| Title: | Analyse Biological Invasion Costs with the 'InvaCost' Database |
|---|---|
| Description: | Provides an up-to-date version of the 'InvaCost' database (<doi:10.6084/m9.figshare.12668570>) in R, and several functions to analyse the costs of invasive alien species (<doi:10.1111/2041-210X.13929>). |
| Authors: | Boris Leroy [cre, aut], Andrew Kramer [aut], Anne-Charlotte Vaissière [ctb], Christophe Diagne [ctb] |
| Maintainer: | Boris Leroy <[email protected]> |
| License: | GPL (>=2.0) |
| Version: | 1.1-6 |
| Built: | 2024-10-24 03:58:19 UTC |
| Source: | https://github.com/farewe/invacost |
This simple function calculates the cumulative cost average annual cost of
invasive alien species over a single period of time. It is used internally
by summarizeCosts.
computeAvgTotCost( costdb, cost.column = "Cost_estimate_per_year_2017_USD_exchange_rate", year.column = "Impact_year", min.year = NULL, max.year = NULL )computeAvgTotCost( costdb, cost.column = "Cost_estimate_per_year_2017_USD_exchange_rate", year.column = "Impact_year", min.year = NULL, max.year = NULL )
costdb |
The expanded 'InvaCost' database output from
|
cost.column |
Name of the cost column to use in |
year.column |
Name of the year column to use in |
min.year |
The minimum year of the period (specify only if different from the range of data) |
max.year |
The minimum year of the period (specify only if different from the range of data) |
a named list with 5 elements
initial_year: first year in the data
final_year: last year in the data
time_span: the difference between initial and final years.
total_cost: total cost.
annual.cost: cost per year
number_estimates: the number of cost estimates before expansion
via expandYearlyCosts
number_year_values: the number of costs per year included
Arguments min.year and max.year do not filter the data. Only
specify them if you wish to change the interval over which averages are
calculated. For example, if your data have values from 1960 to 1964 but you
want to calculated the average value from 1960 to 1969, set
min.year = 1960 and max.year = 1969.
However, if you want to calculate values for an interval narrower than your data, filter the data BEFORE running this function.
Boris Leroy [email protected], Andrew Kramer, Anne-Charlotte Vaissière, Christophe Diagne
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
expandYearlyCosts to get the database in appropriate format.
data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- computeAvgTotCost(db.over.time, min.year = 1960, max.year = 2020) # Excluding data after 2020 (e.g. planned budgets) resdata(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- computeAvgTotCost(db.over.time, min.year = 1960, max.year = 2020) # Excluding data after 2020 (e.g. planned budgets) res
For costs that occur over several years, this function repeats the cost value per year over each year of the relevant period of impact.
expandYearlyCosts(costdb, startcolumn, endcolumn)expandYearlyCosts(costdb, startcolumn, endcolumn)
costdb |
The 'InvaCost' database, usually obtained with
|
startcolumn |
Name of the column containing starting years |
endcolumn |
Name of the column containing ending years |
A data.frame containing the 'InvaCost' database where
all costs occurring over several years are repeated for each year.
Information on the beginning and ending years was not directly provided in
literature sources of economic costs for a substantial part of entries in the
database (Probable_starting_year and Probable_ending_year
columns).
Therefore, for papers for which this information was not available,
educated guesses were made by the 'InvaCost' team on the probable starting
and ending years. These educated guesses were designed
to be conservative, and make no assumption as of whether the economic impacts
have been continued after the publication year of the material where the cost
was collected.
Therefore, we used the publication year as the probable ending year. For
costs repeated over several years but for which no information with respect
to the exact periods of impact was available, we counted only a single year.
These educated guesses are included in the columns (columns
Probable_starting_year_adjusted and
Probable_ending_year_adjusted), and we recommend using them the base
scenario, as its conservative assumptions limit over-estimations of the
costs.
Boris Leroy [email protected], Andrew Kramer, Anne-Charlotte Vaissière, Christophe Diagne
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted")data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted")
This function will download the requested major release of 'InvaCost' to the specified file, then read it into R, such that analyses done on older releases of the database can be reproduced.
getInvaCostVersion(version = "4.1", destination_file = NULL)getInvaCostVersion(version = "4.1", destination_file = NULL)
version |
|
destination_file |
|
The public archive for 'InvaCost' releases is available here: https://figshare.com/articles/dataset/InvaCost_References_and_description_of_economic_cost_estimates_associated_with_biological_invasions_worldwide_/12668570
The files used in this function correspond to official releases by the
'InvaCost' team and are downloaded in CSV (sep = ";") from a dedicated
GitHub repository: https://github.com/Farewe/invacost_versions
a data.frame with dimensions variable depending on the chosen
version.
Boris Leroy [email protected], Andrew Kramer, Anne-Charlotte Vaissière, Christophe Diagne
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
invacost <- getInvaCostVersion("1.0") dim(invacost)invacost <- getInvaCostVersion("1.0") dim(invacost)
The 'InvaCost' database compiling published values of economic costs of Invasive Alien Species. Version 4.1
data(invacost)data(invacost)
A data frame with 13553 rows and 66 variables
Public unique identifier of the cost entry, which is used since the version 4.1 of the database; this is formulated as follows: 'IC_x_y' with 'IC' meaning InvaCost, 'x' being the version number of the database, and 'y' being the sequential number attributed following the order of integration of costs in the database
Original identifier of the cost entry, as proposed by the contributors and then confirmed/modified by the database managers to ensure consistency within the whole database. This code is used to designate the cost entry for (i) internal tracing of the cost estimate by the database managers and (ii) identifying the cost entry when it is mentioned in the Overlap column
Literature engine (Web of Science (WoS), Google Scholar (GS), Google search engine (Go), Pubmed, Scielo) or original source (Targeted collection (TC)) from which the reference was collected (see Diagne et al. 2020 Scientific Data for further details); cells are left empty when no repository was specified or the reference was shared by external users that did not give any information about this
Identifier for the reference where the cost entry is reported; note that this field is currently being improved internally to have a consistent terminology across references within the database
Title of the reference where the cost entry is reported. As much as possible, this is the original source where the cost was first provided
Authors of the reference where the cost entry is reported
If existing/accessible, the abstract of the reference where the cost entry is reported
Year of publication of the reference where the cost entry is reported
Main language used in the original reference reporting the cost entry; 22 languages are currently recorded in the database: Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hindi, Icelandic, Italian, Japanese, Norwegian, Portuguese, Russian, Spanish, Swedish, Ukrainian
Type of reference analyzed (i.e. scientific peer-reviewed article or grey literature); for grey literature, the exact nature of the reference was indicated (e.g., official report, press release)
If any, the list of successive materials checked before reaching the original reference providing the cost entry
The accessibility of the original reference as a searchable document (yes/no)
Taxonomic kingdom of the invasive species associated with the cost entry
Taxonomic phylum of the invasive species associated with the cost entry
Taxonomic class of the invasive species associated with the cost entry
Taxonomic order of the invasive species associated with the cost entry
Taxonomic family of the invasive species associated with the cost entry
Taxonomic genus of the invasive species associated with the cost entry
Taxonomic species of the invasive species associated with the cost entry
Taxonomic sub-species of the invasive species associated with the cost entry
Non-scientific (or vernacular) name(s) provided in the original reference, or by the International Union for Conservation of Nature (IUCN) when not provided
Type of environment (aquatic, terrestrial, semi-aquatic, diverse/unspecified) where the cost estimate occurred
Type of environment where the invasive species lives, independently of where the cost occurred: aquatic (species with a close association with aquatic systems at any life stage, including for reproduction, development and/or foraging), semi-aquatic (species with a looser association with aquatic systems) or terrestrial (otherwise); diverse/unspecified is used when there are multiple IAS pertaining to different environments.
Copy from the original reference of the sentence/paragraph indicating the habitat typology of the studied area
The type of habitat where the cost occurred (1.Forests: closed vegetation dominated by deciduous or evergreen trees; 2.Open forests: woodland vegetation with canopy openings created by environmental stress or disturbance; 3.Scrub: shrublands maintained by environmental stress (aridity) or disturbance; 4.Grasslands: open graminoid-dominated habitats maintained either by climate (steppes, prairies, savannas) or land-use (grazing, mowing) or combination of both – if possible, specified if it corresponds with 4a.Natural grasslands or 4b.Human-maintained grasslands; 5.Sandy: dunes and other habitats on unstable sandy substrate, stressed by low nutrients, drought and disturbed by sand movement; 6.Rocky: cliffs and rock outcrops with very shallow or no soil; 7.Dryland: habitats in which drought stress limits vegetation development; 8.Saline: habitats stressed by high soil salinity; 9.Riparian: a mosaic of wetlands, grasslands, tall-forb stands, scrub and woodlands in stream corridors; 10.Wetland: sites with permanent or seasonal influence of moisture, ranging from oligotrophic to eutrophic; 11.Aquatic: water bodies and streams with submerged and floating plant species; 12.Man-made: habitats created by humans or where human factor is the main shaping force - if possible, specified if it corresponds with 12a.Ruderal (= urban) or 12b.Agricultural habitats
Assessment of the geographical area where the cost occurred as being strictly urban if the cost occurred in an urban area only; other if the cost occurred in a non-urban area or in a large area where urban and non-urban areas cannot be distinguished; both if the study compares the cost between urban and non-urban areas (i.e. urban vs. suburban, or suburban vs. non-urban; the comparison must be explicit) or presents the cost across contrasted areas regarding their level of urbanization (e.g. urbanization gradient). Note that we consider the urban nature of study sites purely based on an 'habitat' perspective (i.e. city versus rural areas) rather than a demographic one (e.g. population size or human density)
Assessment of the geographical area where the cost occurred as a protected area (Y) or not (N); NA is used if the area comprises both protected and unprotected areas, or if the protection status of the place is unknown
Assessment of the geographical area where the cost occurred as an island (Y) or not (N); NA is used when the cost information is not clearly provided, unknown, or comprises both island and mainland together
Geographical region(s) where the cost occurred (Africa, Antarctic-Subantarctic, Asia, Central America, Europe, North America, Oceania, Pacific islands, South America); Diverse/unspecified was used when the cost estimate occurred over several regions simultaneously
Country where the cost occurred; sometimes, this is not congruent with the geographic region as some territories (e.g., overseas areas) are located in another continent than their official country of attachment; we used information from www.naturalearthdata.com/ as a reference for country's name
The second level of geographic division (state, province or territorial administrative area) for the official country where the cost occurred
When provided, the precise location (e.g., city, area) where the cost estimate occurred. Otherwise, we put (i) NA when such information was not provided and the cost entry was provided at a unit or site Spatial scale, or (ii) diverse/unspecified when such information was not provided and the cost entry was provided at global, intercontinental, continental, regional or country Spatial scale
Order of magnitude of the extent, size of the land/water area where the costs incurred. Options include: global (worldwide-scale), intercontinental (areas from two or more geographic regions), continental ('Geographic region' level), regional (several countries within a single 'Geographic region'), country, site (area at intra-country level, including USA states) and unit (well-defined surface area or entity)
If provided, the exact period of time covered by the cost, otherwise the raw formulation provided in the reference analyzed (e.g. late 90s, during 5 years)
Two options: period if the cost is given for a period exceeding a year; or year if the cost is given yearly or for a period up to one year. Alternatively, we put Unspecified if no information is given or guessable from the source
Year in which the cost is known or assumed to have started to occurred. When not explicitly provided by the authors, we mentioned unspecified; in the case of a cost estimate provided for a one-year period straddling two calendar years, we mentioned the latest year of the cost occurrence.
Year in which the cost is known or assumed to have ended. When not explicitly provided by the authors, we mentioned unspecified; in the case of a cost estimate provided for a one-year period straddling two calendar years, we mentioned the latest year of the cost occurrence.
Probable starting year and Probable ending year columns where the cells with unspecified information are replaced, as much as possible, by a specific year from educated guesses based on the duration time provided in the original reference (see Period of estimation column). When relevant (e.g. the authors provide a cost that occurs "since/for a well-defined number of years"), we considered the Publication year as a reference for the probable starting/ending year from which we added or subtracted the number of years provided; when vague formulations were used (e.g., early 90s), we still translated them in probable ending/starting year (e.g., 1990–1995); when annual costs were provided, but without clear information on the temporal range, we conservatively considered the year of the cost occurrence (or the Publication year, if not provided) in both columns. When no relevant approximation is feasible, we leave blank in one and/or the other column(s). These columns are those used for obtaining the number of years by which the raw cost estimates are divided to get the cot estimates per year
Probable starting year and Probable ending year columns where the cells with unspecified information are replaced, as much as possible, by a specific year from educated guesses based on the duration time provided in the original reference (see Period of estimation column). When relevant (e.g. the authors provide a cost that occurs "since/for a well-defined number of years"), we considered the Publication year as a reference for the probable starting/ending year from which we added or subtracted the number of years provided; when vague formulations were used (e.g., early 90s), we still translated them in probable ending/starting year (e.g., 1990–1995); when annual costs were provided, but without clear information on the temporal range, we conservatively considered the year of the cost occurrence (or the Publication year, if not provided) in both columns. When no relevant approximation is feasible, we leave blank in one and/or the other column(s). These columns are those used for obtaining the number of years by which the raw cost estimates are divided to get the cot estimates per year
Status of the cost estimate as potentially ongoing (if the cost can be expected to continue over time) or one-time (if the cost was deemed as unlikely to continue)
Cost estimate directly retrieved from the analyzed reference
Lower boundary of the Raw cost estimate original currency (if a range of estimates was provided by the authors)
Higher boundary of the Raw cost estimate original currency (if a range of estimates was provided by the authors)
Raw cost estimate original currency standardised from original Currency and Applicable year to 2017 US$ based on the official market exchange rate (original currency unit per US$) provided by the World Bank Open Data (available at https://data.worldbank.org/indicator/PA.NUS.FCRF?end=2017&start=1960)
Raw cost estimate original currency standardized to 2017 US$ based on the official Purchase Power Parity (PPP; original currency unit per US$) provided by the World Bank Open Data (available at https://data.worldbank.org/indicator/PA.NUS.PPP?end=2017&start=1990) and the Organisation for Economic Cooperation and Development (available at https://data.oecd.org/conversion/purchasing-powerparities-ppp.htm)
Raw cost estimate original currency transformed to a cost estimate per year of the Period of estimation, which was obtained by dividing the raw cost estimate by the number of years between the Probable starting year_completed and Probable ending year_completed. Blank cells are those that have no information in at least one of these two previous columns
Cost estimate per year original currency standardized from original Currency and Applicable year to 2017 USD based on the official market exchange rate (original currency unit per US$) provided by the World Bank Open Data (available at https://data.worldbank.org/indicator/PA.NUS.FCRF?end=2017&start=1960). Blank cells are those that have no information in at least one of these two previous columns
Cost estimate per year original currency standardized from original Currency and Applicable year to 2017 USD based on Purchase Power Parity (PPP; original currency unit per US$) provided by the World Bank Open Data (available at https://data.worldbank.org/indicator/PA.NUS.PPP?end=2017&start=1990) and the Organisation for Economic Cooperation and Development (available at https://data.oecd.org/conversion/purchasing-powerparities-ppp.htm). Blank cells are those that have no information in at least one of these two previous column
Currency of the Raw cost estimate original currency as extracted in the original reference and standardised (when possible) using internationally recognized ISO 4217 codes (https://www.iso.org/fr/iso-4217-currency-codes.html); 50 currencies are currently recorded in InvaCost: ARS (Argentine peso), AUD (Australian Dollars), CAD (Canadian Dollars), CHF (Swiss Fanc), CLP (Chilean Peso), COL (Colombian Peso), CUP (Cuban Peso), CZK (Czech Crown), DEM (Deutsche Mark), DKK (Danish Krone), ESP (Spanish Peseta), EUR (Euro), FJD (Fijian Dollar), FRF (French Franc), GBP (Pound Sterling), HKD (Hong Kong Dollars), HRK (Croatian Kuna), INR (Indian Rupee), ISK (Icelandic Crown), JPY (Yen), KES (Kenyan Shilling), LKR (Sri Lankan Rupee), LTL (Lithuanian Litas), MAD (Moroccan Dirhams), MGA (Malagasy Ariary), MXN (Mexican Peso), NGN (Naira), NLG (Dutch Guilder), NOK (Norwegian Krone), NZD (New Zealand Dollar), PKR (Pakistani Rupee), PLN (Polish Zoty), REA (Brazilian Real), CNY (Yuan Renminbi), RUB (Russian Ruble), SEK (Swedish Krona), SGD (Singapore Dollar), SOL (Peruvian Sol), TND (Tunisian Dinar), TRY (Turkish Lira), TWD (New Taiwan Dollar), UAH (Ukrainian Hryvnia), USD (United States Dollars), UYU (Uruguay New Peso), XAF ( CFA Franc BEAC), XOF (CFA franc BCEAO), XPF (Pacific Franc), ZAR (South African Rand), ZWL (Zimbabwean Dollar)
Year of the Currency value (not the year of the cost occurrence) considered for the conversion/standardization of the cost estimate
Assessment of the applicable year as effective if explicitly stated by the authors or publication year if no explicit information was provided in the reference analyzed
This states — at the time of the estimation — whether the reported cost was actually observed (i.e., cost actually incurred) or potential (i.e., not incurred but expected cost)
Method used to obtain the cost estimate: report/estimation directly obtained or derived (using inference methods) from field-based information or extrapolation (cost predicted beyond the original spatial and/or temporal observation range from computational modelling)
Sector impacted by the cost estimate in our socio-ecosystems: Agriculture (considered at its broadest sense, food and other useful products produced by human activities through using natural and/plant resources from their ecosystems such as crop growing, livestock breeding, beekeeping, land management); Authorities-Stakeholders (governmental services and/or official organisations such as conservation agencies, forest services, associations) that allocate efforts for the management sensu lato of biological invasions (e.g. control programs, eradication campaigns, research funding) ; Environment (impacts on natural resources, ecological processes and/or ecosystem services that have been valued by authors such as disruption of native habitats or degradation of local habitats); Fishery (fish-based activities and services such as fishing and aquaculture); Forestry (forest-based activities and services such as timber production/industries and private forests); Health (every item directly or indirectly related to the sanitary state of people such as vector control, medical care and other derived damage on human productivity and well-being); Public and social welfare (activities, goods or services contributing - directly or indirectly - to the human well-being and safety in our societies, including local infrastructures such as electric system, quality of life (e.g. income, recreational activities), personal goods (e.g. private properties, lands), public services (e.g. transports, water regulation), and market activities (e.g. tourism, trade)); Unspecified if no information is given in the source.
A variety of terms/categories pertianing to damage and losses incurred by an invasion (e.g. damage-loss, damage repair, medical care, crop losses) or means dedicated to understand or predict (e.g., research), prevent (e.g., education, biosecurity), detect (e.g., monitoring, surveillance) and/or manage (e.g., control, eradication) the invasions; Unspecified is mentionned if no information is given or guessable from the source
Categories of the Type of cost column reassigned into damage (economic losses due to direct and/or indirect impacts of invaders, such as yield loss, health injury, land alteration, infrastructure damage, or income reduction), management (monetary resources allocated to mitigate the spread or impacts of invaders, such as prevention, control, research, long-term management, eradication) or mixed (when when costs included both ‘damage’ and ‘management’ components); every cost for which the exact nature of cost was not clearly defined was assigned to unspecified
Pre-invasion_management (monetary investments for preventing successful invasions in an area - including quarantine or border inspection, risk analyses, biosecurity management, etc.), post-invasion_management (money spent for managing invasions in invaded areas - including control, eradication, containment), knowledge funding (money allocated to all actions and operations that could be of interest at all steps of management at pre- and post-invasion stages - including administration, communication, education, research, etc.), or mixed was assigned when costs include at least (and without possibility to disentangle the specific proportion of) two of the previous categories; every management cost (in the Type of cost column) for which the exact nature of cost was not clearly defined was assigned to unspecified. Every entry that has partly or fully associated with damage costs was assigned to NA
Assessment of the methodological approach used for cost estimation as of (i) high reliability if either provided by officially pre-assessed materials (peer-reviewed articles and official reports) or the estimation method was documented, repeatable and/or traceable if provided by other grey literature, or (ii) low reliability if not
Assessment of the methodological approach used for cost estimation as of high or low reliability based on the evaluation of the estimation methodology by expert contributors
Detailed explanation why a particular methodological approach used for cost estimation was deemed as of high or low reliability based on expert opinion
Complete name and contact details of the expert had deemed the reliability of the cost entry
List of cost entries (using Cost_ID) that might overlap with other cost entry(s) – this is noted as follows: Z(V/W) when the overlap is known and one or more cost ID (V,W) are included in another cost ID (Z); Z/V/W: when the overlap is not clear; and Z(V); Z/W if there are more than one unrelated overlaps
Mention (if any) of the benefit value in the analyzed material (yes/no); 'benefit' refers here to a monetary estimate associated with profitable activities based on IAS. This definition therefore excludes any economic gain based on avoided or mitigated effects of the IAS (for instance, due to efficient management actions over time that may result in apparent benefits from avoidance of damages) - the latter is considered as an “avoided cost” in the InvaCost database. Note that the benefit figure was not recorded or described as being out of the scope of InvaCost
When necessary, narrative elements deemed important either to understand the cost estimate or to support choices made for completing the database; this column was left unchanged from the original entries in order to allow trace-back investigations
Name of contributor(s) having collated the cost entry
Assessment of cost information collated (by at least) two contributors; yes if it has been double checked, no if it has not. The names/contacts of each contributor are provided in the Initial contributors_names column
doi:10.6084/m9.figshare.12668570
Diagne, C., Leroy, B., Gozlan, R. E., Vaissière, A. C., Assailly, C., Nuninger, L., Roiz, D., Jourdain, F., Jaric, I., & Courchamp, F. (2020). InvaCost, a public database of the economic costs of biological invasions worldwide. Scientific Data, 7(1), 1–12. doi:10.1038/s41597-020-00586-z
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
History of database releases: doi:10.6084/m9.figshare.12668570
This function fits different models on 'InvaCost' data expressed per year in order to estimate and predict the trend of invasive alien Species costs over time.
modelCosts( costdb, cost.column = "Cost_estimate_per_year_2017_USD_exchange_rate", year.column = "Impact_year", cost.transf = "log10", in.millions = TRUE, confidence.interval = 0.95, minimum.year = 1960, maximum.year = max(costdb[, year.column]), final.year = max(costdb[, year.column]), incomplete.year.threshold = NULL, incomplete.year.weights = NULL, gam.k = -1, mars.nprune = NULL, ... )modelCosts( costdb, cost.column = "Cost_estimate_per_year_2017_USD_exchange_rate", year.column = "Impact_year", cost.transf = "log10", in.millions = TRUE, confidence.interval = 0.95, minimum.year = 1960, maximum.year = max(costdb[, year.column]), final.year = max(costdb[, year.column]), incomplete.year.threshold = NULL, incomplete.year.weights = NULL, gam.k = -1, mars.nprune = NULL, ... )
costdb |
The expanded 'InvaCost' database output from
|
cost.column |
Name of the cost column to use in |
year.column |
Name of the year column to use in |
cost.transf |
Type of transformation you want to apply on cost values.
The default is a log10 transformation, which is commonly applied in
economics,
allows to fit linear regression with a normal distribution of residuals,
and makes plots easier to read. You can apply another transformation by
specifying the name of the transformation function (e.g., natural
logarithm, |
in.millions |
If |
confidence.interval |
A numeric value between 0 and 1, corresponding to the desired confidence interval around model predictions |
minimum.year |
The starting year of this analysis. By default, 1960 was chosen because it marks the period from which world bank data is available for exchange rates and inflation values |
maximum.year |
The ending year for this analysis. By default, the last
year of |
final.year |
The year for which the costs predicted by models is
printed in the output. Default is the last year of |
incomplete.year.threshold |
Estimated threshold for incomplete cost data. All years above or equal to this threshold will be excluded from model calibration, because of the time-lag between economic impacts of invasive alien species and the documentation and publication of these impacts |
incomplete.year.weights |
A named vector containing weights of years for the regressions. Useful to decrease the weights of incomplete years in regressions. Names of this vector must correspond to years |
gam.k |
The smoothing factor of GAM; default value of -1 lets the GAM find the smoothing factor automatically. Provide a manual value if you have expectations about the shape of the curve and want to avoid overfitting because of interannual variations |
mars.nprune |
The maximum number of model terms in the MARS model. Lowering this value reduces the number of terms in the MARS model, which can be useful if you have expectations about the shape of the curve and want to reduce the impact of interannual variations |
... |
Other arguments (you do not need them!) |
A list with 3 to 6 elements (only the first three are
provided if you selected a cost transformation different from log10):
input.data: the input cost data, for reproducibility of
analyses
cost.data: the costs of invasions per year, as sums of all
costs for each year
parameters: parameters used to run the function. The
minimum.year and maximum.year are based on the input data
(i.e., the user may specify minimum.year = 1960 but the input data may
only have data starting from 1970, hence the minimum.year will be
1970)
fitted.models: a list of objects containing the fitted models.
They can be extracted individually for refining analyses or making new
predictions
estimated.annual.costs: a data.frame containing the predicted
cost values for each year for all the fitted models
RMSE: an array containing RMSE of models for the calibration
data and for all data. NOTE: the RMSE for Quantile Regressions is not a
relevant metric, IGNORE it unless you know what you are doing!
final.year.cost: a vector containing the estimated annual
costs of invasive alien species based on all models for final.year.
The structure of this object can be seen using str().
Boris Leroy [email protected], Andrew Kramer, Anne-Charlotte Vaissière, Christophe Diagne
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
expandYearlyCosts to get the database in appropriate format.
data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- modelCosts(db.over.time) resdata(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- modelCosts(db.over.time) res
This function provides different plotting methods for the estimated annual cost of invasive species based on the temporal trend of costs.
## S3 method for class 'invacost.costmodel' plot( x, plot.breaks = 10^(-15:15), plot.type = "facets", models = c("ols.linear", "ols.quadratic", "robust.linear", "robust.quadratic", "gam", "mars", "quantile"), evaluation.metric = FALSE, graphical.parameters = NULL, ... )## S3 method for class 'invacost.costmodel' plot( x, plot.breaks = 10^(-15:15), plot.type = "facets", models = c("ols.linear", "ols.quadratic", "robust.linear", "robust.quadratic", "gam", "mars", "quantile"), evaluation.metric = FALSE, graphical.parameters = NULL, ... )
x |
The output object from |
plot.breaks |
a vector of numeric values indicating the plot breaks for the Y axis (cost values) |
plot.type |
|
models |
the models the user would like to appear in the plots. Can be any subset of the models included in 'modelCosts'. Default is all models. |
evaluation.metric |
|
graphical.parameters |
set this to |
... |
additional arguments, none implemented for now |
Error bands represent 95
regression, GAM and quantile regression. We cannot construct confidence
intervals around the mean for MARS techniques. However, we can estimate
prediction intervals by fitting a variance model to MARS residuals. Hence,
the error bands for MARS model represent 95
by fitting a linear model to the residuals of the MARS model. To learn more
about this, see varmod
If the legend appears empty (no colours) on your computer screen, try to zoom in the plot, or to write to a file. There is a rare bug where under certain conditions you cannot see the colours in the legend, because of their transparency; zooming in or writing to a file are the best workarounds.
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- modelCosts(db.over.time, minimum.year = 1970, maximum.year = 2020) ### Visualisation plot(res) plot(res, plot.type = "single")data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- modelCosts(db.over.time, minimum.year = 1970, maximum.year = 2020) ### Visualisation plot(res) plot(res, plot.type = "single")
This function provides different plotting methods for the raw average annual cost of invasive species over different periods of time
## S3 method for class 'invacost.costsummary' plot( x, plot.breaks = 10^(-15:15), plot.type = "points", average.annual.values = TRUE, cost.transf = "log10", graphical.parameters = NULL, ... )## S3 method for class 'invacost.costsummary' plot( x, plot.breaks = 10^(-15:15), plot.type = "points", average.annual.values = TRUE, cost.transf = "log10", graphical.parameters = NULL, ... )
x |
The output object from |
plot.breaks |
a vector of numeric values indicating the plot breaks for the Y axis (cost values) |
plot.type |
|
average.annual.values |
if |
cost.transf |
Type of transformation you want to apply on cost values.
Specify |
graphical.parameters |
set this to |
... |
additional arguments, none implemented for now |
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- summarizeCosts(db.over.time, minimum.year = 1970, maximum.year = 2020) ### Visualisation plot(res) plot(res, plot.type = "bars")data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- summarizeCosts(db.over.time, minimum.year = 1970, maximum.year = 2020) ### Visualisation plot(res) plot(res, plot.type = "bars")
This function is useful for presenting the main results (coefficients, tests, etc.) of models in a paper
prettySummary(x)prettySummary(x)
x |
output object from |
Boris Leroy [email protected], Andrew Kramer, Anne-Charlotte Vaissière, Christophe Diagne
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- modelCosts(db.over.time) prettySummary(res)data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- modelCosts(db.over.time) prettySummary(res)
This function summarizes the cumulative costs and average annual costs of invasive alien species and breaks it down into regular periods of time, on the basis of cost estimates as they appeared in the provided in the source references collected in the 'InvaCost' database
summarizeCosts( costdb, cost.column = "Cost_estimate_per_year_2017_USD_exchange_rate", year.column = "Impact_year", in.millions = TRUE, minimum.year = 1960, maximum.year = max(costdb[, year.column]), year.breaks = seq(minimum.year, maximum.year, by = 10), include.last.year = TRUE )summarizeCosts( costdb, cost.column = "Cost_estimate_per_year_2017_USD_exchange_rate", year.column = "Impact_year", in.millions = TRUE, minimum.year = 1960, maximum.year = max(costdb[, year.column]), year.breaks = seq(minimum.year, maximum.year, by = 10), include.last.year = TRUE )
costdb |
The expanded 'InvaCost' database output from
|
cost.column |
Name of the cost column to use in |
year.column |
Name of the year column to use in |
in.millions |
If |
minimum.year |
The starting year of the analysis. By default, 1960 was chosen because it marks the period from which world bank data is available for exchange rates and inflation values. |
maximum.year |
The ending year for the analysis. By default, the last
year of |
year.breaks |
A vector of breaks for the year intervals over which you want to summarize cost values |
include.last.year |
|
Missing data are ignored. However, note that the average for each interval is always calculated on the basis of the full temporal range. For example, if there is only data for 1968 for the 1960-1969 interval, then the total cost for the interval will be equal to the cost of 1968, and the average annual cost for 1960-1969 will be the cost of 1968 / 10.
A list with 6 elements:
cost.data: the input data
parameters: parameters used to run the function. The
minimum.year and maximum.year are based on the input data
(i.e., the user may specify minimum.year = 1960 but the input data may
only have data starting from 1970, hence the minimum.year will be
1970)
year.breaks: the years used to define year intervals over which costs were calculated.
cost.per.year: the costs of invasions expressed per year,
as sums of all costs for each year
average.total.cost: the average annual cost of invasive Alien
species calculated
over the entire time period
average.cost.per.period: a data.frame containing the average
annual cost of invasive alien species calculated over each time interval
The structure of this object can be seen using str().
Boris Leroy [email protected], Andrew Kramer, Anne-Charlotte Vaissière, Christophe Diagne
https://github.com/Farewe/invacost
Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929
expandYearlyCosts to get the database in appropriate format.
data(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- summarizeCosts(db.over.time, maximum.year = 2020) # Excluding data after 2020 (e.g. planned budgets) resdata(invacost) ### Cleaning steps # Eliminating data with no information on starting and ending years invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ] invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ] # Keeping only observed and reliable costs invacost <- invacost[invacost$Implementation == "Observed", ] invacost <- invacost[which(invacost$Method_reliability == "High"), ] # Eliminating data with no usable cost value invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ] ### Expansion db.over.time <- expandYearlyCosts(invacost, startcolumn = "Probable_starting_year_adjusted", endcolumn = "Probable_ending_year_adjusted") ### Analysis res <- summarizeCosts(db.over.time, maximum.year = 2020) # Excluding data after 2020 (e.g. planned budgets) res