from a normal distribution for each species. The parameters used for $L$ are trophic level dependant and are show in @tbl-interaction_decay\n\n| Trophic level | $\\mu$ | $\\sigma$ |\n|---------------|:-----:|:--------:|\n| Plant | 0.3 | 0.075 |\n| Herbivore | 0.2 | 0.050 |\n| Carnivore | 0.1 | 0.025 |\n\n: Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay}\n\n### Generating networks\n\nIn order to create a final community state the species are allowed to persist for a total of 2000 generations. These generations are broken down into three 'phases' the first is the 'proofing' phase where the environment is uniform throughout the landscape (meaning that all species are at their environmental optimum) for 500 generations. After this the environment is 'heated' incrementally until it reaches its 'final state', the environmental optimum of each species is also adjusted as the environmental values begin to change. This occurs over a period of 1 000 generations. The landscape is then held stable for a further 500 generations until an equilibrium is reached. The final state of the landscape is predetermined and is defined by the diamond-square algorithm (this produces fractals with variable spatial autocorrelation) which is generated using `NeutralLandscapes.jl` [@catchenEcoJuliaNeutralLandscapesJl2023], here we vary the degree of landscape heterogeneity by **TODO**.\n\n| Parameter | Value |\n|---------------|:-----:|\n| $S$ | 100 |\n| $M$ | 26*26 |\n| $E_{initial}$ | 40 |\n| $A_{initial}$ | 0.01 |\n\n:Starting parameters for the model. {#tbl-model_params}\n\n### Spatial wombling\n\nBroadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\\theta$) of change. This is done by using first-order partial derivative ($\\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indication how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\\theta$) of the slope. \n\n$$\nm = \\sqrt{\\frac{\\partial f(x,y)}{\\partial x}^2 + \\frac{\\partial f(x,y)}{\\partial y}^2}\n$${#eq-womble}\n\nThe spatial wombling analyses were done using `SpatialBoundaries.jl` [@strydomSpatialBoundariesJlEdge2023]. The documentation provides a more detailed breakdown of the underlying methodology.\n\n## Conclusion\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n:::\n\n", "supporting": [ "index_files/figure-pdf" ], diff --git a/_freeze/index/execute-results/xml.json b/_freeze/index/execute-results/xml.json index f4b86c0..5e44472 100644 --- a/_freeze/index/execute-results/xml.json +++ b/_freeze/index/execute-results/xml.json @@ -1,8 +1,8 @@ { - "hash": "312150588fede004ece65c6bd6961011", + "hash": "c83ac3df83ccf61f191c40c51592d744", "result": { "engine": "jupyter", - "markdown": "---\ntitle: Omnomnomnivores\nauthor:\n - name: Tanya Strydom\n orcid: 0000-0001-6067-1349\n corresponding: false\n email: t.strydom@sheffield.ac.uk\n roles:\n - Investigation\n - Project administration\n - Software\n - Visualization\n affiliations:\n - Curvenote\n - name: Timothée Poisot\n orcid: 0000-0002-0735-5184\n corresponding: true\n email: timothee.poisot@umontreal.ca\n roles: []\n affiliations:\n - Université de Montreal\n - Québec Centre for Biodiversity Sciences\nkeywords:\n - wombling\n - spatial networks\nabstract: |\n In September 2021, a significant jump in seismic activity on the island of La Palma (Canary Islands, Spain) signaled the start of a volcanic crisis that still continues at the time of writing. Earthquake data is continually collected and published by the Instituto Geográphico Nacional (IGN). ...\nplain-language-summary: |\n Earthquake data for the island of La Palma from the September 2021 eruption is found ...\nkey-points:\n - A web scraping script was developed to pull data from the Instituto Geogràphico Nacional into a machine-readable form for analysis\n - Earthquake events on La Palma are consistent with the presence of both mantle and crustal reservoirs.\ndate: last-modified\nbibliography: references.bib\ncitation:\n container-title: Earth and Space Science\nnumber-sections: true\n---\n\n:::{#80a8bd30 .cell .markdown}\n## Introduction\n\n\n## Data & Methods {#sec-data-methods}\n\n### Metacommunity model\n\nThe metacommunity model developed by @Thompson2017Dispersala is a good starting point to use for this 'case study' as it allows us some flexibility with how we want to parameterise the system. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores') and is a collection of modified Lotka–Volterra equations and (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches and looks as follows:\n\n$$\nX_{ij}(t+1)=X_{ij}(t)exp\\left[C_{i} + \\sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\\right]+I_{ij}(t)-X_{ij}(t)a_{i}\n$$ {#eq-metacomm}\n\nWhere $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.01 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is drawn from a uniform distribution with the parameters for the interaction pairs listed in @tbl-interaction_strength, the values drawn from the uniform distribution are scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair.\n\n\n| Interacting pair | Range of uniform distribution|\n|---------------------|:----------------------------:|\n| Plant-plant | -1.0 -- 0.00 |\n| Plant-herbivore | 0.0 -- 0.10 |\n| Plant-carnivore | 0.0 |\n| Herbivore-plant | -0.3 -- 0.00 |\n| Herbivore-herbivore | -0.2-- -0.15 |\n| Herbivore-carnivore | 0.0 -- 0.08 |\n| Carnivore-plant | 0.0 |\n| Carnivore-herbivore | -0.1 -- 0.00 |\n| Carnivore-carnivore | -0.1 -- 0.00 |\n\n: Intervals used for the uniform distribution from which interaction strengths values are drawn from for the different types of species pair interactions. Note this is represent the effect of species type 1 on species type 2 *i.e.,* herbivore-plant represents the effect of a herbivore species on a plant species {#tbl-interaction_strength}\n\n$A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: \n\n$$\n\\hat{A}_{ij}(t)=h\\times\\frac{1}{\\sigma\\sqrt{2\\pi}}\\exp-\\frac{1}{2}\\left(\\frac{E_{j}(t)-H_{i}}{\\sigma}\\right)^2\n$$\n\n$$\nA_{ij}(t)= \\hat{A}_{ij}(t) - max(\\hat{A})\n$$ {#eq-metacomm_env}\n\nSpecies environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to 300), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\\sigma$ is the standard deviation (set to 50).\n\n$I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows:\n\n$$\nI_{ij}(t)=\\sum_{l=j}^{M}a_iX_{il}(t)exp(-Ld_{jl})\n$$ {#eq-metacomm_imm}\n\nWhere $ai$ is the proportion of the population of species $i$ that disperses at each time step, the dispersal rate is drawn from a normal distribution ($\\mu$ = 0.1, $\\sigma$ = 0.025) for each species. The abundance of immigrants to patch $j$ from all other patches is governed by where $d_{jl}$ is the geographic distance between patches $j$ and $l$, and $L$ (the strength of the exponential decrease in dispersal with distance), which is also drawn from a normal distribution for each species. The parameters used for $L$ are trophic level dependant and are show in @tbl-interaction_decay\n\n| Trophic level | $\\mu$ | $\\sigma$ |\n|---------------|:-----:|:--------:|\n| Plant | 0.3 | 0.075 |\n| Herbivore | 0.2 | 0.050 |\n| Carnivore | 0.1 | 0.025 |\n\n: Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay}\n\n### Generating networks\n\nMore info on the baking process and the various connectivity stuff and whatnot\n\n### Spatial wombling\n\nBroadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\\theta$) of change. This is done by using first-order partial derivative ($\\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indiaction how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\\theta$) of the slope. \n\n$$\nm = \\sqrt{\\frac{\\partial f(x,y)}{\\partial x}^2 + \\frac{\\partial f(x,y)}{\\partial y}^2}\n$${#eq-womble}\n\nThe spatial wombling analyses were done using `SpatialBoundaries.jl` [@Strydom2023Spatialboundariesa]. The docuemntation provides a more detailed breakdown of the underlying methodology.\n\n## Conclusion\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n:::\n\n", + "markdown": "---\ntitle: Omnomnomnivores\nauthor:\n - name: Tanya Strydom\n orcid: 0000-0001-6067-1349\n corresponding: false\n email: t.strydom@sheffield.ac.uk\n roles:\n - Investigation\n - Software\n - Visualization\n affiliations:\n - Uum??\n - name: Timothée Poisot\n orcid: 0000-0002-0735-5184\n corresponding: true\n email: timothee.poisot@umontreal.ca\n roles: []\n affiliations:\n - Université de Montreal\n - Québec Centre for Biodiversity Sciences\nkeywords:\n - wombling\n - spatial networks\nabstract: |\n TODO\ndate: last-modified\nbibliography: references.bib\ncitation:\n container-title: Earth and Space Science\nnumber-sections: true\n---\n\n:::{#a44d7eb6 .cell .markdown}\n## Introduction\n\n\n## Data & Methods {#sec-data-methods}\n\n### Metacommunity model\n\nThe model used broadly follows the metacommunity model developed by @thompsonDispersalGovernsReorganization2017. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores'), and is essentially a collection of modified Lotka–Volterra equations, this (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches in the landscape and looks as follows:\n\n$$\nX_{ij}(t+1)=X_{ij}(t)exp\\left[C_{i} + \\sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\\right]+I_{ij}(t)-X_{ij}(t)a_{i}\n$$ {#eq-metacomm}\n\nWhere $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.001 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is determined by the trophic level of each species and is drawn from a uniform distribution. The ranges for each combination of species pairs listed in @tbl-interaction_strength, the values that are drawn from the uniform distribution are then scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair.\n\n\n| Interacting pair | Range of uniform distribution|\n|---------------------|:----------------------------:|\n| Plant-plant | -1.0 -- 0.00 |\n| Plant-herbivore | 0.0 -- 0.10 |\n| Plant-carnivore | 0.0 |\n| Herbivore-plant | -0.3 -- 0.00 |\n| Herbivore-herbivore | -0.2 -- -0.15 |\n| Herbivore-carnivore | 0.0 -- 0.08 |\n| Carnivore-plant | 0.0 |\n| Carnivore-herbivore | -0.1 -- 0.00 |\n| Carnivore-carnivore | -0.1 -- 0.00 |\n\n: Intervals used for the uniform distribution from which interaction strengths values are drawn from for the different types of species pair interactions. Note this is represent the effect of species type 1 on species type 2 *i.e.,* herbivore-plant represents the effect of a herbivore species on a plant species {#tbl-interaction_strength}\n\n$A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: \n\n$$\nA_{ij}(t)=\\left(h\\times\\frac{1}{\\sigma\\sqrt{2\\pi}}\\right)\\times\\left(e^{-\\left(E_{j}(t)-H_{i}\\right)^2/{2\\sigma}^2}-1\\right)\n$$ {#eq-metacomm_env}\n\nWhere the species environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to **50**), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\\sigma$ is the standard deviation (set to **50**).\n\n$I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows:\n\n$$\nI_{ij}(t)=\\sum_{l=j}^{M}a_iX_{il}(t)exp(-Ld_{jl})\n$$ {#eq-metacomm_imm}\n\nWhere $ai$ is the proportion of the population of species $i$ that disperses at each time step, the dispersal rate is drawn from a normal distribution ($\\mu$ = 0.1, $\\sigma$ = 0.025) for each species. The abundance of immigrants to patch $j$ from all other patches is governed by where $d_{jl}$ is the geographic distance between patches $j$ and $l$, and $L$ (the strength of the exponential decrease in dispersal with distance), which is also drawn from a normal distribution for each species. The parameters used for $L$ are trophic level dependant and are show in @tbl-interaction_decay\n\n| Trophic level | $\\mu$ | $\\sigma$ |\n|---------------|:-----:|:--------:|\n| Plant | 0.3 | 0.075 |\n| Herbivore | 0.2 | 0.050 |\n| Carnivore | 0.1 | 0.025 |\n\n: Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay}\n\n### Generating networks\n\nIn order to create a final community state the species are allowed to persist for a total of 2000 generations. These generations are broken down into three 'phases' the first is the 'proofing' phase where the environment is uniform throughout the landscape (meaning that all species are at their environmental optimum) for 500 generations. After this the environment is 'heated' incrementally until it reaches its 'final state', the environmental optimum of each species is also adjusted as the environmental values begin to change. This occurs over a period of 1 000 generations. The landscape is then held stable for a further 500 generations until an equilibrium is reached. The final state of the landscape is predetermined and is defined by the diamond-square algorithm (this produces fractals with variable spatial autocorrelation) which is generated using `NeutralLandscapes.jl` [@catchenEcoJuliaNeutralLandscapesJl2023], here we vary the degree of landscape heterogeneity by **TODO**.\n\n| Parameter | Value |\n|---------------|:-----:|\n| $S$ | 100 |\n| $M$ | 26*26 |\n| $E_{initial}$ | 40 |\n| $A_{initial}$ | 0.01 |\n\n:Starting parameters for the model. {#tbl-model_params}\n\n### Spatial wombling\n\nBroadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\\theta$) of change. This is done by using first-order partial derivative ($\\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indication how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\\theta$) of the slope. \n\n$$\nm = \\sqrt{\\frac{\\partial f(x,y)}{\\partial x}^2 + \\frac{\\partial f(x,y)}{\\partial y}^2}\n$${#eq-womble}\n\nThe spatial wombling analyses were done using `SpatialBoundaries.jl` [@strydomSpatialBoundariesJlEdge2023]. The documentation provides a more detailed breakdown of the underlying methodology.\n\n## Conclusion\n\n## References {.unnumbered}\n\n::: {#refs}\n:::\n:::\n\n", "supporting": [ "index_files/figure-jats" ], diff --git a/index.qmd b/index.qmd index ad7c899..1788c36 100644 --- a/index.qmd +++ b/index.qmd @@ -7,11 +7,10 @@ author: email: t.strydom@sheffield.ac.uk roles: - Investigation - - Project administration - Software - Visualization affiliations: - - Curvenote + - Uum?? - name: Timothée Poisot orcid: 0000-0002-0735-5184 corresponding: true @@ -24,12 +23,7 @@ keywords: - wombling - spatial networks abstract: | - In September 2021, a significant jump in seismic activity on the island of La Palma (Canary Islands, Spain) signaled the start of a volcanic crisis that still continues at the time of writing. Earthquake data is continually collected and published by the Instituto Geográphico Nacional (IGN). ... -plain-language-summary: | - Earthquake data for the island of La Palma from the September 2021 eruption is found ... -key-points: - - A web scraping script was developed to pull data from the Instituto Geogràphico Nacional into a machine-readable form for analysis - - Earthquake events on La Palma are consistent with the presence of both mantle and crustal reservoirs. + TODO date: last-modified bibliography: references.bib citation: @@ -45,23 +39,23 @@ jupyter: python3 ### Metacommunity model -The metacommunity model developed by @Thompson2017Dispersala is a good starting point to use for this 'case study' as it allows us some flexibility with how we want to parameterise the system. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores') and is a collection of modified Lotka–Volterra equations and (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches and looks as follows: +The model used broadly follows the metacommunity model developed by @thompsonDispersalGovernsReorganization2017. The model (@eq-metacomm) itself is based on a tritrophic community ('plants', 'herbivores', and 'carnivores'), and is essentially a collection of modified Lotka–Volterra equations, this (broadly) models species abundance as a function of interaction strength, environmental effect, immigration, and emigration. The metacommunity consists of $S$ species with $M$ environmental patches in the landscape and looks as follows: $$ X_{ij}(t+1)=X_{ij}(t)exp\left[C_{i} + \sum_{k=1}^{S}B_{ik}X_{kj}(t)+A_{ij}(t)\right]+I_{ij}(t)-X_{ij}(t)a_{i} $$ {#eq-metacomm} -Where $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.01 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is drawn from a uniform distribution with the parameters for the interaction pairs listed in @tbl-interaction_strength, the values drawn from the uniform distribution are scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair. +Where $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i$ is its intrinsic rate of increase (which we have set to 0.1 for 'plants' and -0.001 for 'herbivores' and 'carnivores'). $B_{ik}$ is the per capita effect of species $k$ on species $i$. The exact interaction strength for each species pair is determined by the trophic level of each species and is drawn from a uniform distribution. The ranges for each combination of species pairs listed in @tbl-interaction_strength, the values that are drawn from the uniform distribution are then scaled by dividing by $0.33S$ to yield the final interaction strength for each interacting pair. | Interacting pair | Range of uniform distribution| |---------------------|:----------------------------:| | Plant-plant | -1.0 -- 0.00 | -| Plant-herbivore | 0.0 -- 0.10 | +| Plant-herbivore | 0.0 -- 0.10 | | Plant-carnivore | 0.0 | | Herbivore-plant | -0.3 -- 0.00 | -| Herbivore-herbivore | -0.2-- -0.15 | -| Herbivore-carnivore | 0.0 -- 0.08 | +| Herbivore-herbivore | -0.2 -- -0.15 | +| Herbivore-carnivore | 0.0 -- 0.08 | | Carnivore-plant | 0.0 | | Carnivore-herbivore | -0.1 -- 0.00 | | Carnivore-carnivore | -0.1 -- 0.00 | @@ -71,14 +65,10 @@ Where $X_{ij}(t)$ is the abundance of species $i$ in patch $j$ at time $t$. $C_i $A_{ij}(t)$ is the effect of the environment in patch $j$ on species $i$ at time $t$ and can be further expanded as follows: $$ -\hat{A}_{ij}(t)=h\times\frac{1}{\sigma\sqrt{2\pi}}\exp-\frac{1}{2}\left(\frac{E_{j}(t)-H_{i}}{\sigma}\right)^2 -$$ - -$$ -A_{ij}(t)= \hat{A}_{ij}(t) - max(\hat{A}) +A_{ij}(t)=\left(h\times\frac{1}{\sigma\sqrt{2\pi}}\right)\times\left(e^{-\left(E_{j}(t)-H_{i}\right)^2/{2\sigma}^2}-1\right) $$ {#eq-metacomm_env} -Species environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to 300), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\sigma$ is the standard deviation (set to 50). +Where the species environmental optima ($H_i$) are evenly distributed across the entire range of environmental conditions for each trophic level, meaning that species from different trophic levels will be at, or near the same environmental optima. $h$ is a scaling parameter (set to **50**), $E_j(t)$ is the environment in patch $j$ at time $t$ and $\sigma$ is the standard deviation (set to **50**). $I_{ij}(t)$ is the abundance of species $i$ immigrating to patch $j$ at time $t$ and can be expanded as follows: @@ -91,24 +81,33 @@ Where $ai$ is the proportion of the population of species $i$ that disperses at | Trophic level | $\mu$ | $\sigma$ | |---------------|:-----:|:--------:| | Plant | 0.3 | 0.075 | -| Herbivore | 0.2 | 0.050 | +| Herbivore | 0.2 | 0.050 | | Carnivore | 0.1 | 0.025 | : Parameters for the normal distributions used to determine the dispersal decay ($L$) for each species depending on its trophic level. {#tbl-interaction_decay} ### Generating networks -More info on the baking process and the various connectivity stuff and whatnot +In order to create a final community state the species are allowed to persist for a total of 2000 generations. These generations are broken down into three 'phases' the first is the 'proofing' phase where the environment is uniform throughout the landscape (meaning that all species are at their environmental optimum) for 500 generations. After this the environment is 'heated' incrementally until it reaches its 'final state', the environmental optimum of each species is also adjusted as the environmental values begin to change. This occurs over a period of 1 000 generations. The landscape is then held stable for a further 500 generations until an equilibrium is reached. The final state of the landscape is predetermined and is defined by the diamond-square algorithm (this produces fractals with variable spatial autocorrelation) which is generated using `NeutralLandscapes.jl` [@catchenEcoJuliaNeutralLandscapesJl2023], here we vary the degree of landscape heterogeneity by **TODO**. + +| Parameter | Value | +|---------------|:-----:| +| $S$ | 100 | +| $M$ | 26*26 | +| $E_{initial}$ | 40 | +| $A_{initial}$ | 0.01 | + +:Starting parameters for the model. {#tbl-model_params} ### Spatial wombling -Broadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\theta$) of change. This is done by using first-order partial derivative ($\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indiaction how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\theta$) of the slope. +Broadly speaking spatial wombling is an edge-detection algorithm which traverses a geographic area and defines this area in terms of the rate ($m$) and corresponding direction ($\theta$) of change. This is done by using first-order partial derivative ($\partial$) of the 'curvature' of the landscape as described by $f(x,y)$ (see @eq-womble). This essentially gives an indication how steep the gradient ($m$) is between neighbouring cells as well as the direction ($\theta$) of the slope. $$ m = \sqrt{\frac{\partial f(x,y)}{\partial x}^2 + \frac{\partial f(x,y)}{\partial y}^2} $${#eq-womble} -The spatial wombling analyses were done using `SpatialBoundaries.jl` [@Strydom2023Spatialboundariesa]. The docuemntation provides a more detailed breakdown of the underlying methodology. +The spatial wombling analyses were done using `SpatialBoundaries.jl` [@strydomSpatialBoundariesJlEdge2023]. The documentation provides a more detailed breakdown of the underlying methodology. ## Conclusion diff --git a/references.bib b/references.bib index b7435e7..cd92057 100644 --- a/references.bib +++ b/references.bib @@ -1,21 +1,16 @@ -@article{Fortin2021Network, - title = {Network Ecology in Dynamic Landscapes}, - author = {Fortin, Marie-Jos{\'e}e and Dale, Mark R. T. and Brimacombe, Chris}, - year = {2021}, - month = apr, - journal = {Proceedings of the Royal Society B: Biological Sciences}, - volume = {288}, - number = {1949}, - pages = {rspb.2020.1889, 20201889}, - issn = {0962-8452, 1471-2954}, - doi = {10.1098/rspb.2020.1889}, - urldate = {2021-05-04}, - abstract = {Network ecology is an emerging field that allows researchers to conceptualize and analyse ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the `ecological network dynamics framework'. This framework stresses that the interplay between species interaction networks and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links) and trade-offs among them are needed to maintain species interactions in dynamic landscapes. We conclude that to be functional, ecological networks should be scaled according to species dispersal abilities in response to landscape heterogeneity. Determining how such effective ecological networks change through space and time can help reveal their complex dynamics in a changing world.}, - langid = {english}, - file = {/Users/tanyastrydom/Zotero/storage/2ZXU6ELP/Fortin et al. - 2021 - Network ecology in dynamic landscapes.pdf;/Users/tanyastrydom/Zotero/storage/XV5GJ2QE/Fortin et al. - 2021 - Network ecology in dynamic landscapes.pdf} +@misc{catchenEcoJuliaNeutralLandscapesJl2023, + title = {{{EcoJulia}}/{{NeutralLandscapes}}.Jl}, + author = {Catchen, Michael D.}, + year = {2023}, + month = dec, + urldate = {2024-03-13}, + abstract = {Generation of neutral landscapes in Julia.}, + copyright = {MIT}, + howpublished = {EcoJulia}, + keywords = {ecojulia,landscape-ecology} } -@article{fortinDelineationEcologicalBoundaries1995a, +@article{fortinDelineationEcologicalBoundaries1995, title = {Delineation of {{Ecological Boundaries}}: {{Comparison}} of {{Approaches}} and {{Significance Tests}}}, shorttitle = {Delineation of {{Ecological Boundaries}}}, author = {Fortin, Marie-Jos{\'e}e and Drapeau, Pierre}, @@ -26,14 +21,31 @@ @article{fortinDelineationEcologicalBoundaries1995a eprint = {3546117}, eprinttype = {jstor}, pages = {323--332}, - publisher = {{[Nordic Society Oikos, Wiley]}}, + publisher = {[Nordic Society Oikos, Wiley]}, issn = {0030-1299}, doi = {10.2307/3546117}, - urldate = {2021-01-27}, + urldate = {2022-04-21}, abstract = {In this study, quantitative spatial methods such as cluster analysis with spatial constraints and edge detection algorithms are compared with respect to their abilities to delimit boundaries from two-dimensional sampled data. While cluster analysis with spatial constraints forms clusters among neighboring sites that are similar, edge detection algorithms delimit areas of high rate of change. To determine whether the delineated boundaries could have arisen by chance, boundary and superfluity statistics are used and their statistical significance is assessed by permutation tests. Advantages and limits of each approach are illustrated using data sets of tree densities from a second growth stand of northern deciduous forest in southern Qu{\'e}bec, Canada. It is found (1) that applying jointly these two types of approaches provides complementary information about the location and the intensity of the delineated boundaries; and (2) that the boundary and superfluity statistics are useful for assessing the statistical significance of boundaries.} } -@article{fortinQuantificationSpatialCoOccurrences1996a, +@article{fortinNetworkEcologyDynamic2021, + title = {Network Ecology in Dynamic Landscapes}, + author = {Fortin, Marie-Jos{\'e}e and Dale, Mark R. T. and Brimacombe, Chris}, + year = {2021}, + month = apr, + journal = {Proceedings of the Royal Society B: Biological Sciences}, + volume = {288}, + number = {1949}, + pages = {rspb.2020.1889, 20201889}, + issn = {0962-8452, 1471-2954}, + doi = {10.1098/rspb.2020.1889}, + urldate = {2021-05-04}, + abstract = {Network ecology is an emerging field that allows researchers to conceptualize and analyse ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the `ecological network dynamics framework'. This framework stresses that the interplay between species interaction networks and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links) and trade-offs among them are needed to maintain species interactions in dynamic landscapes. We conclude that to be functional, ecological networks should be scaled according to species dispersal abilities in response to landscape heterogeneity. Determining how such effective ecological networks change through space and time can help reveal their complex dynamics in a changing world.}, + langid = {english}, + file = {/Users/tanyastrydom/Zotero/storage/2ZXU6ELP/Fortin et al. - 2021 - Network ecology in dynamic landscapes.pdf;/Users/tanyastrydom/Zotero/storage/XV5GJ2QE/Fortin et al. - 2021 - Network ecology in dynamic landscapes.pdf} +} + +@article{fortinQuantificationSpatialCoOccurrences1996, title = {Quantification of the {{Spatial Co-Occurrences}} of {{Ecological Boundaries}}}, author = {Fortin, Marie-Jos{\'e}e and Drapeau, Pierre and Jacquez, Geoffrey M.}, year = {1996}, @@ -43,10 +55,10 @@ @article{fortinQuantificationSpatialCoOccurrences1996a eprint = {3545584}, eprinttype = {jstor}, pages = {51--60}, - publisher = {{[Nordic Society Oikos, Wiley]}}, + publisher = {[Nordic Society Oikos, Wiley]}, issn = {0030-1299}, doi = {10.2307/3545584}, - urldate = {2021-01-25}, + urldate = {2022-04-11}, abstract = {In this paper, we investigate spatial relationships between vegetation boundaries and environmental boundaries from a second-growth forest in southwestern Qu{\'e}bec, Canada. Four statistics that quantify the amount of direct spatial overlap and the mean minimum distance between boundaries are introduced and used to compute the degree of spatial co-occurrences between boundaries. The significance of these statistics is determined using randomized and restricted permutation tests. Boundaries based on tree species density are found to significantly overlap the locations of boundaries delineated by the environmental data at the study site. Significant overlap is also found using boundaries defined by tree presence-absence data and environmental variables. Vegetation boundaries based on tree species density and on tree presence-absence data are not, however, at the same locations. This suggests that for the study site the two types of vegetation boundaries (tree density and presence-absence) reflect different responses to underlying environmental processes. Vegetation boundaries determined using species diversity and species richness, although spatially related to the presence-absence boundaries, did not overlap the environmental boundaries. Results of the two permutation tests (randomized and restricted) agree only when the spatial relationship between the two boundary types is strong. Overall, randomization is found to be a more conservative test for detecting boundary spatial relationships, rejecting the null hypothesis of no spatial relationship fewer times than the restricted permutation test.} } @@ -84,19 +96,20 @@ @article{strydomSpatialBoundariesJlEdge2023 file = {/Users/tanyastrydom/Zotero/storage/G3V7DM28/ecog.html} } -@article{thompsonDispersalGovernsReorganization2017a, +@article{thompsonDispersalGovernsReorganization2017, title = {Dispersal Governs the Reorganization of Ecological Networks under Environmental Change}, author = {Thompson, Patrick L. and Gonzalez, Andrew}, year = {2017}, - month = jun, + month = may, journal = {Nature Ecology \& Evolution}, volume = {1}, number = {6}, - publisher = {{Nature Publishing Group}}, - address = {{London, United States}}, + pages = {0162}, + issn = {2397-334X}, doi = {10.1038/s41559-017-0162}, - urldate = {2022-10-06}, - abstract = {Ecological networks, such as food webs, mutualist webs and host{\textendash}parasite webs, are reorganizing as species abundances and spatial distributions shift in response to environmental change. Current theoretical expectations for how this reorganization will occur are available for competition or for parts of interaction networks, but these may not extend to more complex networks. Here we use metacommunity theory to develop new expectations for how complex networks will reorganize under environmental change, and show that dispersal is crucial for determining the degree to which networks will retain their composition and structure. When dispersal between habitat patches is low, all types of species interactions act as a strong determinant for whether species can colonize suitable habitats. This colonization resistance drives species turnover, which breaks apart current networks and leads to the formation of new networks. However, when dispersal rates are increased, colonists arrive in high abundance in habitats where they are well adapted, so interactions with resident species contribute less to colonization success. Dispersal ensures that species associations are maintained as they shift in space, so networks retain similar composition and structure. The crucial role of dispersal reinforces the need to manage habitat connectivity to sustain species and interaction diversity into the future. Complex ecological networks are likely to be disrupted as species shift in response to environmental change. A simulation model shows that the level of dispersal determines whether species associations within networks are maintained.}, - copyright = {{\copyright} Macmillan Publishers Limited, part of Springer Nature. 2017.}, - langid = {english} + urldate = {2017-05-09}, + abstract = {Complex ecological networks are likely to be disrupted as species shift in response to environmental change. A simulation model shows that the level of dispersal determines whether species associations within networks are maintained.}, + copyright = {{\copyright} 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.}, + langid = {english}, + file = {/Users/tanyastrydom/Zotero/storage/N4GWCV5D/s41559-017-0162.html} }