DOI: 10.5061/dryad.wstqjq2x3
title: Remove saplings early: Cost effective strategies to contain tree invasions and prevent their impacts

description: There is an urgent need to design management strategies to reduce invasive
 species spread and impact, but the large spatial and temporal scales of
 most biological invasions make them challenging environments in which to
 conduct field studies. In this context, simulation models can play a key
 role in informing invasive species management. Woody plants are among the
 most harmful invaders, yet an evidence base to support different
 management objectives for these species remains poorly developed. Pines
 (Pinus) have been intensively studied, in terms of demography, dispersal,
 spread and impact, which makes them an ideal study system to model
 invasions. Using a multiyear database of observations of an invasive
 population, we employed an approximate Bayesian computation to fit an
 individual-based spatially-explicit model to compare management strategies
 to reduce the spread, population size and impact of a woody invader, Pinus
 contorta (pine hereafter), on grasslands in Patagonia. We simulated a pine
 population spreading from a plantation into a grassland over 50 years.
 Annual control actions in the grasslands started as soon as pines started
 spreading (when the pines from the plantation become reproductive) or were
 delayed at 10-year intervals. For control actions, we targeted different
 pine life stages, prioritized different locations in the landscape, and
 explored a wide range of available budgets. Removing saplings was the most
 cost-effective way to reduce pine spread and population size, whereas
 reducing management delay had a stronger effect on minimizing pine
 invasion impact on native grassland productivity. Focusing only on
 invasive adults was ineffective because it was costly, and it allowed a
 buildup in the population size of other stages which soon became adults
 (and started spreading seeds). Synthesis and applications: Our
 highest-ranking strategies represent management actions to start
 implementing in the field as part of an adaptive management plan that
 iteratively evaluates the validity of our simulation model and updates the
 management recommendations. Our study can be applied to guide management
 of invasive pines and replicated with any invasive woody species with
 sufficient data.
description: We conducted virtual experiments applying a fully factorial
 design, whereby we ran five replicates of all the combinations of levels
 of the variables ‘management delay’, ‘stage targeted’, ‘control location’,
 and ‘total budget’. We simulated a pine population for 50 years, starting
 from a 7-year-old plantation (already producing seeds), adjacent to a
 native grassland. Management actions took place exclusively in the
 grassland, since the original plantation (southernmost eight cells) was
 assumed to be grown for commercial purposes. We simulated delayed control
 actions at 0, 10, 20, 30 or 40 years (from the moment pines started
 spreading, when the pines from the plantation become reproductive) to
 assess the consequences of delaying management. These incremental delays
 were based on real world scenarios where pine invasive populations have
 been spreading for one to four decades without management. Once control
 actions started, they were carried out annually. We targeted different
 life stages for control: seedlings (1-2 year old pines), saplings (3-6
 years), subadults (7-12 years), adults (13 years or more) or all four
 stages (see Table 1 for a detailed description of the life stages). In
 scenarios where all life stages were removed simultaneously, different
 stages were selected at random until all trees were removed, or the
 available budget was spent. We selected cells for control actions at
 different locations of the landscape, according to different
 prioritization criteria (hereafter control location): 1. The random
 criterion selects occupied cells for management arbitrarily across the
 landscape. 2. The invasion front criterion prioritizes recently colonized
 cells. 3. The pine density criterion prioritizes cells with the highest
 population size of the targeted stage(s). 4. The impact criterion
 prioritizes cells which have higher accumulated impact on native grassland
 productivity. To quantify this impact of pine invasion on grassland
 productivity, we built density impact curves relating the effect of
 increasing pine density (for each pine stage) on native grassland
 productivity, based on data obtained from <em>P. contorta</em>
 invasions in native grasslands in Northwest Patagonia, Argentina (Moyano
 et al., 2023). These curves were fitted using an asymptotic function, with
 one single parameter (beta), which we included in our sensitivity
 analyses. We also modified the total available budget,
 which limits the area of the landscape that can be managed each year (see
 below), to identify the minimum budget that achieves containment. To do
 so, first we explored a range of budgets, from 0 US$/year to 10000
 US$/year, in increments of 500 US$/year to find approximate levels of
 investment sufficient to contain the invasive population for each level of
 management delay (which we expected to increase management costs). Once we
 identified these (e.g., 500 US$/year was enough for containment with no
 management delay), we reduced them in intervals of 100$/year to find a
 more precise minimum containment budget (e.g., 200 US$/year was the
 minimum budget that contained the invasive pines if management was not
 delayed). We calculated management extent (i.e., the
 number of cells that could be managed every year) based on the cost of
 mechanical control actions, 2023 US$, of invasive <em>P.
 contorta</em> populations in National Reserve Malalcahuello, Chilean
 Patagonia (Naour et al., 2016). From experimental management plots, we
 obtained the total labor cost of removing individual pines of each stage.
 We carried out a simulated experiment in RS without management to obtain
 average densities for each pine stage across the invaded landscape in the
 absence of management. We used these densities to calculate an average
 cost per managed cell. For example, to calculate the average cost of
 removing all adults from a cell, we multiplied the cost of removing each
 adult by the average number of adults per cell. By dividing the total
 budget (US$/year) by the cost of removing pines in each cell, we obtained
 the total number of cells that could be managed per year. These values
 ranged from 1% to 100% of the invaded area, depending on the targeted
 stage(s) and the available budget. Smaller pine stages are more difficult
 to find in the field and, therefore, we assigned an increasing probability
 of detection to each pine stage, with their corresponding removal
 percentage. As a result, we assumed that, for each cell selected for
 management, control actions effectively removed 80% of seedlings (i.e. 20%
 of seedlings were missed because of their small size), 90% of saplings,
 and 99% of both subadults and adults. Annual control
 actions were applied before dispersal. We assumed that local population
 size was imperfectly known by drawing values from a normal distribution
 centered on the true count and specifying a coefficient of variation of
 5%. To assess if increasing this coefficient of variation in detected
 population size affected our response variables, we increased it to 7.5%
 and 10% and evaluated the proportion of variability in our response
 variables explained by these changes.  Each simulation was replicated five
 times. By the end of each simulation (year 50) we calculated three
 response variables: Pine invasion extent (the total number of grassland
 cells occupied by reproductive pines), invasive pine population size (the
 total number of pines of age &gt; 0 years across the grassland) and
 pine invasion impact on grassland productivity (the accumulated impact of
 pine invasion on native grassland productivity across the
 landscape). References Moyano, J.,
 Zamora-Nasca, L. B., Caplat, P., García-Díaz, P., Langdon, B., Lambin, X.,
 Montti, L., Pauchard, A., & Nuñez, M. A. (2023). Predicting the
 impact of invasive trees from different measures of abundance.
 <em>Journal of Environmental Management</em>,
 <em>325</em>, 116480. https://doi.org/10.1016/j.jenvman.2022.116480 Naour, M., García, R., & Pauchard, A. (2016). <em>Rendimientos y factibilidad técnica de tres métodos diferentes de control de la invasión de Pinus contorta al interior de la Reserva Nacional Malalcahuello.</em> Laboratorio de Invasiones Biológicas.
description: # Remove saplings early: Cost effective strategies to contain tree
 invasions and prevent their impacts
 [https://doi.org/10.5061/dryad.wstqjq2x3](https://doi.org/10.5061/dryad.wstqjq2x3) ## Description of the data and file structure We simulated a pine population spreading from a commercial plantation for 50 years, coupled with annual control actions which started either as soon as pines began to spread (when pines in the plantation become reproductive; proactive management) or assuming that control actions were delayed at incremental decadal intervals (reactive management). For control interventions, we targeted different life stages, prioritized areas at different locations of the landscape and explored a wide range of available budgets limiting the percentage of the landscape that could be managed every year. We aimed to answer the following set of comprehensive questions of management relevance: 1-a) Which is the minimum budget that achieves containment (i.e. only the area covered by the original commercial plantation is occupied by reproductive pines)? b) Which strategy (combination of stage targeted and spatial prioritization) is effective with this minimum budget? 2-a) If we incrementally delay management, how much does the minimum budget necessary for containment increase? b) Do the strategies that achieve containment with the lowest budget change if management is delayed? 3) Do the strategies that achieve containment also minimize the invasive population size and accumulated impact? 4) Which strategies achieve the most cost-effective reduction in invasion extent, invasive population size and invasion impact? 5) Which management variables best explain variation in management effectiveness (i.e. reduction in invasion extent, invasive population size and invasion impact)? ### Files and variables #### File: management\_outputs.csv **Description:**  ##### Variables * Rep: Simulation replicate * Method: Spatial prioritization method (-1: no management; 0: random; 7: reactive to incurred damage; 8: weighted by current population size; 10: biased towards recently colonized patches) * CullStage: Pine stage to remove * StartYear: Management delay (years) * Damage_total: Pine invasion accumulated impact on grassland productivity (unitless score) * NOccupCells: Number of cells occupied by reproductive pines * NInds: Number of pines across the landscape * Budget: Available budget #### File: management\_outputs\_cv5\_7.5\_10.csv **Description:**  ##### Variables * Rep: Simulation replicate * Method: Spatial prioritization method (-1: no management; 0: random; 7: reactive to incurred damage; 8: weighted by current population size; 10: biased towards recently colonized patches) * CullStage: Pine stage to remove * StartYear: Management delay (years) * Damage_total: Pine invasion accumulated impact on grassland productivity (unitless score) * NOccupCells: Number of cells occupied by reproductive pines * NInds: Number of pines across the landscape * Budget: Available budget - cv: Coefficient of variation in detected pine population size ## Code/software Our data can be viewed with any software that reads spreadsheets.