Pontificia Universidad Javeriana
Facultad de Estudios Ambientales y Rurales
Aplicación del modelamiento de agentes para entender algunos de los efectos de la deforestación de ecosistemas naturales
sobre los anfibios del Caquetá desde el aula de clase Autor
Martín Otálora Löw
Año: 2021
Director: Mauricio González Méndez
Co-director: Nicolás Urbina Cardona
Título: Aplicación del modelamiento de agentes para entender algunos de los efectos de la deforestación de ecosistemas naturales sobre los anfibios del Caquetá desde el aula de clase
Introducción:
El problema al que se enfrenta este trabajo de grado es la dificultad de los estudiantes para aproximarse a complejos pero importantes conceptos dentro de la ecología de la conservación, específicamente el efecto de borde, y facilitar la explicación del tema en el aula de clase por medio del diseño de un modelo de agentes. Es posible ver una descripción más detallada del problema y su justificación es los Anexos 1. La forma en la que este trabajo fue desarrollado es el formato artículo, por lo que a continuación se verán los objetivos de este trabajo en español e inmediatamente se tornará en un formato articulo utilizando la guía de la revista Ecography, ya que se busca la publicación de este trabajo y motivo por el cual estará en inglés. Dicho formato consta de una introducción al problema, los métodos utilizados a través del trabajo, los resultados obtenidos, la discusión de dichos resultados, conclusiones y recomendaciones. Sin embargo, es posible ahondar en los temas tratados a través del articulo dirigiéndose al Anexo 1, en donde se profundizan los conceptos clave, y el Anexo 2, donde se hace una descripción del área de estudio, y su respectivo mapa, así como una descripción más detallada de los métodos utilizados. Finalmente, se hizo un protocolo Overview, Design concepts, and Details (ODD) para la correcta descripción del modelo y este también puede ser visto como Supplementary Material. Se utilizó este protocolo pues es el único protocolo para modelos de agentes e individuos; ha tenido dos revisiones. A partir de los datos de campo de un trabajo de grado de la Maestría en Conservación y Uso de Biodiversidad se seleccionaron especies de anuros de diferentes ambientes (potrero, borde de bosque e interior de bosque) en el Departamento del Caquetá. Para cada especie seleccionada se obtuvo datos morfológicos y del hábitat para determinar las reglas de actuación y uso de energía dentro del modelo. El modelo muestra cómo cambia la dispersión y reproducción de los diferentes tipos de especies ante escenarios de deforestación, regeneración natural de la selva por abandono del potrero y restauración ecológica, con el fin de mostrar la dinámica espaciotemporal de los anuros que habitan paisajes transformados por el ser humano. Las diferentes versiones del modelo se pusieron a prueba en reuniones con estudiantes de pregrado y posgrado de la Facultad de Estudios Ambientales y Rurales para generar una versión final que fuera amigable con el usuario. La presente herramienta se usará de manera interactiva con los estudiantes del Pregrado en Ecología (asignatura Ecología de la Conservación), de la Maestría en Conservación y Uso de Biodiversidad (asignatura Biología de la Conservación) y del Doctorado en Estudios Ambientales y Rurales (asignatura Transformaciones del Sistema Global) de la Pontificia Universidad Javeriana.
2. Objetivos 2.1 General
Diseñar un modelo basado en agentes que represente la respuesta de diferentes grupos de especies de anfibios a los efectos de borde bajo
escenarios de deforestación, restauración y regeneración natural en la selva amazónica del Departamento de Caquetá, Colombia.
2.2 Específicos
2.2.1 Identificar grupos de especies de anfibios acorde con sus abundancias en potrero, borde de bosque e interior de bosque, y obtener los valores de las variables ambientales de su hábitat.
2.2. Proponer reglas de actuación y uso de energía de acuerdo con los rasgos funcionales de los grupos de anfibios, para representar su movimiento bajo las restricciones ambientales impuestas a lo largo del gradiente de hábitat de potrero, borde e interior de bosque.
2.2.3 Generar una interfaz que permita la visualización de la forma como cambia la dispersión y reproducción de los grupos de especies de anfibios bajo escenarios de deforestación, restauración y regeneración natural.
ARTÍCULO CIENTÍFICO:
Usage of Agent Modelling to understand some of the effects of deforestation upon amphibians of Caquetá, from the
classroom.
Martín Otálora-Low1*, José Nicolás Urbina-Cardona2, Mauricio González- Méndez2
1Ecology undergraduate student from Pontificia Universidad Javeriana
2Deparment of Ecology and Territory, Faculty of Environmental and Rural Studies, Pontificia Universidad Javeriana
* corresponding author MO-L: m_otalora@javeriana.edu.co
Summary:
Edge effect is an environmental condition generated when anthropogenic matrix interacts with natural forest, creating new habitat ecotones that may impact species survival and biotic interactions. The edge effect has a major impact on the reproduction and dispersal of species, which is reflected in their abundance at a given time, so it is crucial to implement biodiversity conservation actions.
However, the knowledge that’s generated from previous studies is not easily understandable to students due to the complex relationship between a large number of biotic and abiotic variables that are highly dynamic in space and time.
It has therefore always been a challenge to teach edge effects in a didactic way in academic programs from a classroom. This paper aims to generate a simplified explanation of the edge effect on rainforest anurans of Caquetá, Colombia, using agent-based modeling. Field data previously collected as part of a Master's degree project were used to classify and group anuran species based on their response to the forest edge, functional traits, and habitat environmental variables, resulting on 3 groups. Agents can explore their environment checking if it is optimal for them (within suitable ranges), and then moving according to their body weight and leg length. The energy required by each group type to explore its
environment and disperse in the different habitats was defined depending on some morphological and reproductive traits. Different scenarios (Deforestation, restoration, and abandonment) have an impact on the dispersal and reproduction of the species. The preliminary version of the model was presented in two courses (undergraduate and master's) at the end of the edge effects module, to receive feedback from the students and to describe their experience of interacting with the model. Students were able to choose different rates of deforestation and restoration, visualizing the local extinction of some group of frogs and the population growth of others. Most of the students reported that they found the tool very useful to reinforce the concepts seen in class, so it will be implemented as a model for the academic programs of the Faculty of Environmental and Rural Studies, and will be available online for free access.
Introduction:
Fragmentation and habitat loss are some of the leading causes of decreasing number of species worldwide (Arroyo-Rodríguez et al., 2020a; Tscharntke et al., 2012). Various conceptual models have been proposed, in which it is detailed how to approach the main causes and consequences of landscape fragmentation (Didham et al., 2012; Fischer & Lindenmayer, 2007; Ries et al., 2004). Fischer and Lindenmayer (2007) propose a framework in which they integrate a species- oriented vision, and a pattern-oriented vision, which includes connectivity, matrix, and edge effects, enabling a more complete theorical revision of fragmentations (Fischer & Lindenmayer, 2007).
The edge effect is a consequence of landscape fragmentation, it is characterized for generating a very abrupt ecotone between an anthropogenic matrix and a forest patch. The edge effect varies depending on the forest edge age and contrast as well as patch´s form and size, altering biotic and abiotic characteristics of the habitat of species (Harper et al., 2005). Edge effects have very strong effects on native forest species, which could be positive or negative (Pfeifer et al., 2017). For amphibians, the edge effect has, mostly, a negative impact as it tends to reduce their abundance (Schneider-Maunoury et al., 2016).
As native forest trees are cleared to make way for human activities such as agriculture, the species that are exposed to the new forest edge suddenly face novel environmental conditions to which they may not be adapted (Ries et al., 2004). These environmental gradients can be abrupt on amphibians when the edges of the remnant forest limit with grasslands or maize crops (Santos-Barrera
& Urbina-Cardona, 2011) , or can be buffered when they limit with vegetation cover with more complex vegetation structure (e.g. coffee plantation with shade;
(Santos-Barrera & Urbina-Cardona, 2011; J. N. Urbina-Cardona et al., 2006). In this sense, forest amphibian populations are expected to increase in population size as natural regeneration occurs due to abandonment (Hernández-Ordóñez et al., 2015), or ecological restoration actions are implemented (Díaz-García et al., 2017).
One of the biggest drivers of the environmental crisis in Colombia is deforestation, which has caused the transformation of more than 747,000 ha of native forest between 2017 and 2020 (IDEAM, 2021). Nationally, deforestation derives mostly from illegal land grabbing for livestock farming, road infrastructure, illegal logging,
and coca plantations (IDEAM, 2021). Specifically, 63.7% (109.302 ha) of all national deforestation in 2020 was centered in the Amazon region of Colombia, which has 66% of the whole natural forests of the country (IDEAM, 2021). Various studies have been focused on understanding and predicting the way deforestation moves through the amazon, all resulting in the very concerning conclusion that the rate at which deforestation and colonization are moving is unsustainable (Armenteras et al., 2019; Correa Ayram et al., 2020; Etter, McAlpine, et al., 2006; Murad & Pearse, 2018).
Caquetá Department has seen a very aggressive ramp-up in deforestation, due to a very disorganized colonization process (Etter, et al., 2006). Primarily, this colonization has introduced cattle, turning a substantial amount of area into pastures so that the cattle can be productive (Etter et al., 2008). Illegal coca production has also been found to have a constant, but minuscule (Murillo- Sandoval et al., 2020) , effect on deforestation, alongside armed conflict, affecting mainly Meta and Caquetá departments (Bautista-Cespedes et al., 2021; Negret et al., 2019). This process has caused 62.839 ha of deforestation in Caquetá between 2019 and 2020, ranking it as the second most deforested departments (IDEAM, 2021). Said colonization, drives an accelerated rate of forest loss, causing population declines as some species of amphibians are unable to escape or adapt to the sudden degradation of their environment (Palomino-Cuellar, 2019).
Cattle and agriculture are the most harmful activities towards amphibians, as they generate vast amounts of habitat fragmentation and forest edges (Nori et al.,
2015; Schneider-Maunoury et al., 2016), only those species that breed in water bodies and have large body size can survive the novel extreme environment, as bigger bodies, with thicker skins, allow less dehydration increasing survivability (Dale et al., 1994; Pfeifer et al., 2017); and those who survive will have a reduction in their area of distribution due to the interaction between climate change and land use changes (Agudelo-Hz et al., 2019; Londoño et al., 2019). Population decline in amphibians has been proven to be one of the steepest when compared to other vertebrates, according to the International Union for Conservation of Nature, 33.8% of all amphibian species are in a threatened category and 37 species are already extinct (IUCN, 2021). Currently 78.6% of amphibian species are threatened by habitat loss due to anthropogenic transformation of the landscape to make way for agriculture (IUCN, 2021). Furthermore, it has been proven that the composition of amphibians is vital as they impact the flow of matter and energy between terrestrial and aquatic habitats, being both prey and predators their presence indicates a healthy environment, as amphibians tend to be very sensible to environmental changes (Connelly et al., 2008; Whiles et al., 2006, 2013).
The effects of landscape transformation processes on biodiversity are complex and difficult to explain to the public (Vieira Pak & Castillo Brieva, 2010). Likewise, scientific research results are reported in specialized journals that are not consulted or interpreted in the best way by the non-specialized public (i.e students, farmers, politicians) (Ferraz et al., 2021; Redford et al., 2012; N. Urbina- Cardona et al., 2019). Agent-Base Modelling is tool created to represent environmental interactions that would be very hard to see in reality, and thus it is
thought to be a great alternative to be used to explain complex topics in class (Carey & Gougis, 2017; Farrell et al., 2018; Read et al., 2016) , to explain biodiversity reactions to agricultural processes (Brewer, 2006; Chopin et al., 2019)and it has been used recently as way to approach to concepts that would usually need field work, as online learning has grown in the last year (Murphy et al., 2020).
Thus, this research aims to create an Agent-Base Model that’s able to represent some facets of the edge effect on species` dispersal and abundance caused by changes in land use and cover in a way that is interactive and easy to understand for students. To do this, the model is circumscribed by the general objective:
Design a simple agent-based model that represents the reaction of different amphibian species to the edge effect in Caquetá, Colombia. The specific goals are: 1) Stablish representative species of amphibians from pasture, forest edge and forest interior, with known environmental characteristics; 2) Identify and set actuation and energy usage rules according to the functional traits from the selected species to represent the behavior accurately and 3) Generate a User Interface that’s easy to understand, allowing for an easy visualization of changes in reproduction and movement from the amphibians.
Methods:
The model was made in the programing language and integrate development environment NetLogo (Tisue et al., 2004). The model was described in depth using the Overview, Design Concepts and Detail (ODD) protocol proposed by
Grimm (2010) as it has been used to organize and explain models in such way that they are understandable and replicable. The full ODD can be found in the Supplementary material 1 for formatting and readability.
The transition parameters in land use type of pasture, rainforest edge or interior, are configured by the user from a panel with icons that can be slid to configure the scenario. As the simulation runs, the results can be observed on a board that presents different types of habitats (represented by different colored pixels) and on which the different types of anurans are scattered and reproduced.
Classification of anuran species according to type of response to the forest edge:
The agents are based on the field abundances of the amphibian species found by Palomino-Cuellar (2019) from which their habitat was classified as follows 1) Dendropsophus mathiassoni affine for the pasture habitat; 2) Amazophrynella minuta, and Pristimantis variabilis affine for the forest edge habitat; and 3) Engystomops petersi, Pristimantis acuminatus, and Pristimantis conspicillatus affine for the forest interior habitat. The border and forest interior habitat were represented by two and three species, respectively. This is because each species has a small number of individuals, so we group them to maximize the information on functional traits per habitat.
Agents, Patches and Variables:
For the initial model, the board was divided into three habitats: Pastures, rainforest edge, and rainforest interior. Each of these habitats has initial values for the average of the environmental temperature, relative humidity and canopy
cover, variables taken from Palomino-Cuellar (2019) (Table 1). These three habitats change according to a rate set with a slider by the user, allowing to see faster or slower processes, and for the user to determine practices of ecological restoration, deforestation, or natural regeneration after land abandonment. Every time a patch changes it acquires a random new number for environmental temperature, canopy cover, and relative humidity within the range of values observed in the field for each variable in each habitat (Palomino-Cuellar, 2019)
Table 1: Table of the main variables used in the model; temperature, relative humidity, and canopy cover. This data was adapted from field measurements made by Palomino-Cuellar (2019).
Each amphibian species was assigned a categorical value of run capacity, and reproduction, and a limited supply of energy to accomplish survival. This means that the agents in the model can die from lack of energy (exhaustion) and so, depending on traits like parental care, seasonality in reproduction, number of eggs, type of nest, between other factors (Vitt & Caldwell, 2013), a species´ group could consume more or less energy when reproducing, so, in Table 2, 1 would represent low energy consumption, and 5 high energy consumption at reproduction. All parameters refer to Palomino-Cuellar (2019) master’s thesis, allowing us to represent how would each species responds to the edge effect (Table 2).
MIN MAX Average Min Max Average Min Max Average
22.7 28.5 26.5433333 84.5 94.8 90.36 0 7.54 0.90133333
MIN MAX Average Min Max Average Min Max Average
26.7 33.2 30.3333333 67.2 88.2 75.7 81.9 88.14 85.9733333
MIN MAX Average Min Max Average Min Max Average
24.4 28.2 26.26 91.2 100 94 82.42 88.66 85.228
Canopy Relative humidity
Temperature
Dendropsophus mathiassoni
Temperature Relative humidity Canopy
Pristimantis variabilis
Engystomops petersi,Pristimantis conspicillatus, Pristimantis acuminatus
Temperature Relative humidity Canopy
Table 2: Morphological and physiological values per anuran species inhabiting the rainforest in the Caquetá Department. A value from 1 to 5 was assigned to reproduction considering number of eggs and seasonality of reproduction, this number is used in the model as the rate to which a new amphibian is hatched. Leg length to body weight ratio was calculated based on field data (Palomino-Cuellar (2019) done with known measures of the species reflecting the ability of an agent to explore his environment.
The Leg-Weight Ratio was obtained using the functional traits from the frogs following a standard protocol (Cortés-Gómez et al., 2015) to obtain a value reflecting the individuals' ability to move based on the length of their legs and their weight ( !"#$%&&∗())
*+, &".+ ). This ratio indicates how capable is a frog to move their body, meaning that a lower number shows that the legs are longer and/or the body of the frog is smaller. Then, all the results were standardized and multiplied by 5 (/+,01+",23 4%3"#( ∗ 5). In that sense, a value of 5 would reflect the maximum ability of an individual exploring his environment at a maximum of 5 pixels around in the game board. For the model, the average between species that are in the same group, rounded , was used (Table 2).
Processes:
The same three species groups are created at setup (Diagram1), but they can be extirpated throughout the simulation depending on the users’ choices. The user can modify the patches change rate with the provided sliders, adjusting levels of deforestation, and restoration (Figure 1). All agents are assumed to be adults with reproductive capacity. As soon as the user set up the simulation, the patches acquire established values for the variables (Table 1). All anuran individuals have
Dendropsophus mathiassoni 15 3 21 1.5 3.4
Amazophrynella minuta 3 4 Seasonal 18.035 1.3 3.9
Pristimantis variabilis 1 2 16.47 0.9 5.5
Engystomops petersi 2 5 Seasonal 27.365 4.6 1.1
Pristimantis acuminatus 1 3 23.15 3.2 1.5
Pristimantis conspicillatus 1 2 34.62 5.6 0.9
Leg -Weight Ratio
Movement in model SPECIES # Individuals
RELATIVE ENERGY CONSUMPTION IN
REPRODUCTION (1-5) REPRODUCTION LRC
a base movement this means that they are never still and always consume at least 1 unit of energy.
Diagram 1: At setup, the code fist creates the environment with each variable. Then it creates the agents, and locates them in the previously created environment, and gives values to their variables.
Then, when the simulation starts, the agents check their surroundings in a radius ranging from 1 to 5 pixels depending on their Leg-Weight Ratio (Table 2) as the maximum distance they can disperse in one tick. Depending on the group to which the species belongs the individuals inspect if the variables are within their suitable range to a greater or lesser number of pixels in the surrounding area (Table 1). If they’re not within their range, the frog will “run” facing the nearest suitable patch. Running is determined by: The leg-weight ratio and available energy, and thus if energy is 0 the frog dies and disappears from the board (Diagram 2).
Figure 1: Illustration of the agent-based model. Each habitat (adjacent rectangles of different colors on the board) is proportional at set up having 30 x 60 pixels.
In brown we have the pasture, in yellow we have the forest edge, and in green the forest interior. Anuran individuals have the shape of frogs, and they represent one of the 3 species groups mentioned: yellow species are affine for pasture; blue is affine for forest edge; and green is affine for forest interior. At go, patches would either turn orange, to represent natural regeneration, or blue, to represent restoration, or brown, to represent deforestation.
Diagram 2: Flowchart of the decision making of Anuran species in the model. The cloud represents a connection with diagram 3.
On the contrary, if the area surrounding an anuran individual is optimal, it will wait on the set reproduction time and acquire energy in the meantime. All anuran individuals have a 50% chance of creating a new individual, this is to represent that half of the population are female and must have at least 80 units of energy to do so. The new anuran individual hatches with a third of the parent’s energy and a random direction (Diagram 2). The above configuration assumes that changes in the structure of the environment will affect the chances of reproduction (Cayuela et al., 2014).
Once the anuran individuals have responded, either by dispersing, reproducing, or acquiring energy, the environment changes according to the selected numbers in the slider, this provokes a change in the proportion of the environment and randomizes the environmental conditions at the forest edge (Diagram 3).
Depending on the user, three land use/land cover transitions scenarios can be set, all of which have been reported to affect the amphibians: Deforestation dominance (Agudelo-Hz et al., 2019; Cushman, 2005; Nori et al., 2015), natural regeneration after land abandonment dominance (Hernández-Ordóñez et al., 2015; Herrera-Montes & Brokaw, 2010; Suazo-Ortuño, Urbina-Cardona, et al., 2018), or ecological restoration dominance (Brodman et al., 2006; Clauzel et al., 2015; Díaz-García et al., 2017).
Diagram 3: Flowchart of the patches change by neighbors. The change generates new variables in the environment, that’s linked with the Anura decision making, represented through the cloud in the top right.
Application:
After attending the classes of edge and matrix effects on biodiversity module in the conservation ecology (undergraduate Ecology), and conservation biology (MSc Conservation and Biodiversity Use) courses, students were introduced to the current NetLogo model and asked for their feedback to make the graphical interface more user-friendly. A total of 24 students from both academic programs participated voluntarily. They were surveyed after the class on edge and matrix effects (full questionnaire can be found in supplementary material 2).
Results:
The model works in a somewhat realistic matter as it considers environmental variables of the habitat and morphological traits of the frogs measured on the field (Palomino-Cuellar 2019). Because of it, when used in class it serves as an example on how some anuran species react differently to the edge. It also helped the students to understand better the concept of edge effect as corroborated by the answers of a simple poll answered by 24 of his students, where 95.8% of the students stated that the use of the model was beneficial. Furthermore, in the poll, students suggested several other topics that they think could be benefited by this kind of explanation. Overall, the utilization of the model in class allowed the students to approach a tough subject with relative ease enabling a more dynamic class, as students asked several questions about the model as they interacted with it. The following describes how the model was improved after feedback from the students as users.
Description of the user's interaction experience with the model:
The 14 students that have seen the conservation ecology class (Ecology undergraduate program - Pontificia Universidad Javeriana) were more reserved when giving feedback when compared with the 10 students from the master’s degree conservation biology class. The undergraduate students pay attention to the explanation on what was the purpose of the model and how it worked, and after that, the three different scenarios were shown to them, after class, some students wanted to get more details about the model, and so an extra demonstration was given. On the contrary, the interaction with the students of the master's program was more dynamic, meaning they rose their hand constantly to ask why things were happening in the model instead of waiting for the teacher to ask them; they also asked to generate more scenarios, in which they were able to see deforestation, restoration and abandonment. Both approaches of the students exemplify the usefulness of the model, as different attitudes were evident to the questioning of the three different scenarios and the whole usage of the model as a teaching tool.
Deforestation:
Figure 2: Model setup for deforestation scenario. The Slider is at 100 for deforestation, while reforestation is at 0
When the students favored deforestation with the sliders (Figure 2), they were able to see how fast the pasture overtakes the other landcover type (rainforest edge and interior), forcing the frogs that inhabited the forest edge and interior to disperse into the remaining forest, and finally visualizing the extinction of rainforest associated species, and the population increase of the species related to the pasture. The user can visualize that the yellow species that inhabit the pasture reproduce very quickly but no large amounts are scattered across the pixels, this happens as they are programed to move to the best patch possible, but as they considered that the ones near them are optimal, and there is no anuran density limit for the patches, they agglomerate at the left of the screen leaving most of the pasture habitat (brown color pixels) without frogs (Figure 3).
Figure 3: End of the simulation of deforestation. Deforestation overtook the other habitats and extirpated the other 2 groups of native forest affine anuran species.
Land abandonment:
Figure 4: Model setup for land abandonment and natural regeneration. All sliders are at 0.
As the class progressed, students asked what happened to the anurans when there was no deforestation and no restoration, just only the abandonment of the pasture to make way for natural regeneration (Figure 4). This scenario shows how the natural regeneration grows overtaking the pasture, and extirpation of the group 1 of anuran species affine for pasture (yellow color species), while both forest edge (blue color species) and interior (green color species) affine species grow rapidly in abundance (Figure 5). A thing that generated questions was that a small number of pixels turned green, symbolizing a growth in inner forest. This happens because, even though is not driven by humans, nature is able to recover some of its properties, showing that even with 0 human effort to restore, natural regeneration eventually increases forest interior, buffering edge effects.
Figure 5: End of the land abandonment simulation. The paddock was overtaken by natural regeneration, in color orange, eliminating the group 1 of pasture affine frogs while native forest affine anurans groups 2 and 3 grew over time.
Restoration:
Figure 6: Model setup for restoration. Restoration slider is set to 100, deforestation is set to 0.
When looking at restoration, students made the mix of 100 in restoration and 0 deforestation (Figure 6). When the model starts, restoration (blue pixels) will emerge and forest interior (green pixels) borders move quickly to the left, while native forest edge affine frogs (blue species) and forest interior frogs (green species) reproduce normally. When restoration overtakes the whole pasture, the pasture affine frogs (yellow species) begin to die, eventually there’s only blue and green pixels with both green and blue anuran species (Figure 7).
Figure 7: End of the Restoration simulation. The pasture was overtaken by restoration, dark blue pixels, eliminating the group 1 of frogs, and forest interior grew. Groups 2 and 3 of native forest affine anuran species grew over time.
Recommendations from students based on their experience of use
Answers form the students to the poll were centered around the usefulness of the model, specifically most (95.83%) answered positively when asked if they thought that the model enhanced their learning experience (Figure 8). Furthermore, students found helpful (91.67%) the fact that they can change the variables to see different outcomes, allowing a better visualization of what is being explained by the teacher (Figure 9).
Figure 8: Answer to the first question: “Do you believe that using this model was beneficial for your understanding of the Edge Effect?” from 24 students from two different academic programs Blue= no, this tool confused me more, orange= yes, I consider that it was easy to use.
Figure 9: Answer to the second question “Did you experiment with the sliders?” for 24 students from two different academic programs interacted with the sliders, promoting engagement in class.
Yellow=No, blue = Yes
However, when asked if the model was easy to understand 16.67% of students said that it would be necessary to make it more user-friendly (Figure 10). Amongst the recommendations, there are some that cannot be addressed as they are limited by the interface of Netlogo (like changing the sliders form, adding different colors to the sliders, changing the graphs, making the titles bold, and adding more fluid animations).
Figure 10: Answers to the third question: “Do you think that the model was easy to use?” for 24 students. Light yellow= No, dark yellow= Yes .
Nevertheless, there were some recommendations that will be implemented in the future: a counter to know the number of individuals per group as a complement to the graphs, and an easily accessible manual. They also wanted to know the specific species used, their names were hidden as this was not seen as useful to explain the edge effect, however this information can easily be placed at the bottom of the model so that they know fully what is being represented.
Additionally, they suggested a list of instructions somewhere in the model. This was deemed unnecessary at the beginning as the teacher would be the one providing the guidance, yet the students wanting some written general instructions, shows their desire to experiment with the model, this would be
16,67%
83,33%
Do you think that the model was easy to use? N= 24
No, It need to be friendlier Yes, It was easy to use and understand
especially useful before an exam as a study tool; therefore, it will be necessary to develop a video tutorial and a detailed manual to explain the configuration of the land use/land cover transition scenarios for future users.
Finally, students answered how would they expand this kind of tool, some answered off topic, but others gave good ideas to improve this further or even make a new one. They suggested to make more emphasis on natural succession, being more specific about the type of forest that was used and loss of native species by competing against invasive species. This is important, as it shows students feelings towards the model, and the possibility of ramifications.
Discussion:
Agent-based models to explain landscape transformation and their effects on biodiversity:
Agent based modeling can be as complex or as simple, according to the user needs. Using a “simple” agent model, with an assumption of homogeneity is enough when trying to show a problem in such way that in can be used in a pedagogical environment (Brown et al., 2004), however this of course implies that the models need to leave out information, that for the scientific community is needed to improve the rigor and robustness of information regarding the research topic (Brown & Robinson, 2006). As such, agent modeling has been used as a way to test diverse scientific hypothesis through simulations, especially regarding biodiversity. A study from Chetcuti (2021) used agent-based modeling to show the impact of fragmentation regardless of habitat loss, concluding that different
matrix can interact differently with the fragments, having beneficial or negative consequences for the biodiversity. However, the information provided by this article, falls on the complexity side that cannot be used in class without prior conceptual training (Walls & Gabor, 2019), even when the code if free and available.
In contrast, Horiuchi and Takasaki (2012) agent modeling sought to understand how species take advantage of their space when they are in groups. They did it in such a way that helped decision maker acquire the information to understand the behavior and environment used by the Japanese macaque, beginning a dialogue to promote its conservation. With this, even though there is still room for complex models in the academy, easily understandable explanations are still needed and have been proven useful to ensure that processes of landscape transformation and their impact on biodiversity could be considered in decision making (Carey & Gougis, 2017; Farrell et al., 2018; Read et al., 2016).
Grouping Anura:
Grouping Anura species has been used to provide needed information when trying to conserve or restore forest. This is as such, since it would be nearly impossible to target every amphibian species individually; only those with specific goals could do. Effectiveness is crucial when attempting to generate plans for amphibian conservation in transformed landscapes (Zabala-Forero & Urbina- Cardona, 2021), as they are so many and so sensible to changes. Functional ecology is an approach to understand the functions and responses of species within their environment, this acknowledges the roles within a habitat and how
they react to changes in their environment in terms of their functional traits (Salgado-Negret & Paz, 2016). Functional traits of anurans such as snout-vent length, leg length and body weight are crucial for understanding the response of anurans to environmental filters (Álvarez-Grzybowska et al., 2020). Based on the value of functional traits of anuran species it is possible to classify anuran species into functional groups which are assumed to respond similarly to the transformation of the landscape. (Trimble & van Aarde, 2014). In this study, the grouping was done by the ratio between leg length and body weight, type of habitat used and reproduction. Similar results were obtained by Suazo-Ortuño et al (2018) in Mexico, as they considered habitat preference, body size and reproduction habits, to understand the damage that a hurricane caused on anuran functional groups composition.
Palomino-Cuellar (2019) noticed that environmental factors were the main drivers for diversity and composition, especially temperature, light penetration, distance to the border, relative humidity and distance to nearest body of water for the amphibians of the rainforest in the Caquetá department, Colombia. Furthermore, Palomino-Cuellar (2019) groups the Anura she found into two groups, utilizing reproduction, type of habit and interdigital membrane, as the classifiers.
Even though the two groups proposed by Palomino-Cuellar (2019) cannot be used in the model context, her finding, alongside other, matches the knowledge of how environmental variables impact amphibian ensembles, where temperature (Álvarez-Grzybowska et al., 2020; Harper et al., 2005; J. N. Urbina-Cardona et al., 2006), canopy coverage (Cortés-Gómez et al., 2013; J. N. Urbina-Cardona et
al., 2006) and relative humidity (Lehtinen et al., 2003; Santos-Barrera & Urbina- Cardona, 2011), are amongst the more revised. Other variables like understory density,(Pearman, 1997) and wind (Lehtinen et al., 2003) have shown effect upon the Anura, but were not included in the model in order to maintain it simple.
The raw data from Palomino-Cuellar (2019) was filtered for this paper, by the three mayor variables exposed by the research revised above, showing that the use of canopy, temperature and humidity, alongside the reproduction and size of the amphibians, are good representatives of the species and groups in the model.
Energy Usage:
Energy availability will determine performance and functional properties, such as reproduction and movement (Lillywhite, 2016). Furthermore, amphibians, being ectothermic, are very dependent on the environment to acquire and regulate their energy, expressing a lack or imbalance of energy in changes in their general behavior, impacting their ability to move, feed and/or reproduce (Lillywhite, 2016).
Moreover, amphibians are recognized for spending little energy (2%) in to heating their body, and utilizing 50% of the available energy into generating tissue, leaving the rest for reproducing, moving, defending, between other activities; this entails that when eaten, amphibians give a substantial amount of energy to the predator (Crump, 2010). This would be a great way to add more complexity to the model, or do a new one, in which the dispersal of energy when predated is tracked when there is and there is no edge.
Additionally, it has been shown that energy consumption varies according to body size and temperature even when the anuran is at rest (Wells, 2007), meaning that changes in environment affect the rate at which amphibians consume energy at all times (Wells, 2007). This was not contemplated in the present model, as representing energy differently through the groups was thought to hinder the ability of the model to explain the edge effect, as it would have increased complexity.
Using the agent-based model of rainforest anurans to internalize the concepts of edge effects
The results show that this model is able to exemplify the edge effect in a simple way using three key environmental variables and the application of this model in teaching activities was useful, as it provided a tool for students to understand a complex topic online and allowed the teacher to discuss the topic virtually. This kind of models have been used very little (Murphy et al., 2020) to mediate meaningful learning in students, both as a method of teaching and a way to understand learning, but it has seen usage when describing complex biological and social process in scientific publications (Koster et al., 2016). This poses a challenge in translating complex scientific literature to keep students motivated through simple models and case studies. The ability to utilize agent-based models also enables the dialogue to “initiate discussion between experts and stakeholders bringing together different expertise” (van Berkel & Verburg, 2012), allowing for better, more enriched, decisions.
Recently, there has been some approaches to improve and expand the usage of agent-based modeling as a teaching and training tool as the extent of field investigations were reduced due to the COVID-19 pandemic (Murphy et al., 2020). Murphy (2020), breaks down the main components of an agent model and explains benefits to both students and teachers, in such way that one can see different approaches to it. The steps provided correspond with the objective and theme of the model done in this project.
Conclusion
This study was based on the amphibian data previously collected in the field by Palomino-Cuellar (2019), which were the support that allowed the creation of an Agent-Based Model that was able to represent the responses of three groups of amphibians to the edge effect. This model not only considered the functional traits that determine the movement and reproduction of the study species, but also constrained the dispersal of individuals based on the values of three environmental habitat variables (temperature, canopy cover and relative humidity) that are important for amphibians. Additionally, the model had several stages, in which it was intended to maintain the core functionality of the model, but to make it as easy to use as possibly so that it could be use in class by students. To do so, it was necessary to make the rules of action clear, as they are the steppingstones to maintain the realism of the model (to a certain extent).
Then, the model was presented to two classes, an undergraduate and a masters, to make a pilot on how the model would be used in a real class. From the pilot,
feedback was received for the model to improve one, showing that, in the future, could be used in class.
The edge effect is a highly sensible, complex and important topic to understand as it is intertwined with fragmentation, and so the main objective of this work was to generate a visual representation of it through modelling, however, it is believed that with enough usage and feedback this model can enter basic courses of ecology to aid on the explanation of this topic, giving more tools to both the teachers and the students possibly bettering the retention of the concept.
Recommendations:
The present model is versatile and flexible so that it can incorporate new parameters and change scenarios as the dynamics of edge-effect classes generate new interaction needs on the part of the teacher and his students. One of such incorporations could be to provide a manual how to use the model to promote independent learning from video tutorials. Another way where the model can improve is from the environmental and vegetation structure variables as well as from specie´s functional traits. As it is known, niche and functional traits are very diverse, affecting the species differently, and so adding more variables, such as wind or rain could add more realism (but more complexity at the same time) to the model, being more useful in the Master’s and Doctoral degree classes. The final, recommendation is for the readers and users, they must not be afraid to play with the model and the code, as it is free, they are able to learn more from the model by using it, adding, and subtracting lines from the code asking to the code the questions they want to answer.
Acknowledgments:
This paper could not be done without the support and love from my mom, my dad and Carolina to whom I owe to always give 100%, to Nicolás Urbina and Mauricio González for revising, correcting, and guiding me through this process, giving special thanks to their patience, to Laura Velasquez for giving advice, helping me organize my ideas and encouraging me. Finally, to F.E.A.R for being my home for these 5 years and the next to come.
References:
Agudelo-Hz, W. J., Urbina-Cardona, N., & Armenteras-Pascual, D. (2019).
Critical shifts on spatial traits and the risk of extinction of Andean anurans:
an assessment of the combined effects of climate and land-use change in Colombia. Perspectives in Ecology and Conservation, 17(4), 206–219.
https://doi.org/10.1016/j.pecon.2019.11.002
Alcaldía Municipal de Solano. (2021, May 27). Nuestro municipio.
http://www.solano-caqueta.gov.co/municipio/nuestro-municipio
Álvarez-Grzybowska, E., Urbina-Cardona, N., Córdova-Tapia, F., & García, A.
(2020). Amphibian communities in two contrasting ecosystems: functional diversity and environmental filters. Biodiversity and Conservation, 29(8), 2457–2485. https://doi.org/10.1007/s10531-020-01984-w
Anderson, T. M., & Dragićević, S. (2018). Network-agent based model for simulating the dynamic spatial network structure of complex ecological systems. Ecological Modelling, 389, 19–32.
https://doi.org/10.1016/j.ecolmodel.2018.10.008
Armenteras, D., Murcia, U., González, T. M., Barón, O. J., & Arias, J. E. (2019).
Scenarios of land use and land cover change for NW Amazonia: Impact on forest intactness. Global Ecology and Conservation, 17, e00567.
https://doi.org/10.1016/J.GECCO.2019.E00567
Arroyo-Rodríguez, V., Fahrig, L., Tabarelli, M., Watling, J. I., Tischendorf, L., Benchimol, M., Cazetta, E., Faria, D., Leal, I. R., Melo, F. P. L., Morante- Filho, J. C., Santos, B. A., Arasa-Gisbert, R., Arce-Peña, N., Cervantes- López, M. J., Cudney-Valenzuela, S., Galán-Acedo, C., San-José, M., Vieira, I. C. G., … Tscharntke, T. (2020a). Designing optimal human- modified landscapes for forest biodiversity conservation. Ecology Letters, 23(9), 1404–1420. https://doi.org/10.1111/ele.13535
Arroyo-Rodríguez, V., Fahrig, L., Tabarelli, M., Watling, J. I., Tischendorf, L., Benchimol, M., Cazetta, E., Faria, D., Leal, I. R., Melo, F. P. L., Morante- Filho, J. C., Santos, B. A., Arasa-Gisbert, R., Arce-Peña, N., Cervantes- López, M. J., Cudney-Valenzuela, S., Galán-Acedo, C., San-José, M., Vieira, I. C. G., … Tscharntke, T. (2020b). Designing optimal human-
modified landscapes for forest biodiversity conservation. Ecology Letters, 23(9), 1404–1420. https://doi.org/10.1111/ELE.13535
Ascensão, F., Clevenger, A., Santos-Reis, M., Urbano, P., & Jackson, N.
(2013). Wildlife-vehicle collision mitigation: Is partial fencing the answer?
An agent-based model approach. Ecological Modelling, 257, 36–43.
https://doi.org/10.1016/j.ecolmodel.2013.02.026
Barbati, M., Bruno, G., & Genovese, A. (2012). Applications of agent-based models for optimization problems: A literature review. Expert Systems with Applications, 39(5), 6020–6028.
https://doi.org/10.1016/J.ESWA.2011.12.015
Bautista-Cespedes, O. V., Willemen, L., Castro-Nunez, A., & Groen, T. A.
(2021). The effects of armed conflict on forest cover changes across temporal and spatial scales in the Colombian Amazon. Regional Environmental Change 2021 21:3, 21(3), 1–16.
Bertalanffy, L. Von. (1950). AN OUTLINE OF GENERAL SYSTEM THEORY.
The British Journal for the Philosophy of Science, I(2), 134–165.
https://doi.org/10.1093/bjps/I.2.134
Blaustein, A. R., Han, B. A., Relyea, R. A., Johnson, P. T. J., Buck, J. C., Gervasi, S. S., & Kats, L. B. (2011). The complexity of amphibian population declines: Understanding the role of cofactors in driving
amphibian losses. Annals of the New York Academy of Sciences, 1223(1), 108–119. https://doi.org/10.1111/j.1749-6632.2010.05909.x
Brewer, C. (2001). Cultivating Conservation Literacy: “Trickle-Down” Education Is Not Enough. Conservation Biology, 15(5), 1203–1205.
http://www.jstor.org/stable/3061474
Brewer, C. (2006). Translating Data into Meaning: Education in Conservation Biology. Conservation Biology, 20(3), 689–691.
http://www.jstor.org/stable/3879232
Brodman, R., Parrish, M., Kraus, H., & Cortwright, S. (2006). Amphibian biodiversity recovery in a large - scale ecosystem restoration.
Herpetological Conservation and Biology, 1(May), 101–108.
Brown, D. G., Page, S. E., Riolo, R., & Rand, W. (2004). Agent-based and analytical modeling to evaluate the effectiveness of greenbelts.
Environmental Modelling and Software, 19(12), 1097–1109.
https://doi.org/10.1016/j.envsoft.2003.11.012
Brown, D. G., & Robinson, D. T. (2006). Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and Society, 11(1). https://doi.org/10.5751/ES-01749-110146
Carey, C. C., & Gougis, R. D. (2017). Simulation Modeling of Lakes in
Undergraduate and Graduate Classrooms Increases Comprehension of Climate Change Concepts and Experience with Computational Tools.
Journal of Science Education and Technology, 26(1), 1–11.
https://doi.org/10.1007/s10956-016-9644-2
Cayuela, H., Besnard, A., Bonnaire, E., Perret, H., Rivoalen, J., Miaud, C., &
Joly, P. (2014). To breed or not to breed: Past reproductive status and environmental cues drive current breeding decisions in a long-lived amphibian. Oecologia, 176(1), 107–116. https://doi.org/10.1007/s00442- 014-3003-x
Chetcuti, J., Kunin, W. E., & Bullock, J. M. (2021). Matrix composition mediates effects of habitat fragmentation: a modelling study. Landscape Ecology,
36(6), 1631–1646. https://doi.org/10.1007/S10980-021-01243- 5/FIGURES/6
Chopin, P., Bergkvist, G., & Hossard, L. (2019). Modelling biodiversity change in agricultural landscape scenarios - A review and prospects for future research. Biological Conservation, 235, 1–17.
https://doi.org/10.1016/J.BIOCON.2019.03.046
Clauzel, C., Bannwarth, C., & Foltete, J. C. (2015). Integrating regional-scale connectivity in habitat restoration: An application for amphibian
conservation in eastern France. Journal for Nature Conservation, 23, 98–
107. https://doi.org/10.1016/j.jnc.2014.07.001
Connelly, S., Pringle, C. M., Bixby, R. J., Brenes, R., Whiles, M. R., Lips, K. R., Kilham, S., & Huryn, A. D. (2008). Changes in stream primary producer communities resulting from large-scale catastrophic amphibian declines:
Can small-scale experiments predict effects of tadpole loss? Ecosystems, 11(8), 1262–1276. https://doi.org/10.1007/s10021-008-9191-7
Correa Ayram, C. A., Etter, A., Díaz-Timoté, J., Rodríguez Buriticá, S., Ramírez, W., & Corzo, G. (2020). Spatiotemporal evaluation of the human footprint in Colombia: Four decades of anthropic impact in highly biodiverse
ecosystems. Ecological Indicators, 117, 106630.
https://doi.org/10.1016/j.ecolind.2020.106630
Cortés-Gómez, A. M., Castro-Herrera, F., & Nicolás Urbina-Cardona, J. (2013).
Small changes in vegetation structure create great changes in amphibian ensembles in the Colombian Pacific rainforest. Research Article
Mongabay.Com Open Access Journal-Tropical Conservation Science, 6(6), 749–769.
Cortés-Gómez, A. M., Ramírez, M. P., & Urbina-Cardona, N. (2015). Protocolo para medición de rasgos funcionales en anfibios. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, 126–179.
Crump, M. L. (2010). Amphibian diversity and life history. In Amphibian ecology and conservation (p. 556). https://doi.org/10.1073/pnas.0703993104
Cushman, S. A. (2005). Effects of habitat loss and fragmentation on amphibians: A review and prospectus.
https://doi.org/10.1016/j.biocon.2005.09.031
Dale, V. H., Pearson, S. M., Offerman, H. L., & O’Neill, R. V. (1994). Relating Patterns of Land-Use Change to Faunal Biodiversity in the Central Amazon. Conservation Biology, 8(4), 1027–1036.
https://doi.org/10.1046/J.1523-1739.1994.08041027.X DANE. (2018). Municipal_area_1985-2020.
DeAngelis, D. L., & Mooij, W. M. (2005). Individual-Based Modeling of Ecological and Evolutionary Processes. Annual Review of Ecology, Evolution, and Systematics, 36(1), 147–168.
https://doi.org/10.1146/annurev.ecolsys.36.102003.152644
Díaz-García, J. M., Pineda, E., López-Barrera, F., & Moreno, C. E. (2017).
Amphibian species and functional diversity as indicators of restoration success in tropical montane forest. Biodiversity and Conservation, 26(11), 2569–2589. https://doi.org/10.1007/s10531-017-1372-2
Dickson, D. (1993). Simplify science? I’ll drink to that. In Nature (Vol. 362, Issue 6423, p. 781). https://doi.org/10.1038/362781b0
Didham, R. K., Kapos, V., & Ewers, R. M. (2012). Rethinking the conceptual foundations of habitat fragmentation research. Oikos, 121(2), 161–170.
https://doi.org/10.1111/j.1600-0706.2011.20273.x
Driscoll, D. A., Banks, S. C., Barton, P. S., Lindenmayer, D. B., & Smith, A. L.
(2013). Conceptual domain of the matrix in fragmented landscapes. Trends in Ecology and Evolution, 28(10), 605–613.
https://doi.org/10.1016/j.tree.2013.06.010 Epstein, J. M. (2008). Why Model?
Etter, A. (1991). Introducción a la Ecología del Paisaje: un marco de integración para los levantamientos ecológicos. Landscape Ecology, 32(3)(October 1991), 25–56.
https://www.researchgate.net/publication/266391069_INTRODUCCION_A_
LA_ECOLOGIA_DEL_PAISAJE_Un_Marco_de_Integracion_para_los_Lev antamientos_Ecologicos?channel=doi&linkId=543052fb0cf29bbc12771d98
&showFulltext=true
Etter, A., McAlpine, C., Phinn, S., Pullar, D., & Possingham, H. (2006).
Unplanned land clearing of Colombian rainforests: Spreading like disease?
Landscape and Urban Planning, 77(3), 240–254.
https://doi.org/10.1016/j.landurbplan.2005.03.002
Etter, A., McAlpine, C., & Possingham, H. (2008). Historical patterns and drivers of landscape change in Colombia since 1500: A regionalized spatial
approach. Annals of the Association of American Geographers, 98(1), 2–
23. https://doi.org/10.1080/00045600701733911
Etter, A., Phinn, S., Mcalpine, C., Pullar, D., & Possingham, H. (2006).
Characterizing a tropical deforestation wave: A dynamic spatial analysis of a deforestation hotspot in the Colombian Amazon. Global Change Biology, 12(8), 1409–1420. https://doi.org/10.1111/j.1365-2486.2006.01168.x Farrell, K. J., Carey, C. C., & Kaitlin Farrell, C. J. (2018). Power, pitfalls, and
potential for integrating computational literacy into undergraduate ecology courses. Ecology and Evolution, 1, 1–8. https://doi.org/10.1002/ece3.4363 Ferraz, K. M. P. M. de, Morato, R. G., Bovo, A. A. A., Costa, C. O. R., Ribeiro,
Y. G. G., Paula, R. C., Desbiez, A. L. J., Angelieri, C. S. C., & Traylor- Holzer, K. (2021). Bridging the gap between researchers, conservation planners, and decision makers to improve species conservation decision- making. Conservation Science and Practice, 3(2).
https://doi.org/10.1111/csp2.330
Fischer, J., & Lindenmayer, D. B. (2007). Landscape modification and habitat fragmentation: a synthesis Joern Fischer* and David B. Lindenmayer Centre. Global Ecology and Biogeography, 15(3), 55–66.
https://doi.org/10.1111/j.1466-8238.2006.00287.x
Fonseca, C. R., Coutinho, R. M., Azevedo, F., Berbert, J. M., Corso, G., &
Kraenkel, R. A. (2013). Modeling Habitat Split: Landscape and Life History Traits Determine Amphibian Extinction Thresholds. PLoS ONE, 8(6), e66806. https://doi.org/10.1371/journal.pone.0066806
Frigg, R., & Hartmann, S. (2020). Models in Science. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Spring 202). Metaphysics Research Lab, Stanford University.
https://plato.stanford.edu/archives/spr2020/entries/models-science/
Gaal, R. (2017, April). Science is Not Just For Scientists.
https://www.aps.org/publications/apsnews/201703/science.cfm
Gascon, G. (1993). Breeding-habitat use by five Amazonian frogs at forest edge. Biodiversity and Conservation, 2(4), 438–444.
https://doi.org/10.1007/BF00114045
Gazzaley, A., & Rosen, L. (2017). The Distracted Mind: Ancient Brains in a High-Tech World - Adam Gazzaley, Larry D. Rosen - Google Books.
Professional Safety, 2016–2018.
https://books.google.com.co/books?hl=es&lr=&id=o8sbDQAAQBAJ&oi=fnd
&pg=PR5&dq=The+Distracted+Mind+-+Ancient+Brains+in+a+High- Tech+World&ots=dkDqQL7ViL&sig=5GhsQnwl-
WaM214wqMtgGb2gQZg&redir_esc=y#v=onepage&q=The Distracted Mind - Ancient Brains in a High-Te
Gobernación de Caquetá. (2017). HISTORIA DEL CAQUETÁ - Gobernación del Caquetá. http://www.caqueta.gov.co/departamento/historia-del-caqueta Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S.
F. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221(23), 2760–2768.
https://doi.org/10.1016/j.ecolmodel.2010.08.019
Guerry, A. D., & Hunter, M. L. (2002). Amphibian distributions in a landscape of forests and agriculture: An examination of landscape composition and configuration. Conservation Biology, 16(3), 745–754.
https://doi.org/10.1046/J.1523-1739.2002.00557.X
Harper, K. A., Macdonald, S. E., Burton, P. J., Chen, J., Brosofske, K. D.,
Saunders, S. C., Euskirchen, E. S., Roberts, D., Jaiteh, M. S., & Esseen, P.
A. (2005). Edge influence on forest structure and composition in fragmented landscapes. Conservation Biology, 19(3), 768–782.
https://doi.org/10.1111/j.1523-1739.2005.00045.x
Hayes, T. B., Case, P., Chui, S., Chung, D., Haeffele, C., Haston, K., Lee, M., Mai, V. P., Marjuoa, Y., Parker, J., & Tsui, M. (2006). Pesticide mixtures, endocrine disruption, and amphibian declines: Are we underestimating the impact? Environmental Health Perspectives, 114(SUPPL.1), 40–50.
https://doi.org/10.1289/ehp.8051
Hernández-Ordóñez, O., Urbina-Cardona, N., & Martínez-Ramos, M. (2015).
Recovery of amphibian and reptile assemblages during old-field succession of tropical rain forests. Biotropica, 47(3), 377–388.
https://doi.org/10.1111/btp.12207
Herrera-Montes, A., & Brokaw, N. (2010). Conservation value of tropical secondary forest: A herpetofaunal perspective. Biological Conservation, 143(6), 1414–1422. https://doi.org/10.1016/j.biocon.2010.03.016
Hopkins, W. A. (2007). Amphibians as models for studying environmental change. In ILAR Journal (Vol. 48, Issue 3, pp. 270–277).
https://doi.org/10.1093/ilar.48.3.270
Horiuchi, S., & Takasaki, H. (2012). Boundary nature induces greater group size and group density in habitat edges: An agent-based model revealed.
Population Ecology, 54(1), 197–203. https://doi.org/10.1007/s10144-011- 0279-0
IDEAM. (2021). Resultados Del Monitoreo Deforestacion. In IDEAM.
IUCN. (2021). The IUCN Red List of Threatened Species.
Koster, A., Koch, F., Assumpção, N., & Primo, T. (2016). The role of agent- based simulation in education. Communications in Computer and
Information Science, 677, 156–167. https://doi.org/10.1007/978-3-319- 52039-1_10
Lehtinen, R. M., Ramanamanjato, J. B., & Raveloarison, J. G. (2003). Edge effects and extinction proneness in a herpetofauna from Madagascar.
Biodiversity and Conservation, 12(7), 1357–1370.
https://doi.org/10.1023/A:1023673301850
Leventon, J., Schaal, T., Velten, S., Dänhardt, J., Fischer, J., Abson, D. J., &
Newig, J. (2017). Collaboration or fragmentation? Biodiversity management through the common agricultural policy. Land Use Policy, 64, 1–12.
https://doi.org/10.1016/j.landusepol.2017.02.009
Lillywhite, H. B. (2016). Behavior and physiology An ecological and evolutionary viewpoint on the energy and water relations of ectothermic amphibians amphibians and reptiles. In Amphibian and Reptile Adapatations to the Environment. Interplay Between Physiology and Behavior (pp. 5–7).
Londoño, M. C., Saboyá, L. P., & Urbina-Cardona, N. (2019). Conocimiento científico de los efectos del cambio climático sobre la biodiversidad continental: productividad de las instituciones colombianas y propuesta para un análisis integral. 4(1), 26.
Mahmood, R., Pielke, R. A., Hubbard, K. G., Niyogi, D., Bonan, G., Lawrence, P., McNider, R., McAlpine, C., Etter, A., Gameda, S., Qian, B., Carleton, A., Beltran-Przekurat, A., Chase, T., Quintanar, A. I., Adegoke, J. O.,
Vezhapparambu, S., Conner, G., Asefi, S., … Syktus, J. (2010). Impacts of land use/land cover change on climate and future research priorities.
Bulletin of the American Meteorological Society, 91(1), 37–46.
https://doi.org/10.1175/2009BAMS2769.1
Murad, C. A., & Pearse, J. (2018). Landsat study of deforestation in the Amazon region of Colombia: Departments of Caquetá and Putumayo. Remote Sensing Applications: Society and Environment, 11, 161–171.
https://doi.org/10.1016/j.rsase.2018.07.003
Murcia, C. (1995). Edge effects in fragmented forests: implications for conservation. Trends in Ecology & Evolution, 10(2), 58–62.
https://doi.org/10.1016/S0169-5347(00)88977-6
Murillo-Sandoval, P. J., van Dexter, K., van den Hoek, J., Wrathall, D., &
Kennedy, R. (2020). The end of gunpoint conservation: forest disturbance after the Colombian peace agreement. Environmental Research Letters, 15(3), 034033. https://doi.org/10.1088/1748-9326/AB6AE3
Murphy, K. J., Ciuti, S., & Kane, A. (2020). An introduction to agent-based models as an accessible surrogate to field-based research and teaching.
Ecology and Evolution, 10(22), 12482–12498.
https://doi.org/10.1002/ECE3.6848
Negret, P. J., Sonter, L., Watson, J. E. M., Possingham, H. P., Jones, K. R., Suarez, C., Ochoa-Quintero, J. M., & Maron, M. (2019). Emerging evidence that armed conflict and coca cultivation influence deforestation patterns.
Biological Conservation, 239, 108176.
https://doi.org/10.1016/J.BIOCON.2019.07.021
Niyogi, D., Mahmood, R., & Adegoke, J. O. (2009). Land-use/land-cover change and its impacts on weather and climate. In Boundary-Layer Meteorology (Vol. 133, Issue 3, pp. 297–298). https://doi.org/10.1007/s10546-009-9437- 8
Nori, J., Lemes, P., Urbina-Cardona, N., Baldo, D., Lescano, J., & Loyola, R.
(2015). Amphibian conservation, land-use changes and protected areas: A global overview. 191, 367–374.
Page, S. (2008). Agent-based models. The New Palgrave Dictionary of Economics, 1(8).
Palomino-Cuellar, J. V. (2019). Diversidad funcional y taxonomica de anfibios en diferentes coberturas de la tierra en una porcion de la llanura
Amazonica Caqueteña.
Pearman, P. B. (1997). Correlates of amphibian diversity in an altered landscape of Amazonian Ecuador. Conservation Biology, 11(5), 1211–
1225. https://doi.org/10.1046/J.1523-1739.1997.96202.X
Pfeifer, M., Lefebvre, V., Peres, C. A., Banks-Leite, C., Wearn, O. R., Marsh, C.
J., Butchart, S. H. M., Arroyo-Rodríguez, V., Barlow, J., Cerezo, A.,
Cisneros, L., D’Cruze, N., Faria, D., Hadley, A., Harris, S. M., Klingbeil, B.
T., Kormann, U., Lens, L., Medina-Rangel, G. F., … Ewers, R. M. (2017).
Creation of forest edges has a global impact on forest vertebrates. Nature, 551(7679), 187–191. https://doi.org/10.1038/nature24457
Portik, D. M., Leaché, A. D., Rivera, D., Barej, M. F., Burger, M., Hirschfeld, M., Rödel, M. O., Blackburn, D. C., & Fujita, M. K. (2017). Evaluating
mechanisms of diversification in a Guineo-Congolian tropical forest frog using demographic model selection. Molecular Ecology, 26(19), 5245–
5263. https://doi.org/10.1111/mec.14266
Ranvestel, A. W., Lips, K. R., Pringle, C. M., Whiles, M. R., & Bixby, R. J.
(2004). Neotropical tadpoles influence stream benthos: Evidence for the ecological consequences of decline in amphibian populations. Freshwater Biology, 49(3), 274–285. https://doi.org/10.1111/j.1365-2427.2004.01184.x Read, E. K., O’Rourke, M., Hong, G. S., Hanson, P. C., Winslow, L. A.,
Crowley, S., Brewer, C. A., & Weathers, K. C. (2016). Building the team for team science. Ecosphere, 7(3). https://doi.org/10.1002/ecs2.1291
Redford, K. H., Groves, C., Medellin, R. A., & Robinson, J. G. (2012).
Conservation Stories, Conservation Science, and the Role of the Intergovernmental Platform on Biodiversity and Ecosystem Services.
Conservation Biology, 26(5), 757–759. https://doi.org/10.1111/J.1523- 1739.2012.01925.X
Relyea, R., & Hoverman, J. (2006). Assessing the ecology in ecotoxicology: A review and synthesis in freshwater systems. Ecology Letters, 9(10), 1157–
1171. https://doi.org/10.1111/j.1461-0248.2006.00966.x
Ries, L., Fletcher, R. J., Battin, J., & Sisk, T. D. (2004). Ecological responses to habitat edges: Mechanisms, models, and variability explained. Annual Review of Ecology, Evolution, and Systematics, 35, 491–522.
https://doi.org/10.1146/annurev.ecolsys.35.112202.130148
Rosen, J. (2018). Help to shape policy with your science. Nature, 560(7720), 671–673. https://doi.org/10.1038/D41586-018-06038-4
Salgado-Negret, B., & Paz, H. (2016). Escalando de los rasgos funcionales a procesos poblacionales, comunitarios y ecosistémicos. In La Ecología Funcional como aproximación al estudio, manejo y conservación de la biodiversidad: protocolos y aplicaciones.
Santos-Barrera, G., & Urbina-Cardona, J. N. (2011). The role of the matrix-edge dynamics of amphibian conservation in tropical montane fragmented
