A novel modeling approach for the “end-to-end” analysis of marine ecosystems

    A novel modeling approach for the “end-to-end” analysis of marine ecosystems Candelaria E. Sansores, Flavio Reyes-Ram´ırez, Luis E. Calderon-Aguilera, H´ector F. G´omez PII: DOI: Reference:

S1574-9541(16)00002-9 doi: 10.1016/j.ecoinf.2016.01.001 ECOINF 650

To appear in:

Ecological Informatics

Received date: Revised date: Accepted date:

1 September 2015 29 November 2015 3 January 2016

Please cite this article as: Sansores, Candelaria E., Reyes-Ram´ırez, Flavio, Calderon-Aguilera, Luis E., G´ omez, H´ector F., A novel modeling approach for the “end-to-end” analysis of marine ecosystems, Ecological Informatics (2016), doi: 10.1016/j.ecoinf.2016.01.001

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT A novel modeling approach for the “End-to-End” analysis of marine ecosystems

RI

PT

Candelaria E. Sansoresa,*, [email protected] a Universidad del Caribe, Complex System Simulation Laboratory, SM.78, Mza. 1, Lote 1, Fracc. Tabachines, Cancún, 77528, México.

SC

Flavio Reyes-Ramíreza, [email protected] Universidad del Caribe, Complex System Simulation Laboratory, SM.78, Mza. 1, Lote 1, Fracc. Tabachines, Cancún, 77528, México.

NU

a

Luis E. Calderon-Aguilerab, [email protected] Centro de Investigación Científica y de Educación Superior de Ensenada, km 107 Carretera Tijuana-Ensenada, Ensenada CP 22860 B.C., México.

MA

b

TE

D

Héctor F. Gómeza, [email protected] a Universidad del Caribe, Complex System Simulation Laboratory, SM.78, Mza. 1, Lote 1, Fracc. Tabachines, Cancún, 77528, México.

AC CE P

*Corresponding author: [email protected] Phone: +52-998-8814400 ext. 1001

ACCEPTED MANUSCRIPT A novel modeling approach for the “End-to-End” analysis of marine ecosystems

Abstract. There is a growing demand for “end-to-end” models, which are

PT

modeling tools used to analyze and understand the fundamental complexities of marine ecosystems and processes emerging from the interaction of

RI

individuals from different trophic groups with respect to the physical

SC

environment and, even, human activity. These models are valuable quantitative tools for ecosystem-based management. To explore potential

NU

answers to complex questions regarding ecosystems using these models, it is necessary to incorporate classical ontogenic changes through the life cycle of target individuals, in addition to inherited behavioral strategies, as an

MA

additional differentiating aspect, particularly when the behavior has a direct impact on the ecosystem phenomena under study. However, it is difficult to combine different fine scale time and spatial granularities to infer animal

D

behavior and ontogenic development. This complexity has kept these two

TE

levels of analysis separated, because most current tools do not have the required computational resources and advanced software architecture. To

AC CE P

address this issue, we propose an individual-based modeling framework that is capable of handling and unifying the two experimental categories with a comprehensive biological and behavioral model that strictly adheres to the physiological functions of ingestion, growth, and metabolism of organisms. In addition, this model incorporates the exchange and transfer of mass and energy through local interactions at all trophic levels (lower to higher), the physical environment, and anthropogenic activity. For the framework to model short time events, such as classical predator-prey interactions, while also generating long-term ecosystem emergent properties, a special interleaving scheduling engine and physical space computer model was devised, which optimizes memory and processing resources. The framework was tested through several experiments with a three-population ecosystem containing up to 40 thousand organisms evolving inside a 200,000 m2 simulation environment during 12,000 model hours; yet, requiring only a few hours of program execution on a regular personal computer. The model included various environmental physical elements, such as several hundred shelters,

ACCEPTED MANUSCRIPT the number of which can be easily modified in each experiment to simulate substrate degradation and its impact on populations. With the aid of the quantitative and qualitative tools provided by the model, it was possible to

PT

observe a coupling between prey and predator population dynamics. In conclusion, we confirmed that the end-to-end model developed here could

RI

successfully generate detailed specific hypotheses about fish behavior and

SC

quantify impacts on population dynamics.

Keywords: Individual-based model; Fish behavior; Ecosystem

NU

management; Multi-agent systems; Agent-based simulation; Endto-end analysis

MA

1. Introduction

The individual-based modeling (IBM) paradigm is an alternative approach in

D

ecology and evolutionary theory for understanding fundamental ecosystem processes and complexities (Breckling et al., 2006; DeAngelis and Mooij,

TE

2005; Grimm and Railsback, 2005; Huse et al., 2008), which cannot be completely addressed by traditional equation-based models. IBM focuses on

AC CE P

bottom-up explanations, and on explaining how macro phenomena result from relatively simple, locally interacting organisms (Uchmański and Grimm, 1996). Under this paradigm, individual organisms exhibit unique and autonomous properties. These properties are first specified during model formulation and ultimately through model design and computer implementation, and must be present in IBMs as important aspects of differentiation among individuals. The formulation and implementation of IBMs are challenging tasks for ecologists. Formulation is challenging because the relevant properties assigned to describe individuals are not foreseen or easily determined during model conceptualization. In addition, these properties strongly depend on a particular ecological process under question and the questions that are being addressed. Implementation is challenging because, even if the model is conceptually and explicitly clearly formulated, inadequate selection and implementation of the computational artifacts to represent the concepts that it embodies could still occur. In the field of ecological modeling, and after 25 years of IBM development

ACCEPTED MANUSCRIPT (Bousquet and Le Page, 2004; DeAngelis and Gross, 1992; Grimm, 1999; Hogeweg and Hesper, 1990; Huston et al., 1988; Plagányi, 2007), many models, methods, and, even, modeling tools have been proposed to assist

PT

ecologists to perform both tasks. After reviewing some of these proposals, we found they may be generally classified into one of two categories according to

RI

their application domain: behavioral simulation and ecosystems simulation. This classification obeys the radically different spatial and time granularity and

SC

scale requirements of these two experimentation scenarios.

Behavioral simulations are early applications of the IBM paradigm where

NU

individual-level behavioral rules give rise to complex aggregated collective patterns. This paradigm requires several, but not too many, individuals;

MA

ranging from 10s to 100s of individuals, at most. This paradigm also requires fine spatial and time granularity, in the order of centimeters or meters and seconds, to be able to observe micro-behavioral interactions in a simulated

D

environment. Typically, this type of simulation does not require large spatial

TE

and time dimensions, as demonstrated by classical swarm and predator-prey models (Daewel et al., 2008; Hemelrijk and Kunz, 2005; McCauley et al.,

AC CE P

1993).

In comparison, individual-based ecosystem simulations were conceived as a complementary tool to classical state variable models. In these models, individual physiological processes and local interactions give rise to emergent aggregated population properties. This category demands that models encompass a large number of individuals belonging to several different species, 1000s or more, if possible, and, of course, a large spatial dimension to accommodate those individuals. Since emergent population dynamics are generated, a large time dimension is required, usually years or decades, to infer how a population changes over multiple generations. Coarse time and spatial granularity are enough in ecosystem simulations, in the order of 100s of meters and from several days to years, respectively. Classical simulations using this category include trophic network modeling (McDermot and Rose, 2000; Parrott and Kok, 2002; van Nes et al., 2002). However, to explore potential answers to complex questions about ecosystems, it is sometimes necessary to incorporate the behavioral factor as an additional differentiating aspect in ecosystem simulations, especially when

ACCEPTED MANUSCRIPT the behavior has a direct impact on the variables under study. For example, ecosystem-based management studies may question what the long time consequences of habitat degradation are or what would happen if habitat

PT

heterogeneity were lost? Small scale processes, like foraging behavior, space use by individuals, and local resource competition are important factors

RI

(Buchmann et al., 2013). Consequently, to answer these questions, it is necessary to incorporate the different behaviors exhibited by species that

SC

have a direct impact on their habitat into the model, in addition to observing the evolution of the entire ecosystem. This necessity creates an additional

NU

level of complexity by combining the requirements of the large temporal and spatial space of the ecosystem category with the fine granularity requirements

MA

of the behavioral category.

There is also growing interest in models of marine ecosystems that assess the effects of climate change and fishing on ecosystem dynamics (Fulton,

D

2010; Rose et al., 2010; Travers et al., 2007). These models, termed end-to-

TE

end models, combine several models of different parameters within a single modeling framework; including physicochemical and oceanographic models,

AC CE P

population models ranging from microbes to higher-trophic-level organisms, and anthropogenic activity and impact models. Once again, the capacity to combine long-term simulations with small-scale local interactions is of primary importance (Rose et al., 2010). To address the stated issues, we propose BioMASS (Biological Multi-Agent Simulation System), which is a modeling framework that unifies the two modeling categories (behavior and ecosystem) within a single simulation. We aimed to merge the two experimental categories within a single framework, in addition to linking the two levels of analysis by aggregating eco-physiological processes, behaviors, and interactions among individuals, to elucidate their impact on ecosystem variables. Furthermore, we provide a biological model that strictly adheres to the physiological functions of ingestion, growth, and metabolism of organisms and the exchange and transfer of mass and energy through trophic interactions. Overall, this approach surpasses classical predator-prey behavioral models. Our system may be run on a personal computer without additional hardware resources, because it supports large numbers of agents by using a physical space computer model that optimizes

ACCEPTED MANUSCRIPT memory and processing resources up to the point of supporting the simultaneous execution of 10s of 1000s of agents evolving in large dimensional spaces with high spatial resolution. For BioMASS to simulate

PT

short time events (such as classical predator-prey interactions), while generating long-term ecosystem emergent properties, a special interleaving

RI

scheduling engine was devised. BioMASS also addresses many of the open issues stated by Rose et al. (2010) to realize end-to-end models.

SC

Several modeling and simulation frameworks are regularly cited by publications in the field of ecological modeling, which are available as

NU

software toolkits that require some degree of programming to build simulation scenarios. These frameworks address simulation aspects like discrete time-

MA

event scheduling, charting, and 2D and 3D space modeling (Mason, 2015; Netlogo, 2015; Repast, 2015). However, because these frameworks tend to be general-purpose tools, they do not contain any domain-specific support

D

functionality.

TE

We propose a domain-oriented framework with built-in theoretical foundations capable of supporting diverse models and addressing broader questions for

AC CE P

experimentation. Equipped with a powerful, easy to use graphical user interface (GUI), BioMASS is a modeling and simulation multi-agent marine ecosystem framework that consists of a collection of concepts, rules, constraints, models, and theories in the form of meta-models. The goal of meta-models is to enforce heterogeneity and self-sufficiency abilities (the foundations of a true IBM) for artificial organisms and resources of a marine habitat. The meta-models are instantiated into natural ecosystem simulation models, producing experimental sets that conform to the concepts embedded in the meta-models. Through BioMASS, we provide a practical simulation tool supporting the longterm evolution of populations under strict short-term local-scope interactions among organisms that form the foundation of individual-based models. We anticipate that BioMASS will become established as a complementary tool of choice for ecologists, both for quantitative ecosystems assessment and as a testbed for theoretical analysis related with interacting mechanisms among organisms.

ACCEPTED MANUSCRIPT 2. BioMASS architecture BioMASS generates synthetic marine worlds inhabited by realistic artificial flora and fauna. BioMASS is based on the confluence of IBM and Multi-Agent

PT

System (MAS) disciplines that span biological and computational sciences. BioMASS artificial animals are complex synthetic organisms that have

RI

functional, physiological bodies, perceptual sensors, locomotion actuators,

SC

and brains with a behavioral decision center. Flora is basically sessile plants and drifting plankton present in the environment at a given concentration that

NU

is capable of reproducing at a certain rate. In the case of animals, a single software agent characterizes every organism. In the case of flora, an agent represents a single patch of millions of microscopic plants (rather than a

MA

single microscopic plant), with concentration as the main attribute. Figure 1 presents a schematic of these components as part of the BioMASS architecture. The object Organism in Fig. 1 portrays flora and fauna

D

individuals. It is assembled to integrate all of the properties and functionality of

AC CE P

TE

the objects that it belongs to.

Agent Model Sensing & Decision-making Architecture

Scheduling Agents

Resource

Graphical User Interface Physical Model Time Model

Space Model

Biological Model Physiology, Taxonomy, Behavior, Ecology

Defines

Physical Objects

Population

Belongs

Organism

Environment

Fig. 1. BioMASS simulation framework and architecture. Every organism complies with three conceptual models, simultaneously being a physical object situated in space, a scheduled agent, and a biological individual belonging to a population. The Graphical User Interface is used to adjust the different parameters related to these models. Solid lines represent membership, while dotted lines are “defined by” relationships.

ACCEPTED MANUSCRIPT We chose the MAS paradigm to model and implement IBM, because the abstractions of this approach are particularly adequate to simulate properties and processes at the individual level. Individual physiological processes, life

PT

cycle, decision-making mechanisms, interactions with other individuals and with the environment, and other processes that determine the local state of an

RI

organism are well suited for translation into computerized multi-agent system components. As a model of an individual, an agent is widely adopted in

SC

different disciplines to model and simulate complex adaptive systems. A MAS faces similar challenges as an IBM. A MAS deals with the construction of

NU

complex systems of autonomous interacting entities and their coordination to achieve its design goals. The IBM's requirements of autonomy and individual

MA

uniqueness are achieved through a set of computational attributes for these software agents. Thus, a multi-agent system provides an interesting analogy for simulating ecosystems of interacting organisms.

D

In the BioMASS architecture shown in Fig. 1, every organism is

TE

simultaneously a physical object situated inside a space-time model, an agent scheduled to act at given unpredicted time, and an individual belonging to a

AC CE P

biological population or group of individuals that share some common characteristics. A conceptual model supports and defines each one of these identities: the physical model, the agent model, and the biological model. The basis of this architecture is inspired on the seminal work of complex adaptive systems (CAS) of Holland (1994, 1995). A CAS is a nonlinear system with interesting emergent behavior where its primitive components can change their specification, or evolve, over time. Holland proposed the Echo Model (Holland, 1995), a simulation tool developed to investigate mechanisms which regulate diversity and information-processing in systems comprised of many interacting adaptive agents. As the Echo Model, BioMASS includes important features like resource allocation, heterogeneity and endogenous fitness. It also adds the concepts of location, competition for resources and interactions to model individual-based spatially explicit behavioral ecology on trophic interactions (Schmitz and Booth, 1997). Unlike the Echo model, intended to capture important generic properties of ecological systems, and not necessarily to model any particular ecology in detail, BioMASS is a realistic artificial life model which emphasizes behavioral and physiological processes

ACCEPTED MANUSCRIPT to describe a family of marine ecosystem models. At this moment, BioMASS does not consider evolutionary dynamics, which is built in as a fundamental

PT

component of the Echo Model.

RI

3. The physical model

The physical model, which is used to represent the environment, is based on

SC

a continuous virtual space framework that supports complex sensory and locomotion functions, such as efficient neighborhood exploration inside a

NU

perception range and beyond the immediate surroundings. These functions are of primary importance in a simulation environment where local interactions

MA

among a group of different (in this case, continuously moving) entities are essential, similar to any individual based model. The classical schemes that other simulation tools use to solve these problems

D

include the use of discrete virtual spaces (mostly two-dimensional rectangular

TE

lattice) and algorithms, in which the computational cost per individual increases quadratically with the number of discrete cells of the search area

AC CE P

(Fig. 2). Usually, the number of space cells is much greater than the number of agents; thus, to avoid very costly processing, the search space tends to be reduced to a few surrounding cells or, even, to just four or eight adjacent cells (Von Neumann or Moore neighborhoods).

(a)

Y coordinate

Perception range R

c ∝ π R2 R = perception range c = Computational cost per agent

X coordinate

ACCEPTED MANUSCRIPT (b)

Which individuals are in my perception range? x1

2

x2

y2

3

x3

y3

. . . . .

. . . . . xn

. . . . . yn

c∝n n = number of individuals c= Computational cost per agent

RI

n

y1

PT

1

SC

Fig. 2. Neighborhood search in discrete and continuous spaces. In discrete spaces (on the left) the computational cost per agent increases quadratically with the size of the perception range. To reduce this cost, the number of cells

perception

capabilities)

or

NU

must be kept low either by reducing the range (and losing individual increasing

cell

size

(and

losing

model

MA

representation detail). In continuous spaces (on the right), the computational cost per agent increases linearly with the number of agents independently of

D

the size of the perception range.

TE

BioMASS search functions use specially developed optimized algorithms for continuous spaces, in which the cost per agent grows linearly (Fig. 2) with the

AC CE P

amount of agents, but is retained at a very low level in most situations. A twodimensional virtual space model was chosen to simplify the search and locomotion functions, as well as graphical processing, further. However, the organisms and all the physical objects situated inside the space are threedimensional. In other words, while organisms exhibit three-dimensional properties (such as volume and mass), they may not share the same x and y horizontal coordinates, with one occurring above the other. This situation is similar to a very shallow pond where fishes swim freely around, but cannot pass above one another. BioMASS is only 2D in terms of individual freedom of movement resulting in a reduction in population density capacity measured in organisms per simulation area. However, this disadvantage is not very important compared with the two-fold gain in terms of computing performance of a 2D over a 3D approach. The physical model also includes an innovative scheduling tool capable of different levels of time granularity, which is an important characteristic to obtain long-term emergent population-level properties from short-term

ACCEPTED MANUSCRIPT individual-level processes, such as behavioral interactions. The physical model supports the inclusion of diverse environmental physical objects or resources, such as shelters, which are defined through certain parameters,

PT

such as their position, size, and capacity.

RI

4. The agent model

The selected agent model was chosen from a rich diversity of existing models

SC

in MAS discipline. Neuroscience, psychology, and ethology have inspired computer scientists to design abstract models and build a plethora of agents

NU

with decision-making architecture ranging from simple to intelligent. The simplest capabilities of animals (i.e., the ability to perceive and act within the

MA

environment in a meaningful and purposive manner) may be represented as a series of behaviors, with a behavior being a reaction to a stimulus. This reaction could be a simple reflex reaction on reactive systems or a more

D

elaborated response with the use of intervening abstract representations for

TE

knowledge and time history as in the deliberative or cognitive systems. We adopted what is termed “behavior-based agent architecture” as best suited to

AC CE P

exhibit fish behavior. This architecture is not as limited as reactive systems, which are restricted due to their tightly coupled perception to action mechanisms. A behavior-based system still has the ability to react in a realtime manner, but with the ability to use representations to generate efficient (not only reactive) behavior. Three key factors dictate the life of practically all living-beings; specifically, the need to reproduce, the need to eat, and the need to avoid being eaten (Reebs, 2001). However, these factors are not always compatible. Compromises must be achieved between courting a potential mate and evading enemies, or between the necessity of searching for food and avoiding detection by predators. Despite the balance attained between two desirable, but incompatible, behaviors, conflicts may result when these two or more behaviors are active at the same time. Therefore, an arbitration system is required to resolve conflicts between competing behavioral alternatives and choosing a single behavioral response adequate for a given situation. In BioMASS, these issues are addressed by a decision-making architecture

ACCEPTED MANUSCRIPT built into the agent model, with predation as the underlying causal factor. This architecture shown in Fig. 3 is based on a subsumption method (Brooks, 1991), which is a way of decomposing complicated intelligent behavior into

PT

many simple behavior modules organized as a hierarchy of mutually exclusive

Sensors

Conditions for behavior2?

Activate behavior1

Activate behavior2

yes

Activate behavior3

MA

yes

Conditions for behavior3?

D

Sensors

yes

SC

Conditions for behavior1?

NU

Sensors

RI

layers.

TE

Fig. 3. Agent decision-making architecture. Within this architecture, every individual evaluates the activation conditionals for the available behaviors at

AC CE P

each step of the simulation and from the top to the bottom of the subsumption hierarchy.

This model contains a priority-based coordination function to select a single response according to its perception of the world. This function employs a fixed prioritization network, in which a strict behavioral dominance hierarchy exists, typically through the use of suppression and inhibition in a descending manner. The subsumption architecture present in BioMASS allows the incorporation of a large number of input variables and potential output actions in a mechanism that is agile, that does not require much computing power, and that allows unambiguous decisions to be made. A disadvantage of this method is that it does not model the processes of indecision or doubt, which are clearly present in intelligent organisms, and are the consequence of decision algorithms that use complex cognitive models and information stored in the organism’s memory. However, in our case, this disadvantage is not too relevant, since we modeled organisms of lower intelligence (but which are not

ACCEPTED MANUSCRIPT simply reactive to their environment) that require small amounts of cognitive data, such as a list of possible prey and certain behavior parameters.

PT

5. The biological model In BioMASS, organisms acquire energy in the form of biomass, which, in the

RI

case of animals, is by predating other organisms. Following what is termed the localization principle, organisms (and subsequently populations) are

SC

exclusively affected by other organisms and environmental factors located in the immediate spatial and temporal vicinity (De Angelis and Gross, 1992). As

NU

in nature, the initial flux of energy is injected into the ecosystem through autotrophic organisms that grow spontaneously following a given growth

MA

function to simulate the energy fixing photosynthesis process. As in real life, organisms follow their individual life cycle of birth, growth, reproduction, and death. The life cycle of the population results from the generational

D

succession impelled by reproduction and death. When mobile, organisms

TE

move around the physical space, autonomously interacting with the environment and other organisms.

AC CE P

There is an initial state for every organism and a set of rules for its further development that are grouped into four categories, in which each one has a supporting conceptual model; these are the taxonomical model, the physiological model, the ecological model, and the behavioral model. Each these models achieves a new level of differentiation among organisms in a simulation.

The taxonomical model allows the creation of classes of organisms with different morphological and physiological properties. The physiological model allows two organisms from the same taxonomic class to grow and develop differently. The ecological model supports the relationships network among organisms, in which the behavioral model allows organisms to take different decision paths in response to unique situations in time and space. Another differentiation process relies on a mechanism to create a population distribution for each class of organisms with individuals of different ages and sizes at the start of simulation.

ACCEPTED MANUSCRIPT 5.1 The ecological model The basic unit for the ecological model in BioMASS is the population, which is defined as a set of individuals with similar taxonomical, physiological, and

PT

behavioral characteristics. In BioMASS, the population unit contains a generic description of an individual, like that of the genus and species taxonomic

RI

categories.

SC

BioMASS incorporates the ecological concept of functional group, which represents an aggregate of individuals or populations with similar ethological

NU

(behavioral) and ecological (relational) characteristics. Once the different populations participating in a given simulation are established, and their physiological characteristics have been designed through parameterization, a

MA

trophic net is defined choosing a functional group (an ecological trophic role) for each population. Each functional group is given a list of eligible prey items.

TE

D

5.2 The taxonomical model

This model is supported by a hierarchy of software object classes containing data structures and programming code that implement the functions of the

AC CE P

different organisms. The parent class incorporates all of the common features, while the younger classes implement the differences. At runtime, every organism is created as an instance of one of the lowest (most specialized, but still very generic) classes in the hierarchy, as shown in Fig. 4. Only the most important attributes are “hardcoded” into this hierarchy, with many more differentiating idiosyncrasies being incorporated at runtime via parameters configured using the graphical user interface, and are stored in the data variables. In this way, the ecologist may “build” different types of organisms without any need of programming skills, by just choosing between autotroph and heterotroph and configuring a set of parameters. The profound differences between autotroph and heterotroph organisms, especially those linked to their energetic models, are reflected in the programming code that is used, which is why they require completely separated software classes.

ACCEPTED MANUSCRIPT biomass.model.taxonomy

multiagent.model.agent

Organism

physical.model

Space

PT

Agent

Heterotroph

ReactiveAgent

RI

PhysicalObject

Autotroph

SC

SchedulingObject

NU

Fig. 4. Taxonomical model. The taxonomical model provides the first differentiation mechanism for organisms, because taxonomical position in the hierarchy determines deep morphological and physiological differences

MA

among individuals. More subtle differences are specified at a later time by means of ecological and behavioral property settings. This schematic shows

D

how each organism is also a reactive agent and a physical object situated

TE

inside a given environment.

AC CE P

5.3 The physiological model To fulfill the design goal of BioMASS with respect to ecosystem simulations, every organism is built on a physiological model with biomass flow and conservation at its foundation. However, the system opens at biomass production (primary productivity) and ends at biomass decomposition. To create a completely closed system, it would be necessary to simulate the inorganic part of the carbon natural cycle, including biomass decomposition processes, the storage of inorganic carbon, oxygen, water, and mineral nutrients in the atmosphere and ocean, and the carbon fixing process of photosynthesis. A completely closed system is very complex to model and is unnecessary for most simulation scenarios. At the beginning of the open biomass cycle, autotrophic (biomass producers) organisms are represented as patches characterized by their size and biomass concentration; namely, one software agent per patch. Biomass incorporation is modeled a logistic differential equation (1) based algorithm that makes the biomass concentration of patches increase at a rate that is

ACCEPTED MANUSCRIPT proportional to the biomass concentration itself at a given moment, but respecting a fixed maximum. The concentration might be rationalized as the aggregated biomass of a growing population of plankton organisms contained

PT

within the patch or as the biomass of a growing plant. A maximum is reached because there are limited resources for growing populations in every natural

ௗ௧

=
(1 −

௉(௧) ெ

)()

SC

ௗ௉

RI

system, such as carbon or inorganic nutrients in the case of plants. (1)

NU

In this differential equation, P(t) is the total amount of biomass at time t, k is a proportionality constant, and M is the maximum biomass that the environment

MA

can support.

Heterotrophic organisms are modeled in far more detail. Each heterotroph incorporates biomass through an ingestion-digestion process and, from this

D

start point, lean (or protein) and fat (or lipid) biomasses follow different paths.

TE

Ingested lean biomass is periodically incorporated to the organism’s own lean biomass by an algorithm based on the well-known growth model, often used

AC CE P

in fisheries, proposed by von Bertalanffy, who based his formulation on physiological considerations (Pauly, 1981; von Bertalanffy, 1957, 1960). ௧ = ஶ (1 −  ି௄(௧ି௧బ ) ) (2)

In the model, Lt is the length at time t, L∞ is the asymptotic length - the mean length that fish in a given stock would reach if they were to grow indefinitely, K is the growth rate parameter (or the rate at which L∞ is approached), and t0 is the age (hypothetical) of the fish at zero length, if it had always grown in a manner described by the equation. Starting from the estimated length at given age t, fish biomass may be inferred using the isometric equation, which is also known as the length-weight relationship (Anderson and Neumann, 1996), frequently used in fisheries (3).  = ௕

(3)

ACCEPTED MANUSCRIPT The parameters for the equations may be obtained from 1Fishbase (Froese and Pauly, 2014). Within this model, the only way that an organism may lose lean mass is by being killed and its biomass ingested by other organisms. By

PT

combining the two equations, it is possible to obtain the biomass estimation

D

MA

NU

SC

RI

for a fish at a given age, as shown in Fig. 5.

TE

Fig. 5. Fish biomass versus age. By combining the von Bertalanffy age-length equation and the length-weight relationship, fish biomass may be estimated

AC CE P

from their age.

Fat biomass may be incorporated through a periodically-invoked fattening function. In this process, the organism increases its fat reserves (which are actually an energy repository), as long as fat biomass becomes available through ingested matter and as long as an upper hard limit (specific to the type of organism) is not reached. Fat biomass is disincorporated or consumed by the organism following a Standard Metabolic Rate (SMR), which is the organism’s energy consumption at rest; that is, the energy used for basic life support functions (Ware, 1978; Xiaojun and Ruyung, 1990). This parameter may be adjusted by the modeler via the graphical user interface before a simulation run, and remains constant. Provisions to simulate the effect of environmental temperature over the SMR and other forms of energy 1

Fishbase is the largest and most extensively accessed online database for adult finfish, providing comprehensive species data, including information on taxonomy, geographical distribution, biometrics and morphology, behavior and habitats, ecology, and population dynamics, as well as reproductive, metabolic, and genetic data.

ACCEPTED MANUSCRIPT expenditure (such as those related to movement) will be included on future versions. To simulate the trophic processes that occur inside an ecosystem more

PT

closely, by paying special attention to the energy flow, it was necessary to model the physiological processes of heterotrophs to a highly detailed level,

MA

Fat mass consumed by metabolism

Predator body

D

Lean mass incorporated following growth function

NU

SC

RI

as shown in Fig. 6.

AC CE P

Prey body

TE

DIGESTION

Biomass ingested

ENERGY

Fat mass incorporated to replenish energy reserve

Predator stomach Excess wasted

Adjustable ratio: Lean mass (68% - 98%) Fat mass (2% - 32%)

Fig. 6. The physiology of an individual. The ingested biomass is stored temporarily in the stomach, where is periodically taken by the biomass incorporation functions for growth and fattening. Biomass that is not digested is periodically discarded. Fat biomass is consumed by a metabolic function, while protein and lean mass remain in the organism until it is transferred to other organisms through predation or to the environment in the form of a dead individual or remnants of one. Each heterotroph has a stomach (or provisional repository) that has not yet been incorporated to biomass. The size of the stomach imposes a limit on the amount of biomass that might be captured in one or several ingestion events.

ACCEPTED MANUSCRIPT The biomass is periodically taken from the stomach by growing and fatten processes (described previously), with the remaining biomass being intermittently discarded as waste. Wasted biomass simply evaporates, as the

PT

simulation framework does not contain any decomposition processes at present. Adjustable parameters control the maximum and minimum tolerable

RI

ratio of fat-biomass against total-biomass. When maximum is reached, fatten stops; decaying to the minimum means death by starvation. In our simulation

SC

these parameters were arbitrarily set to 32% and 2% respectively. At the other end of the biomass cycle, once an organism dies, its biomass

NU

remains in the system until it is completely consumed by other organisms. In this way, we follow nature with respect to maximizing biomass utilization, with

MA

the difference being that the decomposition process that closes the system in nature does not actually exist in BioMASS, because decomposition never occurs. Unconsumed death biomass simply remains as physical objects

D

inside the simulation environment.

TE

The BioMASS approach to reproduction is rather simplistic, completely avoiding the roles of sex and genetic inheritance, since there is no genetic

AC CE P

code to share and transmit. Once an individual reaches reproductive maturity, there is a possibility for it to contribute offspring at any time during a reproduction period, with the offspring being recruited to the population as juveniles. In this way, the problems of modeling the complex processes of mating, spawning, and larval stages are completely avoided. The time for reproduction is obtained randomly using a normal distribution centered at the beginning of the reproduction period, allowing sexually mature organisms to reproduce at different moments of the reproduction period (Fig. 7).

Reproduction probability

(a) Reproduction periods

Time

ACCEPTED MANUSCRIPT

t ha

itm en

t

(b)

PT

eggs

th

SC

sexually mature organism

sp a

wn

ow gr

RI

Juvenile

in g

Re Sim pr p o l cy du ifie cl ct d e io n

re cr u

g in ch

Sim Not larvae ula simu ted lat Bio ed b MA y B SS ioM Po AS pu S lat ion

Fig. 7. The reproduction cycle in BioMASS. Once an individual reaches

NU

maturity length, it may reproduce at any time during a reproduction period. The exact moment is determined randomly using a normal distribution (left).

MA

To avoid simulating complex stages of spawning and hatching, the whole reproduction cycle is reduced to a simplified juvenile recruitment phase (right).

D

5.4 The behavioral model

TE

The behaviors presented previously in the agent model are “placeholders” or “generic behaviors” that take part of the decision-making subsumption

AC CE P

architecture. The specific “actions” of those behaviors and their priority are domain-dependent, in this case, fish behaviors. In BioMASS, the specification of those actions is made by the behavioral model. Programmatically, the behaviors in the agent model are abstract classes and the behaviors in the biological model are concrete classes that implement the instructions of those behaviors (Fig. 8). In this way we are able to reuse the architecture independently of the type of organisms or even for other non-biological domains.

ACCEPTED MANUSCRIPT

Agent

Organism

multiagent.model.agent

biomass.model.taxonomy

Heterotroph

multiagent.model.agent

biomass.model.taxonomy

PT

ReactiveAgent

RI

+behaviors 1..*

Carnivore

Behavior

SC

biomass.model.taxonomy

HuntBehavior

NU

multiagent.model.agent

Biomass.model.behavior

MA

Biomass.model.behavior

WanderBehavior

Fig. 8. Decision-making architecture class diagram. In this schematic we can observe how the agent and behavioral models interact. Every biological

TE

Behavior abstract class.

D

behavior, like the hunt described by the HuntBehavior class, inherits from the

AC CE P

The decision-making subsumption architecture might be instantiated with a particular choice of behaviors to model one type of organism. The behavioral model contains an assortment of nine different behaviors at present, from which a subset may be chosen for a given functional group. These selected behaviors pile up inside the agent decision-making subsystem, giving the individuals certain behavioral traits. The selectable behaviors are: drift, escape, explore, forage, hide, hunt, rest, scout, and wander. This comprehensive set of hierarchical behaviors can be extended very easily. This feature is an advantage over similar work regarding decision-making architectures. For instance, Eliassen et al. (2015) determine the behavior of individuals considering only two states "fear" and "hungry", these states are selected in a mutually exclusive manner, depending on the sensed and processed information. As Eliassen et al. (2015), BioMASS architecture is also based on the architectural structures of sensing and information processing, physiological and neurological states, and behavioral control in animals. Unlike them, BioMASS does not consider the evolutionary adaptation

ACCEPTED MANUSCRIPT of behavior, a desirable feature for animal behavior. To ease the simulation design work, BioMASS GUI provides a set of prearranged ecological roles, each with a predefined collection of behaviors.

PT

The definition of these ecological roles and their characteristics originates from experience in the field of marine ecology. In short, it refers to the

RI

number, type, and distribution of functions performed by organisms within an ecosystem (Irschick et al., 2013; Petchey and Gaston, 2006). The current

SC

version contains 12 fixed ecological roles. Table 1 presents these ecological roles and their related behaviors, which are shared depending on hunting

NU

strategy, size, and morphology.

MA

Table 1. BioMASS ecological roles. These roles were defined based on the value and range of organism traits that influence ecosystem functioning,

Top predator

Invertebrate-eating fish/ piscivores

Large-invertebrateeating fish

Territorial smallinvertebrate-eating fish

Planktivorous fish

Herbivorous fish

Predatory invertebrates

D TE

Scavenger invertebrates

Herbivores invertebrates

Plankton

Sessile autotrophs

AC CE P

Behaviors

Ecological Roles Omnivores invertebrates

mainly the trophic level.

Drift Escape Explore Forage Hide Hunt Rest Scout Wander

By choosing an ecological role for each functional group, a set of behaviors are incorporated to the model, so that the organisms play their assigned role correctly. For example, the ecological role of herbivorous fish incorporates the hide, escape, forage, scout, explore, and rest behaviors (Fig. 9a), subsuming each behavior to the others in that order. A brief description of each behavior follows.

ACCEPTED MANUSCRIPT Herbivorous fish

Danger and not hidden and Shelter known

yes

Hide

PT

(a)

No yes

Escape

RI

Danger and shelter not known

yes

Forage

No yes

Scout

MA

Hungry and Shelter known No

yes

Shelter not known

D

Explore

TE

No

yes

Default

Rest

AC CE P (b)

NU

Hungry and Good feeding opportunity

SC

No

Top carnivore

yes

Good feeding Opportunity?

Hunt

No

yes

Default

Wander

Fig. 9. The decision making subsumption architecture might be instantiated with a particular choice of behaviors to model one type of organism. The examples presented in this schematic depict the set of behaviors used to model a reef herbivorous fish (a) and a top carnivore (b). The rather simple drift behavior just computes a random movement vector the

ACCEPTED MANUSCRIPT first time it is invoked. Then, for each step of the simulation, the direction of movement is usually followed at very slow speed. The model was designed to simulate the random drift of small organisms, such as oceanic plankton. The

PT

activation of this model is unconditional, but frequently overridden or subsumed by other behaviors as conditions for their activation are met.

RI

The escape behavior is activated whenever dangerous predators lie within the individual’s sensorial range and a proper shelter to hide in is not available.

SC

The hazard is evaluated by identifying the detected organisms and computing their relative mass with respect to that of the individual. If the size of any of

NU

the organisms is beyond a certain pre-established threshold, this behavior is activated. If active, the individual takes the distance and size of predators into

MA

account to compute and follow an escape vector. The explore behavior is activated when a proper shelter has not been recorded. The objective is to find a proper shelter for the individual to be used

D

in case of danger. To accomplish this objective, the individual wanders around

TE

randomly checking the shelters that lie within its perception range. If one of the shelters is of the proper size (not too small, not too big), then it is recorded

AC CE P

into memory.

The forage behavior is the “eating” behavior for both herbivores and planktivores, and is activated when a qualified source of food is found. The evaluation process takes the hunger of the individual, the nutritional value of the food patch detected, and the distance to the patch into account. If active, the individual moves toward the food patch and, once there, takes a “mouthful” of food, lowering its biomass content. The hide behavior is very similar to the escape behavior. It is activated whenever dangerous predators lie within the individual’s sensorial range and a proper shelter for hiding has been identified. As in the escape behavior, the hazard is evaluated by identifying the detected organisms and computing their relative mass to check if any exceeds the pre-established threshold. Once activated, the distance and size of the predators are taken into account to compute an evasion route; however, in this behavior, if a shelter is known and nearby, the route to it is also considered. If, by the moment the individual tries to enter this shelter, there is not enough room for it, the individual “forgets” this shelter and the behavior is subsumed by the escape behavior.

ACCEPTED MANUSCRIPT The hunt behavior is the “eating” behavior for carnivores and, as the forage behavior, it is activated whenever a qualified prey is found in its immediate surrounding. A response function is used to model the hunter’s “interest” in

PT

the prey as a function of the relative size and the distance between predator and prey. A big prey is good because the predator may capture more biomass

RI

in just one hunt; however, a prey that is too big could be too fast and very difficult to catch, especially if it is far away. This multi-factor inference process

SC

differs from simpler relative-size-based methods used by other similar simulation tools like OSMOSE (Shin and Cury, 2001). Once activated, an

NU

“encounter” vector is computed and followed to reach the prey. If the prey is encountered, the hunter takes a mouthful of biomass (if there is enough) out

MA

of the prey killing it.

This rest behavior is activated whenever the organism detects a proper shelter. Under this behavior, the organism moves at low speed toward the

D

shelter. If it is already there, it tries to enter. If there is no room inside, the

TE

organism forgets this shelter and abandons the behavior. The concept is that the organism does better resting protected inside a shelter when there is

AC CE P

nothing else to do. Other behaviors must override this, if this organism is to survive (i.e., explore, hunt, and feed). The scout behavior is activated when a proper shelter has been recorded and a hunger index is above certain threshold. The objective is to find a proper food source near to the shelter. To accomplish this objective, the individual wanders around randomly scanning the area that lies within a certain range of the shelter.

The wander behavior is activated when a proper shelter has not been recorded and there is no hazard or food source nearby. The organism simply moves following a random vector that is changed periodically. 6. Results To demonstrate the capabilities of BioMASS as a tool for ecologists, we simulated a community with a primary producer (Thalassia seagrass) and two interacting species, an herbivorous fish (Girella nigricans, the opaleye or rudderfish) and a carnivorous fish (Sphyraena lucasana, the barracuda). To

ACCEPTED MANUSCRIPT accelerate the simulation dynamics in this preliminary evaluation, the hunger function response of the population was exaggerated by making individuals eat even when their energy requirements were satisfied. This modification

PT

produced highly voracious organisms. BioMASS simulation models are described using the GUI, adjusting the

RI

parameters listed on Table 2.

SC

Table 2. BioMASS parameters customizable through the graphical user

NU

interface.

BioMASS Parameters Simulation space width.

spaceWidth

Simulation space length.

spaceDepth

Simulation space depth.

timeStepSecs

Simulation time for every simulation time step.

Metabolism

Units cm cm cm S

How many simulations steps for a new text record to be appended to output files.

chartSteps

How many simulations steps for a new point to be added to output charts.

shelterArea

Portion of the simulation area covered by shelters.

shelterMnRad

Minimum radius for circle-shaped shelters.

cm

shelterMxRad

Maximum radius for circle-shaped shelters.

cm

shelterDispersion

Standard deviation for normal distribution of shelters around the center of the simulation area.

maxFatFraction optFatFraction stomachCapacity mouthCapacity SMR

linf

TE

D

recordSteps

lifeSpan

Growth

MA

spaceLength

minFatFraction

t0

steps steps 0.0 - 1.0

cm

Minimum proportion of fat mass with respect to total biomass.

0.0 - 1.0

Maximum allowable proportion of fat mass with respect to total biomass.

0.0 - 1.0

Optimal proportion of fat mass with respect to total biomass.

0.0 - 1.0

Stomach capacity relative to total body size.

0.0 - 1.0

Mouth capacity relative to total body size.

0.0 - 1.0

Standard metabolic rate. Proportion of fat mass with respect to optimal biomass consumed daily.

Organism longevity

yr

݈ஶ , von Bertalanffy equation fish length at infinity age.

cm

‫ݐ‬଴ , von Bertalanffy zero-length time parameter.

yr -

a

Constant for the von Bertalanffy length to weight equation.

b

Constant for the von Bertalanffy length to weight equation.

k

Von Bertalanffy growth coefficient

yr-1

firstMaturityLength

Minimum length for reproducible organisms.

cm

recruitment

Number of new juvenile organisms recruited per each reproducing organism.

recruitmentLength

Length of individuals at recruitment time.

Autotro phs

Reproduction

Definition

AC CE P

Simulation Environment

Parameter

maxBioMassConcentration

Maximum autotroph concentration per patch.

-

individuals cm

g cm-3

patchRadius

Autotroph patch radius.

cm

N0

Youngest class size.

Z

Instant mortality rate constant.

minSpeed

Minimum individual speed given in number of body lengths per second.

maxSpeed

Maximum individual speed given in number of body lengths per second.

s-1

driftSpeed

Drifting organisms peed given in number of body lengths per second zero in the case of sessile autotrophs.

s-1

escapeSpeed

Speed of an escaping organism given in number of body lengths per second, by default escapeSpeed = maxSpeed.

s-1

exploreSpeed

Speed of an organism searching for a shelter given in number of body lengths per second, by default exploreSpeed = minSpeed.

s-1

changeDirProbability

Probability of direction change at every time step for explore, wander and scout behaviors.

forageSpeed

Speed of an herbivore rushing to a food source given in number of body lengths per second, by default forageSpeed=maxSpeed

s-1

hideSpeed

Speed of an organism rushing to hide inside a shelter given in number of body lengths per second, by default hideSpeed = maxSpeed.

s-1

huntSpeed

Speed of an organism rushing to devour a prey given in number of body lengths per second, by default huntSpeed = maxSpeed. The big hunter is by default faster than a smaller escaping or hiding prey.

s-1

restSpeed

A well-fed organism inside a shelter rests given in number of body lengths per second. By default restSpeed=0.

s-1

scoutSpeed

Speed of an herbivore scouting for food around its shelter given in number of body lengths per second, by default scoutSpeed = minSpeed.

s

TE

D

MA

NU

SC

RI

PT

individuals

maxDistFromShelter wanderSpeed

AC CE P

Behaviors

Population

ACCEPTED MANUSCRIPT

Maximum distance that an herbivore scouts away from its shelter. Usually maxDistFromShelter=perceptionRange.

Speed of an organism wandering around given in number of body lengths per second, by default wanderSpeed = minSpeed.

In our case, three functional groups were created based on the ecological traits present in three out of the 12 available predefined ecological roles: Sessile autotrophs, Herbivorous fish, and Top predator. A list of potential prey was established for each of the two heterotroph functional groups, with other behavior-related settings also being adjusted. An initial population distribution was then created for each functional group, providing the number of individuals in the lower-age class and a mortality rate. In addition, some anatomical and physiological population-wide parameters were set with values that were extracted from the Fishbase database and other scientific sources. The simulation scenario was initialized adjusting the 2D physical space, specified in centimeters, to a size of 57,600 by 36,000. A maximum

yr-1

s-1

0.0 – 1.0

-1

body lengths s-1

ACCEPTED MANUSCRIPT percentage of the simulation area to be covered by shelters was provided, along with the minimum and maximum size. The refuges were randomly scattered following a normal radial distribution concentrically to the simulation

PT

space, with a dispersion factor also being introduced through the GUI. Regarding the scheduling engine and exploiting its capability to sustain

RI

several simultaneous time granularities, two loops were specified: (1) a 3600steps loop with 1 time unit per step (1 hour executed second by second) and

SC

(2) a fast-forward loop with 23 steps of 3600 time units each (23 hours executed hourly). The first, finer-granularity shorter-duration loop was

NU

intended to support behavior-driven local interactions among organisms, while the coarser-granularity longer-duration loop supported physiological functions

MA

and allowed emergent effects on the simulated populations to be detected. At runtime, BioMASS displays a graphical representation of the simulated arrangement using simple geometrical shapes, such as circles and squares of

D

different colors for the different objects, individuals and behaviors. This

simulation process.

TE

graphical representation provides an important visual aid to follow the

AC CE P

Figure 10 shows the age-class histograms for a simulated population of Girella-nigricans herbivorous fishes. The charts compare the population age distribution at the start (Fig. 10a) and end (Fig. 10b) of 12,000 model-hours (approx. 1.4 model-years) simulation, using individual (Fig. 10a) and biomass (Fig. 10b) totals per age class. Proof of an aging process is the presence of an 11th age class (the oldest) at the end of the model run, which was not present at the start. The grand totals shown at the upper right corner on the two charts also demonstrate population growth, in terms of individual counts and cumulative biomass.

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 10. Histograms showing individual and biomass totals for a herbivorous

AC CE P

population of a given simulation scenario. The left slide depicts the number of organisms and the right slide the biomass, both grouped by age class. Both slides present the initial distribution (left bar of each class) and the distribution after the end of simulation (right bar of each class). Figure 11 shows a comparison between individual-averaged biomass per age class histogram for two populations participating in the same simulation. The left slide presents a Chopa Verde herbivorous population (Fig. 11a), while the right side presents a Barracuda population (Fig. 11b) as their predators. Of note, some age classes were absent in the carnivorous population, possibly due to starvation, as there were no prey items for this species in the corresponding Chopa Verde simulation.

10

15

20

TE

5

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

25

30

AC CE P

Fig. 11. Comparison of the evolution of prey and predator biomass. The left slide represents the individual-averaged herbivore biomass grouped by age class, while the right slide represents the corresponding biomass of their carnivorous predators. Both slides represent the initial distribution (blue left bar of each class) and distribution after the completion of the simulation (green right bar of each class). Figure 12 show the biomass evolution for the same two herbivorous and carnivorous fish populations over 12,000 model-hours of simulation. The live biomass is the result of adding the lean and fat biomasses, while the dead biomass derives from the sum of killed and starved biomasses. Of note, the right-hand slide shows the null killed biomass for a population without predators (Fig. 12b). Starting from the 1000th model-hour, this slide presents a clear starvation process, which corresponds to a decline in fat biomass for the same population. The starved biomass line (in red) is hidden by the similar dead biomass line (in magenta), since starvation is the only cause of mortality for the top-predator. This decline parallels the decline in total biomass for the

ACCEPTED MANUSCRIPT prey population (Fig. 12a), which presents three events of rapid decline at the 1000th, 6000th and 10500th model-hours. The first one corresponds with a hunt increment of the top-predator while the last two can only be explained by

PT

a decline in biomass fat/total ratio leading to starvation, since there are not

AC CE P

TE

D

MA

NU

SC

RI

corresponding killed biomass increments (Fig. 12b) at those two moments.

Fig. 12. Population biomass time series. Extract of two populations from an experiment run for 12,000 simulation hours. The charts illustrate the live biomass (subdivided in lean and fat biomass) and the dead biomass (subdivided into “killed” and “starved” biomass according to the cause of death). Figure 13 shows the evolution over model time of total biomass (lean plus fat biomass) for the different age classes available for the two herbivore (Fig. 13a) and carnivore (Fig. 13b) prey-predator populations. These charts show a clear decline in younger and smaller classes of herbivores, which correspond with a boom and decline of a newly recruited young carnivore class. The older, bigger herbivores keep accumulating biomass after the decline of the

ACCEPTED MANUSCRIPT youngest predators, showing the start of an equilibrium forming between both

AC CE P

TE

D

MA

NU

SC

RI

PT

populations.

Fig. 13. Population biomass time series by age class. Extract of two populations from an experiment run for 12,000 simulation hours. The charts illustrate the live biomass reported by age class. Figure 14 shows the ratio of fat-biomass against total-biomass for the prey and predator populations, grouped by age class. This ratio may be seen as a health measure for the organisms, given that too-low fat reserve lead to death by starvation. We are able to confirm the starvation events already discussed above (Fig. 13a) due to a reduction of the biomass fat/total ratio visible at the 1000th, 6000th, 10500th model-hours for herbivores (Fig. 14a) and at 1000th model-hours for the carnivores (Fig. 13b).

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 14. Time series for population fat/total biomass ratio. Extract of two

AC CE P

populations from an experiment run for 12,000 simulation hours. The slides illustrate the biomass fat/total ratio as a measure of health for the different age classes.

The recorded data show how community stability might emerge from individual taxonomic, physiological, behavioral, and ontogenetic variability. 7. Discussion Here, we developed a model framework with a new physical environment and scheduler, because existing simulation frameworks are not able to support long-term/large-scale with high granularity. Such support, combined with agent and biological models, in addition to a graphical user interface (GUI), forms the basis of the BioMASS simulation system, which is implemented as a Java library (Java SE). To obtain variability in long-term populations, while incorporating individual short-term local interactions, it was highly important that the embedded-loop

ACCEPTED MANUSCRIPT scheduling mechanism implemented in BioMASS had the capability to call iteratively different stepping loops, each with a different time step. Within this scheme, the model was able to simulate, in every outer cycle, some loops at

PT

the highest resolution and other loops with a lower, computationally cheaper resolution. Concealed in the design, the individuals know when to execute the

RI

tasks related with fine or coarse granularity; that is, behavioral activities are executed for very short steps only (1 time unit), whereas metabolic, growth,

SC

and reproductive activities occur in any step size. While the scheduler is fully customizable, care must be taken because the absence of loops at full

NU

resolution yields a useless simulation without behaviors and, thus, without interactions among organisms (and ending with no long-term effects).

MA

At its current state of development, the selection of behaviors related with preset ecological traits in the system cannot be modified, because selecting behaviors arbitrarily could build individuals with contradictory behavior. To

D

remove this limitation and allow more experimental freedom, it is necessary to

TE

establish certain combining rules and enforce them through programming code that will be included in future versions of the system. However, different

AC CE P

settings affect the available behaviors. Although the behaviors have default values, it would be useful to experiment with them and observe how these individual behavioral properties may affect long term emergent dynamics, structure, or patterns at the aggregated level. The setting values are generally relative to the size of individuals of any age. For example, a max speed ratio of 5.0 establishes that the individuals of this functional group move at a maximum speed of five times their individual length per second. As an automatic effect, bigger individuals are faster than smaller ones from the same functional group. The hazard size factor represents the minimum relative size of a detected predator with respect to that of the individual to qualify it as dangerous. The populations are built using an age distribution equation, such as (4): ௜ = ଴  ି௠(௜௱௧)

(4)

where Ni is the number of individuals in class i, N0 is the number of organisms in the initial class or cohort size, m is the mortality rate from class i to class

ACCEPTED MANUSCRIPT i+1, and t is the time lapse between two consecutive generations. Although not a completely objective measure, when correctly parameterized, the individuals in a BioMASS simulation evolve following their behaviors in a

hunting,

hiding,

and

feeding.

However,

this

PT

way that resembles a living system. It is possible to view the organisms qualitative

important

RI

representation is not enough to know what is happening at the population level. For this reason, the runtime graphical display is complemented with

SC

informative charts about biomass evolution for each simulated population. The runtime that charts functions is very important for monitoring reasons, and

NU

was kept to a minimum to save the processing power required to support simulation scenarios with 1000s or even 10s of 1000s of agents and objects.

MA

Following the need to save processing power, BioMASS reports very detailed experimental data via a set of output text files that are updated by a reporting module, which records detailed information for every simulated population,

D

such as their growth parameters, the initial age distribution, and development

TE

over time. A shelters usage statistics file is also available. The frequencies of runtime charting and the output file updates are set in the GUI. The output

AC CE P

files are arranged in a way that it is straightforward for importation to most worksheet

or

data-analysis

programs

to

produce

detailed

graphical

representations. In our case, to simplify the analysis of these files, a set of SciLab (Scilab, 2015) scripts was written, which produced the graphical charts previously shown.

Compared with other IBM models (like PISCATOR (van Nes et al., 2002) and OSMOSE (Shin and Cury, 2001)), BioMASS is distinguished by its capacity to focus on small ecosystems and the possibility to model the impact of human activity through the disruption of the physical environment and competition for its use. Other IBM models mainly focus on ecosystems that have large populations of organisms deployed in large geographical areas, and must follow the concept of super-individuals (Scheffer et al., 1995) to support large quantities of organisms. A super-individual is a single entity that is represented by an aggregate of organisms. An excellent example is that of Rose et al. (2015), an end-to-end model consisting of four coupled submodels: hydrodynamics, Eulerian nutrient–phytoplankton–zooplankton, a super-individual-based fish submodel, and an agent-based fishing fleet

ACCEPTED MANUSCRIPT submodel. Being useful to solve computational complexity, the superindividual approach leads obviously to a loss of individuality. In contrast, in BioMASS, every individual is represented independently, even when they

PT

belong to large groups, schools, or populations. This representation was possible because of the advanced scheduling mechanism and spatial model

RI

supporting the simulation framework. Nevertheless, the concept of superindividuals is useful in certain situations as demonstrated by Rose et al.

SC

(2015), in the particular case of BioMASS it is used to represent aggregates of immobile or drifting (unwilled behavior) organisms, specifically sessile plants

NU

or plankton patches. The ability to discriminate among individuals in our system permits simulations that place more focus on individual behaviors and

MA

how they contribute to inter-individual interactions.

Table 3. BioMASS modeling and simulation capabilities compared to the three

D

most cited general-purpose simulation tools and two related domain-specific

TE

simulation models. The general-purpose simulation tools require extensive programming skills in order to build all the functions and processes supporting

AC CE P

the IBM approach. The domain-specific simulation models incorporate all the IBM support logic but true individuality is lost since they make use of the super-individual concept. Criteria

Simulation Tools Netlogo BioMASS

Repast

Mason

yes

yes

yes

yes

yes

no

Bioenergetic model

programming required

programming required

programming required

yes

yes

yes

Behavioral model

programming required

programming required

programming required

yes

not detail at individual level

not detail at individual level

yes

yes

Spatially situated agents

Large scale time and space support Fine resolution time and space support

not simultaneously not simultaneously

no yes

yes

OSMOSE

PISCATOR

no

no

programming required

programming required

programming required

yes

super-individual

super-individual

Time-event scheduling 2D and 3D Spaces

yes

yes

yes

yes

sequential loops

sequential loops

yes

yes

yes

2D

no

no

Charting

yes

yes

yes

yes

yes

yes

Individual-Based

Coastal ecosystems, especially those located in tropical regions with tourist activity, are characterized by a high diversity of species in populations with small numbers of individuals that are in close contact with the substrate. Therefore, humans primarily impact these systems by altering the physical space (reduction, pollution, and destruction), rather than by fishing. In this

ACCEPTED MANUSCRIPT respect, BioMASS resembles PISCATOR and OSMOSE, and even EcoPath (a non-IBM system), in its capacity to incorporate the effect of human activity. Unlike EcoPath and PISCATOR, our system, along with OSMOSE, models

PT

the effect of human activity, beyond capture, through alteration of the physical space.

RI

However, the ecosystem may be impacted in more subtle ways, like altering the normal behavior of organisms by invading their space with entities that are

SC

perceived as threats (boats, swimmers, etc.). This process is most common in touristic areas where fishing is prohibited and construction activities are

NU

heavily regulated; yet, the degradation process of marine ecosystems remains evident. Thus, it is important to focus on individual behavior. In other words,

MA

predation and modification of the physical environment are not the only ways to disturb an ecosystem. For instance, it is possible to alter the behavior of organisms by driving them away from their habitat.

D

It would be easy to incorporate in BioMASS the effect of human activity by

TE

means of human agents characterized to affect the physical environment (destroying shelters), to capture individuals of one or some of the simulated

AC CE P

fish populations or just affecting the fish behavior merely by their presence. For the accurate simulation of population dynamics in end-to-end modeling, feedback between low-trophic and high-trophic levels is necessary (Rose et al., 2001). In BioMASS feedback is naturally performed since all trophic levels are executed simultaneously. High-trophic level fish consumption affects lowtrophic level organisms like plants or plankton concentration, which then has a density-dependent effect on fish growth. Similarly low-level physical objects as shelters distribution and sizes affect high-level organisms behaviors. Several lines of work are envisaged from the results of this study. First, several experiments need to be designed with marine ecologists to validate BioMASS biological models against actual scenarios. Although these models were designed under direct supervision by a domain expert, community validation by other scientists is desirable. Second, computationally, it is very difficult to run simulations with a large numbers of individuals, which is why many models draw on the concept of the super-individual. We addressed this issue with an innovative concept to represent the physical space with detection and movement operations that save processing time and memory;

ACCEPTED MANUSCRIPT however, computer resources are still limited. Ideally, this model could be distributed in several computing devices to leverage its resources. This development of BioMASS is currently in progress. Finally, we intend to extend

PT

the BioMASS framework to consider genetic diversity, through reproduction mechanisms, and cultural diversity, which requires cognitive processes such

RI

as learning and memorizing.

In summary, BioMASS individual-based models are able to test detailed

SC

specific hypotheses about fish behaviors and quantify how these behaviors impact population dynamics. A potential number of experiments could be

NU

designed to explain or substantiate qualitative population hypotheses related to fish interactions and/or physiology, such as predator preferences (prey type

MA

or size, diet change), competition, and the impacts of environmental change. BioMASS is a detailed modeling system combined with a graphical experimentation data tool, which provide a unique way of testing and

D

understanding the mechanism that drive populations, and may assist

TE

ecologists in biological assessments and with respect to making objective

AC CE P

management decisions on threatened or commercial stock.

8. Conclusions

Most popular simulation frameworks used in ecological research use the individual-based modeling paradigm and are intended for wide audiences and diverse applications; consequently, these models have costly overheads that limit their capacity. BioMASS exhibits modeling capabilities that are not available within those frameworks. In particular, BioMASS (1) has the ability to support short-term local-environment dynamics that occur around each individual, (2) is nested inside a long-term broad-scope simulation, and (3) has ability to optimize detection and movement operations in extensive simulated areas with vast numbers of organisms. These abilities are fundamental characteristics for modeling a living ecosystem that has multiyear interactions among several fish species. These capacities, which are one of the main contributions of this work, are supported by the novel physical model proposed in this study, and are implemented as a set of programming

ACCEPTED MANUSCRIPT libraries that facilitate its use in other simulation systems, not necessarily biology-oriented.

PT

Acknowledgments The Mexican Ministry of Education supported this research with grant

RI

PROMEP/103.5/12/7647.

SC

References

Anderson, R.O., Neumann, R.M., 1996. Length, weight, and associated

NU

structural indices, in: Murphy, B.R., Willis, D.W. (eds.), Fisheries techniques, Second ed. American Fisheries Society, Bethesda, MD, pp.

MA

447–481.

Bousquet, F., Le Page, C., 2004. Multi-agent simulations and ecosystem management:

a

review.

Ecol.

Model.

176,

313-332.

doi:

D

http://dx.doi.org/10.1016/j.ecolmodel.2004.01.011

TE

Breckling, B., Middelhoff, U., Reuter, H., 2006. Individual-based models as tools for ecological theory and application: Understanding the emergence

AC CE P

of organizational properties in ecological systems. Ecol. Model. 194, 102113.

Brooks, R.A., 1991. Intelligence without representation. Artif. Intell. 47, 139159.

Buchmann, C.M., Schurr, F.M., Nathan, R., Jeltsch, F., 2013. Habitat loss and fragmentation affecting mammal and bird communities—The role of interspecific competition and individual space use. Ecol. Inform. 14, 90-98. doi: http://dx.doi.org/10.1016/j.ecoinf.2012.11.015 Daewel, U.T.E., Peck, M.A., Kühn, W., St. John, M.A., Alekseeva, I., Schrum, C., 2008. Coupling ecosystem and individual-based models to simulate the influence of environmental variability on potential growth and survival of larval sprat (Sprattus sprattus L.) in the North Sea. Fish. Oceanogr. 17, 333-351. doi: http://dx.doi.org/10.1111/j.1365-2419.2008.00482.x DeAngelis, D.L., Gross, L.J., 1992. Individual-based models and approaches in ecology: populations, communities, and ecosystems. Chapman & Hall. DeAngelis, D.L., Mooij, W.M., 2005. Individual-based modeling of ecological

ACCEPTED MANUSCRIPT and evolutionary processes. Annu. Rev. Ecol., Evol. Syst. 36, 147. Eliassen, S., Andersen, B.S., Jørgensen, C., Giske, J., 2015. From sensing to emergent adaptations: Modelling the proximate architecture for decision-

PT

making. Ecol. Model. doi: http://dx.doi.org/10.1016/j.ecolmodel.2015.09.001 Froese, R., Pauly, D. (2014). FishBase. Retrieved February 19, 2015, from

RI

http://www.fishbase.org

Fulton, E.A., 2010. Approaches to end-to-end ecosystem models. J. Mar.

SC

Syst. 81, 171-183. doi: http://dx.doi.org/10.1016/j.jmarsys.2009.12.012 Grimm, V., 1999. Ten years of individual-based modelling in ecology: what

NU

have we learned and what could we learn in the future? Ecol. Model. 115, 129-148.

Princeton university press.

MA

Grimm, V., Railsback, S.F., 2005. Individual-based modeling and ecology.

Hemelrijk, C.K., Kunz, H., 2005. Density distribution and size sorting in fish

D

schools: an individual-based model. Behav. Ecol. 16, 178-187. doi:

TE

http://dx.doi.org/10.1093/beheco/arh149 Hogeweg, P., Hesper, B., 1990. Individual-oriented modelling in ecology.

AC CE P

Mathematical and Computer Modelling 13, 83-90. Holland, J.H., 1994. Echoing emergence: Objectives, rough definitions, and speculations for echo-class models, in: Cowan, G.A., Pines, D., Meltzer, D. (eds.), Complexity: Metaphors, Models, and Reality. Addison-Wesley, Reading, MA, pp. 309-342. Holland, J.H., 1995. Hidden order: How adaptation builds complexity. Addison-Wesley, Reading, MA. Huse, G., Giske, J., Salvanes, A.G.V., 2008. Individual-based models. Handbook of Fish Biology and Fisheries 2, 228-248. Huston, M., DeAngelis, D., Post, W., 1988. New computer models unify ecological theory. Bioscience 38, 682-691. Irschick, D.J., Fox, C., Thompson, K., Knapp, A., Baker, L., Meyer, J., 2013. Functional ecology: integrative research in the modern age of ecology. Funct. Ecol. 27, 1-4. doi: http://dx.doi.org/10.1111/1365-2435.12037 Mason. (2015). Multi-agent Simulator of Neighborhoods. Retrieved July 12, 2015, from http://cs.gmu.edu/~eclab/projects/mason/ McCauley, E., Wilson, W.G., Roos, A.M.d., 1993. Dynamics of Age-Structured

ACCEPTED MANUSCRIPT and Spatially Structured Predator-Prey Interactions: Individual-Based Models and Population-Level Formulations. Am. Nat. 142, 412-442. doi: http://dx.doi.org/10.2307/2462651

PT

McDermot, D., Rose, K.A., 2000. An individual-based model of lake fish communities: application to piscivore stocking in Lake Mendota. Ecol.

RI

Model. 125, 67-102. doi: http://dx.doi.org/10.1016/S0304-3800(99)00172-6 Netlogo. (2015). Multi-agent programmable modeling environment. Retrieved

SC

July 12, 2015, from http://ccl.northwestern.edu/netlogo/

Parrott, L., Kok, R., 2002. A generic, individual-based approach to modelling

NU

higher trophic levels in simulation of terrestrial ecosystems. Ecol. Model. 154, 151-178. doi: http://dx.doi.org/10.1016/S0304-3800(02)00065-0

MA

Pauly, D., 1981. The relationship between gill surface area and growth performance in fish: a generalization of von Bertalanffy’s theory of growth. Meeresforschung 28, 251-282.

D

Petchey, O.L., Gaston, K.J., 2006. Functional diversity: back to basics and

TE

looking forward. Ecol. Lett. 9, 741-758. doi: http://dx.doi.org/10.1111/j.14610248.2006.00924.x

AC CE P

Plagányi, É.E., 2007. Models for an ecosystem approach to fisheries. FAO Fish. Tech. Pap. 477.

Reebs, S.G., 2001. Fish behavior in the aquarium and in the wild. Cornell University Press, Ithaca. Repast. (2015). Recursive porous agent simulation toolkit. Retrieved July 12, 2015, from http://repast.sourceforge.net Rose, K.A., Allen, J.I., Artioli, Y., Barange, M., Blackford, J., Carlotti, F., Cropp, R., Daewel, U., Edwards, K., Flynn, K., Hill, S.L., HilleRisLambers, R., Huse, G., Mackinson, S., Megrey, B., Moll, A., Rivkin, R., Salihoglu, B., Schrum, C., Shannon, L., Shin, Y.-J., Smith, S.L., Smith, C., Solidoro, C., St. John, M., Zhou, M., 2010. End-To-End Models for the Analysis of Marine Ecosystems: Challenges, Issues, and Next Steps. Mar. Coast. Fish. 2, 115-130. doi: http://dx.doi.org/10.1577/C09-059.1 Rose, K.A., Cowan, J.H., Winemiller, K.O., Myers, R.A., Hilborn, R., 2001. Compensatory density dependence in fish populations: Importance, controversy, understanding, and prognosis. Fish Fish. 2, 293-327. Rose, K.A., Fiechter, J., Curchitser, E.N., Hedstrom, K., Bernal, M.,

ACCEPTED MANUSCRIPT Creekmore, S., Haynie, A., Ito, S.-i., Lluch-Cota, S., Megrey, B.A., Edwards, C.A., Checkley, D., Koslow, T., McClatchie, S., Werner, F., MacCall, A., Agostini, V., 2015. Demonstration of a fully-coupled end-to-

Current.

Prog.

PT

end model for small pelagic fish using sardine and anchovy in the California Oceanogr.

RI

http://dx.doi.org/10.1016/j.pocean.2015.01.012

doi:

Scheffer, M., Baveco, J.M., DeAngelis, D.L., Rose, K.A., van Nes, E.H., 1995.

SC

Super-individuals: a simple solution for modelling large populations on an individual basis. Ecol. Model. 80, 161.

NU

Schmitz, O.J., Booth, G., 1997. Modelling food web complexity: the consequences of individual-based, spatially explicit behavioural ecology on

MA

trophic interactions. Evol. Ecol. 11, 379-398.

Scilab. (2015). Open source software for numerical computation. Retrieved February 19, 2015, from http://www.scilab.org

D

Shin, Y.-J., Cury, P., 2001. Exploring fish community dynamics through size-

TE

dependent trophic interactions using a spatialized individual-based model. Aquat. Living Resour. 14, 65-80. doi: http://dx.doi.org/10.1016/S0990-

AC CE P

7440(01)01106-8

Travers, M., Shin, Y.J., Jennings, S., Cury, P., 2007. Towards end-to-end models for investigating the effects of climate and fishing in marine ecosystems. Prog. Oceanogr. 75, 751. Uchmański, J., Grimm, V., 1996. Individual-based modelling in ecology: what makes

the

difference?.

Trends

Ecol.

Evol.

11,

437-441.

doi:

http://dx.doi.org/10.1016/0169-5347(96)20091-6 van Nes, E.H., Lammens, E.H.R.R., Scheffer, M., 2002. PISCATOR, an individual-based model to analyze the dynamics of lake fish communities. Ecol.

Model.

152,

261-278.

doi:

http://dx.doi.org/10.1016/S0304-

3800(02)00005-4 von Bertalanffy, L., 1957. Quantitative laws in metabolism and growth. Q. Rev. Biol. 32, 217-231. doi: http://dx.doi.org/10.2307/2815257 von Bertalanffy, L., 1960. Principles and theory of growth, in: Nowinski, W.W. (ed.), Fundamental aspects of normal and malignant growth. Elsevier, Amsterdam, pp. 137-259. Ware, D., 1978. Bioenergetics of pelagic fish: theoretical change in swimming

ACCEPTED MANUSCRIPT speed and ration with body size. Journal of the Fisheries Board of Canada 35, 220-228. Xiaojun, X., Ruyung, S., 1990. The bioenergetics of the southern catfish

AC CE P

TE

D

MA

NU

SC

RI

weight and temperature. Physiol. Zool., 1181-1195.

PT

(Silurus meridionalis Chen). I. Resting metabolic rate as a function of body

ACCEPTED MANUSCRIPT Highlights A novel modeling approach for the “End-to-End” analysis of marine ecosystems

AC CE P

TE

D

MA

NU

SC

RI

PT

• First modeling and simulation tool in the field of IBM capable of handling thousands of spatially situated mobile agents with rich sets of detailed behavioral and bioenergetics functional features and complex neighborhoods interactions. • A modeling and simulation tool situated half way between the general-purpose simulation toolkits and domain-specific models. • A practical simulation tool supporting the long-term evolution of populations under strict short-term local-scope interactions among organisms.