DOI: 10.5061/dryad.x95x69pvf
title: Simulated impacts of harvesting Chamaedorea linearis and C.pinnatifrons (Arecaceae): Implications for conservation

description: Palms are important sources of non-timber forest products. One of their
 most common uses is as ornamentals, which often involves harvesting whole
 individuals or plant parts from wild populations. Chamaedorea linearis and
 C. pinnatifrons are still not excessively harvested, but they have
 substantial ornamental potential, and their populations are decreasing.
 The use of a wild species can contribute to its conservation if the use is
 governed by adequate harvest rates. We simulated harvest impacts on the
 population dynamics of C. linearis and C. pinnatifrons in an Andean
 montane forest in Parque Natural Chicaque, Colombia, using integral
 projection models (IPMs) over the period 2019–2021. We projected
 management scenarios including the harvest of juveniles, as well as leaves
 and fruits of adults. In our model, the finite growth rate of C. linearis
 decreased (=0.76–0.91) while in C. pinnatifrons it remained stable (
 =0.98–1.04). The simulations of the harvest of juveniles and leaves
 suggested negative impacts on the populations’ sizes and in the transient
 growth rate ( 20) in the long term when compared to no-harvest. Fruits
 harvests had no considerable effects under 20% of removal once per year in
 both of the populations, but had a decrease in populations under 20% of
 removal twice and four times a year. We conclude that sustainable use
 strategies should focus on protecting juveniles in both populations. Fruit
 harvest for propagation can be promoted as a sustainable use strategy that
 does not endanger their conservation.
description: <em>Study sites and species
 description</em> <em>Chamaedorea
 linearis</em> and <em>C. pinnatifrons</em> are 3-10
 meters-tall, solitary, dioecious palm species. Both predominantly inhabit
 the Andean region of Colombia; although their range extends from Mexico to
 Bolivia in tropical forests (Galeano & Bernal, 2010). Our study
 focused on these species within the Parque Natural Chicaque, Colombia
 (4°37'8.06'' N, 74°18'48.38'' W). The park
 spans an area of 300 ha and is characterized by cloud forests situated at
 elevations from 2,000 to 2,700 m a.s.l. The temperatures in the area range
 from 12 to 17°C, and the annual rainfall exhibits a bimodal pattern, with
 an average of 2000 mm primarily occurring during the months of April-May
 and October-November (Rivera & Córdoba, 1998). The forest within
 the park is a mix of secondary growth and features multiple layers,
 including herbaceous, shrub, and arboreal strata (Rivera &
 Córdoba, 1998). <em>Data
 collection</em> In 2019, a total of 410
 individuals of <em>C. linearis </em>and 615 individuals of
 <em>C. pinnatifrons</em> were marked and monitored,
 encompassing acaulescent seedlings, juveniles, as well as stemmed
 subadults and adults. To register their mortality within the first year,
 an additional 80 stemmed individuals of <em>C.
 pinnatifrons</em> were marked in 2020. All palm individuals were
 checked during the 2020 and 2021 censuses. For
 acaulescent individuals, the number of pinnae on the right side of the
 youngest fully expanded leaf was used as the stage variable. Stem height,
 measured from the ground level to the base of the oldest leaf sheath,
 served as the stage variable for stemmed individuals (Bernal &
 Galeano, 2013). In each census, we recorded the number of racemes (for
 adults), leaves produced, and new seedlings. Survival
 rates were determined by calculating the proportion of living individuals
 in each census. Growth rates were estimated by measuring the annual
 increase in size for both acaulescent and stemmed individuals. For
 acaulescent individuals, the pinnae number in the youngest new leaf in
 each census was the growth indicator. For stemmed individuals the growth
 rate was the annual stem growth, which was calculated by multiplying the
 number of leaves produced by the internode length. To obtain the internode
 length, the stem was divided into ranges of 0.3 m for both species,
 covering the observed variations in internode length along the stem. The
 number of scars within each range was counted for each stemmed individual
 and the internode distance was averaged across individuals for each range,
 following Bernal & Galeano (2013). Fecundity was estimated via the
 reproductive status of stemmed individuals and the number of racemes in
 each census. The recruitment rates were considered as the number of new
 seedlings in each census. <em>Population
 dynamics</em> To examine the relationship between
 vital rates such as survival, growth, fecundity and the state variables,
 we employed descriptive statistics, parameterized linear regression
 models, and generalized linear models that suited the data (Merow et al.,
 2014). Survival was quantified as the probability of individuals surviving
 in each census. Growth encompassed both the increase in size and the
 likelihood of stem formation. Fecundity was estimated by considering the
 number of racemes in relation to the height and size of individuals
 initiating reproduction. Descriptive statistics were also utilized to
 determine the average size of recruited individuals and the average number
 of fruits per raceme. In the selection of parameters for constructing
 population dynamics models, we employed criteria such as p-values (P
 &lt;0.05), the Akaike information criterion (AIC) (lowest AIC value
 among the evaluated models), and the determination coefficient (R2) to
 select the best-fit model (Akaike, 1981; Zar, 2010). These criteria helped
 to inform the selection of relevant parameters (Table S1, Table S2, and
 Table S3). All statistical analyses were performed using the R software (R
 Core Team, 2020). Integral Projection Models (IPM) were
 used to describe the dynamics of size and structure of the population over
 a discrete period of time (Easterling et al., 2000), given by, n(y,t+1)= ∫
 <sub>x</sub> K(y,x) n(x,t) dx  , where the kernel (k (y,x )) 
 represented all possible size transitions in the population from size x at
 time, t, to size y at t+1. The kernel is based on three main functions
 representing the vital rates of survival, s(x) , growth, g (x,y) , and
 fecundity, f (x,y), given by k (y,x)=s (x)+g (y,x)+f (y,x). 
 These functions capture the probabilities of survival, the
 size-dependent growth rates, and the reproductive output of individuals
 within the population. By integrating the kernel function over the
 interval X, which encompasses all possible sizes, we model the changes in
 population size and structure over time using the IPM framework
 (Easterling et al., 2000). The model we developed focused on the
 acaulescent and stemmed stages of both species, incorporating continuous
 and discrete variables that described their size states. A two-staged
 model was built because stem length, the usual state variable descriptor,
 is not present for many years after germination; instead, individuals form
 an underground stem that thickens as they grow (Henderson 2002). As such,
 for the acaulescent stage we used pinnae number as the state descriptor
 (Isaza et al., 2017). Notably, the vital rates of males and females in
 both species were found to be similar (for details see Cepeda et al.,
 2022), therefore, we did not incorporate sex differences into the model.
 The expressions derived from our previous analysis, including linear
 regression models, generalized linear models, and descriptive statistics
 were utilized to construct the kernel and calculate the transition
 probabilities. These probabilities were crucial in determining the
 asymptotic growth rate, λ, along with the corresponding 95% confidence
 intervals, as obtained from 1,000 replicates. To discretize the kernel we
 applied the midpoint rule (Easterling et al., 2000). We used 200 mesh
 points for both the acaulescent and the stemmed stages for a total of 400
 mesh points, ensuring adequate resolution for the modeling
 process. To assess the contribution of different growth
 categories to the overall population growth rate (λ) we calculated
 elasticity values (Isaza et al., 2017). Specifically, we employed the
 function e(y,x) to estimate size-specific elasticity (Easterling et al.,
 2000). This analysis allowed us to understand the relative importance of
 different sizes in λ, with the integration of kernel intervals assigned to
 specific individual sizes given by, 
 ∫<sub>A</sub><sup>B</sup> e (y
 ,x)dydx=1. Furthermore, we determined elasticity values
 for vital rates to gain insights into which parameters made the greatest
 contributions to λ. This calculation was performed using the formula
 e<em><sub>β</sub></em> = (log
 λ<sub>1.01</sub> - log λ<sub>0.99</sub>) / (log
 ⁡(1.01β) -  log (0.99β))  where β represents the perturbation magnitude
 (Isaza et al., 2017). By introducing small perturbations (1% magnitude) to
 the value of each parameter and assessing their effects on λ, we could
 estimate the magnitude of elasticity values associated with each vital
 rate parameter (Isaza et al., 2017). This approach provided information on
 the sensitivity and importance of each parameter in influencing λ with the
 rest of the parameters. <em>Harvest
 estimation</em> There was limited data to
 estimate the harvest parameters for our species. Therefore, we relied on
 existing data on the frequency and intensity of harvests for other
 Chamaedorea species (Endress et al., 2006; Valverde et al., 2006; Zuidema
 et al., 2009). We examined data related to the harvest of juveniles and of
 leaves and mature fruits on adult plants. Our study included three
 different treatment groups for both juvenile and fruit harvests: control
 (no harvest), 1x (20% of resources harvested once a year), 2x (20% of
 resources harvested twice a year), and 4x (20% of resources harvested four
 times a year) (Endress et al., 2006). In the case of leaf harvests in
 Chamaedorea, the leaves of subadults measuring over 40 cm in length were
 targeted. The harvest intensities ranged from 20% to 100% with annual
 frequencies, as reported in previous studies (Endress et al., 2006;
 Zuidema et al., 2007). We conducted simulations to assess the impact of an
 annual leaf harvest equivalent to 50% of the leaves, using average
 reduction values for survival, growth, and fecundity derived from prior
 studies on adults of C. radicalis and C. elegans (Endress et al., 2006;
 Martínez-Ramos et al., 2009). The response to defoliation experiments
 indicated a 14.3% (±6.7) reduction in survival, a 21.8% (±1.06) reduction
 in growth, and a 67.1% (±23.98) reduction in fecundity (Tables S4, S5,
 S6). Since our data covered two consecutive years, we employed a
 stochastic approach to simulate harvests. For each simulated year the
 model randomly selected one of the scenarios. Juvenile harvests were
 performed as destructive harvests. However, leaf and fruit harvests were
 considered non-destructive. For juveniles and fruit harvests, we modified
 the kernel parameters to reflect the frequencies proposed for juveniles
 and reproductive individuals respectively. To account for leaf harvests,
 we adjusted the kernel parameters based on the reported percentages and
 standard deviations of reduction in survival, growth, and fecundity for
 stemmed individuals (Endress et al., 2006). For fruit harvests, we
 modified the kernel parameters to reflect the frequencies proposed for
 reproductive individuals. To project the harvest
 simulations we extended the timeline for 20 years beyond the measurement
 period. This duration is more relevant for developing management plans
 compared to longer-term estimations (García et al., 2016). To assess the
 sustainability of different harvest scenarios, we estimated the geometric
 mean standard of the transient population growth rate (λt20) for the last
 five years of each simulation and examined the number of harvestable
 individuals.
description: # Simulated impacts of harvesting Chamaedorea linearis and C.pinnatifrons
 (Arecaceae): Implications for conservation
 [https://doi.org/10.5061/dryad.x95x69pvf](https://doi.org/10.5061/dryad.x95x69pvf) ## Description of the data and file structure The data was collected to determine the vital rates of survival, growth, and fecundity, population dynamics, and harvest simulations of leaves, fruits, and juveniles of *Chamaedorea linearis* and *C. pinnatifrons. * Vital rates: Survival rates were determined by calculating the proportion of living individuals in each census. Growth rates were estimated by measuring the annual increase in size for both acaulescent and stemmed individuals. For acaulescent individuals the pinnae number in the youngest new leaf in each census was the growth indicator. For stemmed individuals the growth rate was the annual stem growth, which was calculated by multiplying the number of leaves produced by the internode length. To obtain the internode length, the stem was divided into ranges of 0.3 m for both species, covering the observed variations in internode length along the stem. The number of scars within each range was counted for each stemmed individual and the internode distance was averaged across individuals for each range. Fecundity was estimated via the reproductive status of stemmed individuals and the number of racemes recorded in each census. The recruitment rates were considered as the number of new seedlings in each census. Population dynamics: Integral Projection Models (IPM) were used to describe the dynamics of size and structure of the population over a discrete period of time. The vital rates of males and females in both species were found to be similar, therefore, we did not incorporate sex differences into the model. The expressions derived from our previous analysis, including linear regression models, generalized linear models, and descriptive statistics were utilized to construct the kernel and calculate the transition probabilities. The asymptotic growth rate, λ, was estimated along with the corresponding 95% confidence intervals, as obtained from 1000 replicates. To discretize the kernel we applied the midpoint rule. We used 200 mesh points for both the acaulescent and the stemmed stages for a total of 400 mesh points. We employed the function e(y,x) to estimate size-specific elasticity. We determined elasticity values for vital rates. This calculation was performed using the formula where β represents the perturbation magnitude. By introducing small perturbations (1% magnitude) to the value of each parameter and assessing their effects on λ we were able to estimate the magnitude of elasticity values associated with each vital rate parameter. Harvest simulations: Our study included three different treatment groups for both juvenile and fruit harvests: control (no harvest), 1x (20% of resources harvested once a year), 2x (20% of resources harvested twice a year), and 4x (20% of resources harvested four times a year). In the case of leaf harvests in *Chamaedorea*, the leaves of subadults measuring over 40 cm in length were targeted. The harvest intensities ranged from 20% to 100% with annual frequencies, as reported in previous studies (Endress et al., 2006; Zuidema et al., 2007). The response to defoliation experiments indicated a 14.3% (±6.7) reduction in survival, a 21.8% (±1.06) reduction in growth, and a 67.1% (±23.98) reduction in fecundity. We employed a stochastic approach to simulate harvests. For each simulated year the model randomly selected one of the constructed scenarios. Juvenile harvests were performed as destructive harvests. However, leaf and fruit harvests were considered non-destructive. For juveniles and fruit harvests, we modified the kernel parameters to reflect the frequencies proposed for juveniles and reproductive individuals respectively. To account for leaf harvests, we adjusted the kernel parameters based on the reported percentages and standard deviations of reduction in survival, growth, and fecundity for stemmed individuals. For fruit harvests, we modified the kernel parameters to reflect the frequencies proposed for reproductive individuals. To project the harvest simulations we extended the timeline for 20 years beyond the measurement period. To assess the sustainability of different harvest scenarios, we estimated the geometric mean standard of the transient population growth rate (λt20) for the last five years of each simulation and examined the number of harvestable individuals. ### Files and variables #### File: C\_linearisc2.csv **Description:** The file contains data for each individual of *C. linearis* during first and second year of census regarding their growth category, survival, growth (leaves and pinnae), and reproductive status.  ##### Variables * plot: Plot were the individual was found * indiv: Number of individual * categ1: Growth category of the individuals on the first census. (se: seedling, ju: juvenile, su: subadult, ad: adult) * categ2: Growth category of the individuals on the last census. (se: seedling, ju: juvenile, su: subadult, ad: adult) * sp: Species (cl: Chamaedorea linearis) * surv12: Survival year 1 (1: living individual, 0: dead individual) * surv23: Survival year 2 (1: living individual, 0: dead individual) * tallo: Presence or absence of stem (1: Individual with stem, 0: Individual without stem) * rep: Presence or absence of reproductive structures (1: Individual with reproductive structures, 0: Individual without reproductive structures) * a1: Stem height during first census. * anillos: Number of rings. * hojas3: Number of leaves second year. * hojas2: Number of leaves first year. * hojas1.5: Number of leaves recorded on first census.  * hojasp12: Number of leaves produced during first year. * hojasp23: Number of leaves produced during second year.  * pinnas prod t2: Total number of pinnae of youngest leaf produced. * p23: Number of pinnae of youngest leaf produced during second year.  * p12: Number of pinnae of youngest leaf produced during first year.  * p1: Number of pinnae recorded the initial census.  * sex: Sex of the individual * f1: Number of fruits during first census.  * r1: Number of racemes during first census. * observ: Observations * NA cells indicate the non-availability of that information while doing the measurements.  #### File: C\_pinnatifronsc2.csv **Description:** The file contains data for each individual of *C. pinnatifrons *during first and second year of census regarding their growth category, survival, growth (leaves and pinnae), and reproductive status.  ##### Variables * plot: Plot were the individual was found * indiv: Number of individual * categ1: Growth category of the individuals on the first census. (se: seedling, ju: juvenile, su: subadult, ad: adult) * categ2: Growth category of the individuals on the last census. (se: seedling, ju: juvenile, su: subadult, ad: adult) * sp: Species (cp: Chamaedorea pinnatifrons) * surv12: Survival year 1 (1: living individual, 0: dead individual) * surv23: Survival year 2 (1: living individual, 0: dead individual) * tallo: Presence or absence of stem (1: Individual with stem, 0: Individual without stem) * rep: Presence or absence of reproductive structures  (1: Individual with reproductive structures, 0: Individual without reproductive structures) * a1: Stem height during first census. * anillos: Number of rings. * hojas3: Number of leaves second year. * hojas2: Number of leaves first year. * hojas1: Number of leaves recorded on first census.  * hojasp12: Number of leaves produced during first year. * hojasp23: Number of leaves produced during second year.  * pinnas prod: Total number of pinnae of youngest leaf produced. * p23: Number of pinnae of youngest leaf produced during second year.  * p12: Number of pinnae of youngest leaf produced during first year.  * p1: Number of pinnae recorded the initial census.  * sex: Sex of the individual * f1: Number of fruits during first census.  * r1: Number of racemes during first census. * observ: Observations * NA cells indicate the non-availability of that information while doing the measurements.  #### File: Cl\_rings.csv **Description:** The file contains the information of average internode distance each 30 cm for *C. linearis*.  ##### Variables * alt: Height range of stem (m).  * dist: Internode distance (m). #### File: Cp\_rings.csv **Description:** The file contains the information of average internode distance each 30 cm for *C. pinnatifrons*.  ##### Variables * alt: Height range of stem (m). * dist: Internode distance (m).  #### File: harvestcl\_stoch.R **Description:** The file contains the code for the harvest simulation of juveniles, leaves, and fruits of *C. linearis*.  #### File: harvestcp\_stoch.R **Description:** The file contains the code for the harvest simulation of juveniles, leaves, and fruits of *C. pinnatifrons*.  #### File: Kernel\_IPM\_C\_linearis\_year\_2.R **Description:** The file contains the code to perform the Integral Projection Model of *C. linearis* for year 2.  #### File: Kernel\_IPM\_C\_linearis\_year\_1.R **Description:** The file contains the code to perform the Integral Projection Model of *C. linearis* for year 1.  #### File: vital\_rates\_cl.R **Description:** The file contains the code to determine the vital rates of survival, growth, and reproduction for *C. linearis*.  #### File: vital\_rates\_cp.R **Description:** The file contains the code to determine the vital rates of survival, growth, and reproduction for *C. pinnatifrons*.  #### File: Kernel\_IPM\_C\_pinnatifrons\_year\_1.R **Description:** The file contains the code to perform the Integral Projection Model of *C. pinnatifrons *for year 1.  #### File: Kernel\_IPM\_C\_pinnatifrons\_year\_2.R **Description:** The file contains the code to perform the Integral Projection Model of *C. pinnatifrons *for year 2.  ## Code/software Our data can be accessed through Windows Excel and analyzed through R software (v4.2) to calculate vital rates, analyze population dynamics, and simulate harvest scenarios. First, we applied the vital rates code on the files with information regarding each individual during first and second years to determine survival, growth, and fecundity rates, which informed the population dynamics analysis. Next, we performed the Integral Projection Models code to estimate the population growth rate and conduct elasticity analyses based on the vital rates previously obtained. Finally, we used the harvest simulations code with modified growth, survival, and fecundity parameters to model the extraction of juveniles, leaves, and fruits.