Simulated impacts of harvesting Chamaedorea linearis and C.pinnatifrons (Arecaceae): Implications for conservation
Data files
Dec 13, 2024 version files 157.09 KB
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C_linearisc2.csv
33.24 KB
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C_pinnatifronsc2.csv
50.59 KB
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Cl_rings.csv
225 B
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Cp_rings.csv
163 B
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harvestcl_stoch.R
7.33 KB
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harvestcp_stoch.R
7.34 KB
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Kernel_IPM_C_linearis_year_1.R
8.08 KB
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Kernel_IPM_C_linearis_year_2.R
8.04 KB
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Kernel_IPM_C_pinnatifrons_year_1.R
8.96 KB
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Kernel_IPM_C_pinnatifrons_year_2.R
9.13 KB
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README.md
10.80 KB
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vital_rates_cl.R
6.87 KB
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vital_rates_cp.R
6.32 KB
Abstract
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.
README: Simulated impacts of harvesting Chamaedorea linearis and C.pinnatifrons (Arecaceae): Implications for conservation
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.
Methods
Study sites and species description
Chamaedorea linearis and C. pinnatifrons 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).
Data collection
In 2019, a total of 410 individuals of C. linearis and 615 individuals of C. pinnatifrons 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 C. pinnatifrons 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.
Population dynamics
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 <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)= ∫ x 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, ∫AB 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 eβ = (log λ1.01 - log λ0.99) / (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.
Harvest estimation
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.