The Psychopathology of Anxiety and Some Other Syndromes

**81**

**Chapter 6**

**Abstract**

*Hülya Kök Eren*

Anxiety Disorders

Anxiety disorders are the most common of all psychiatric illnesses and result in significant functional impairment and distress. In DSM 5, anxiety disorders are divided into eleven subgroups. Anxiety, which we consider normal (mild and moderate anxiety), plays an important role in the development of the individual. However, if the level of anxiety increases, it may lead to mental problems. A high level of anxiety, long duration, and intensification of anxiety symptoms may cause anxiety disorders. These are; separation anxiety disorder, selective mutism, panic disorder, agoraphobia, specific phobia, social anxiety disorder, generalized anxiety disorder, other unspecified anxiety disorder, anxiety disorder related to another medical condition, anxiety disorder caused by substance or medication, anxiety disorders caused by substances and medication are unspecified anxiety disorders. Treatment methods used in anxiety disorders are individual psychotherapy, cognitive therapy, behavioral

therapy, systematic sensitization, exposure, and psychopharmacology.

generalized anxiety disorder, selective mutism

the contrary, serves as a hindering function [1].

**1. Introduction to anxiety disorders**

**1.1 Anxiety concept**

**1.2 Anxiety and fear**

**Keywords:** anxiety disorders, panic disorder, phobia, social anxiety disorder,

Anxiety is a feeling that individuals experience at different levels throughout their lives. Anxiety is a healthy, normal response to perceived threats or unique experiences, and it is a necessary motivating force for survival. Mild anxiety is a driving force necessary for adaptation and advancement to the higher stage of spiritual development. However, when the anxiety level increases too much, it, on

Anxiety and fear are emotions that people are usually confused about. Fear is a response to a known threat. However, anxiety can be defined as fear, tension, uneasiness, or restlessness expected from an unidentified or unknown source. We can evaluate anxiety as a cognitive state of fear [2]. Physical symptoms of anxiety include palpitations, difficulty breathing, rapid breathing, tremors in the hands and feet, and excessive sweating. Psychological symptoms, on the other hand, include distress, excitement, sudden feeling, and fear that something terrible will happen [3]. Anxiety, which we consider normal (mild and moderate anxiety), plays an important role in the development of the individual. However, if the level of anxiety increases, it may lead to mental problems. A high level of anxiety, long duration,


**4. Gamma sterilization of human tissue grafts**

Connective tissue allografts, such as bone, cartilage, tendons, ligaments, dura mater, skin, amnion, pericardium, heart valves and corneas, are widely used for reconstructive surgery in many clinical disciplines, including orthopaedics, traumatology, neurosurgery, cardiosurgery, plastic surgery, laryngology and ophthalmology. The grafts are prepared by specialized laboratories called 'tissue banks'. The risk of infectious disease transmission with tissue allografts is a major concern in tissue banking practice.

Microorganisms can be introduced into grafts during tissue procurement, processing, preservation and storage, but even if all these procedures are done under aseptic conditions, the possibility of bacterial, fungal and viral disease transmission of donor origin cannot be excluded. Bacterial, including tuberculosis, fungal, and viral infections, such as human immunodeficiency virus (HIV), hepatitis B and C (HBV, HCV), cytomegalo virus (CMV), as well as rabies and prion diseases, have been transmitted by tissue allografts. Thus, radiation sterilization of tissue grafts has been implemented in some tissue banks, and a dose of 25 kGy has been used in many of these tissue banks. The advantage of radiation sterilization is that it allows the processing of grafts, which have been previously sealed or tightly closed in special wrappings. Such procedures prevent any accidental recontamination during packing.

The problem is additionally complicated by the possible presence, in human tissues, of pathogenic viruses, such as the human immunodeficiency virus (HIV) (Daar et al, 1991), hepatitis viruses (HBV, HCV) (Conrad et al, 1995), cytomegalovirus or others. Data concerning the sensitivity of these viruses to ionizing radiation are scarce. This is mainly due to the fact that there are no suitable tests to study their inactivation, no appropriate animal models exist and no suitable method of in vitro culture of highly differentiated target cells (e.g. hepatocytes) for these viruses has yet been developed.

The wide range of D10 values (4–8.3 kGy) determined for HIV and other viruses might be due to the influence of environmental conditions. Many factors can modify the sensitivity of pathogens microorganisms to ionizing radiation, including the temperature of irradiation. For example, the reduction of HIV virus was achieved with a dose of 50–100 kGy in frozen plasma (–80oC), and with 25 kGy at 15oC (Hiemstra et al, 1991). The D10 value for HIV-1 irradiated at room temperature was 7.2 kGy, and 8.3 kGy at –80oC (Hernigou et al, 2000). The presence or absence of water and oxygen, and presence of radiation protectors are also factors can modify the sensibility of pathogens microorganisms. In the absence of water (for example, in dry air or lyophilized grafts) the resistance of pathogens increases. On the other hand, in the presence of water, an indirect effect of ionizing radiation predominates and the sensitivity of microorganisms increases. Oxygen enhances the damaging effect to microorganisms and further increases their sensitivity to radiation as discussed previously. Therefore, if lyophilization is used as a preservation procedure, it would be better to leave some amount of water in the tissue than attempt to remove as much water as possible. It should be noted that irradiation at low temperatures increases, while that at higher temperatures decreases the resistance of bacteria and viruses.

Connective tissue allografts, such as bone, cartilage, tendons, ligaments, dura mater, skin, amnion, pericardium, heart valves and corneas, are widely used for reconstructive surgery in many clinical disciplines, including orthopaedics, traumatology, neurosurgery, cardiosurgery, plastic surgery, laryngology and ophthalmology. The grafts are prepared by specialized laboratories called 'tissue banks'. The risk of infectious disease transmission with

Microorganisms can be introduced into grafts during tissue procurement, processing, preservation and storage, but even if all these procedures are done under aseptic conditions, the possibility of bacterial, fungal and viral disease transmission of donor origin cannot be excluded. Bacterial, including tuberculosis, fungal, and viral infections, such as human immunodeficiency virus (HIV), hepatitis B and C (HBV, HCV), cytomegalo virus (CMV), as well as rabies and prion diseases, have been transmitted by tissue allografts. Thus, radiation sterilization of tissue grafts has been implemented in some tissue banks, and a dose of 25 kGy has been used in many of these tissue banks. The advantage of radiation sterilization is that it allows the processing of grafts, which have been previously sealed or tightly closed in special wrappings. Such procedures prevent any accidental recontamination during

The problem is additionally complicated by the possible presence, in human tissues, of pathogenic viruses, such as the human immunodeficiency virus (HIV) (Daar et al, 1991), hepatitis viruses (HBV, HCV) (Conrad et al, 1995), cytomegalovirus or others. Data concerning the sensitivity of these viruses to ionizing radiation are scarce. This is mainly due to the fact that there are no suitable tests to study their inactivation, no appropriate animal models exist and no suitable method of in vitro culture of highly differentiated target

The wide range of D10 values (4–8.3 kGy) determined for HIV and other viruses might be due to the influence of environmental conditions. Many factors can modify the sensitivity of pathogens microorganisms to ionizing radiation, including the temperature of irradiation. For example, the reduction of HIV virus was achieved with a dose of 50–100 kGy in frozen plasma (–80oC), and with 25 kGy at 15oC (Hiemstra et al, 1991). The D10 value for HIV-1 irradiated at room temperature was 7.2 kGy, and 8.3 kGy at –80oC (Hernigou et al, 2000). The presence or absence of water and oxygen, and presence of radiation protectors are also factors can modify the sensibility of pathogens microorganisms. In the absence of water (for example, in dry air or lyophilized grafts) the resistance of pathogens increases. On the other hand, in the presence of water, an indirect effect of ionizing radiation predominates and the sensitivity of microorganisms increases. Oxygen enhances the damaging effect to microorganisms and further increases their sensitivity to radiation as discussed previously. Therefore, if lyophilization is used as a preservation procedure, it would be better to leave some amount of water in the tissue than attempt to remove as much water as possible. It should be noted that irradiation at low temperatures increases, while that at higher temperatures decreases the resistance of

**4. Gamma sterilization of human tissue grafts**

tissue allografts is a major concern in tissue banking practice.

cells (e.g. hepatocytes) for these viruses has yet been developed.

packing.

bacteria and viruses.

Fig. 6. Effects of gamma radiation on bone collagen molecules.

Collagen is a very variable protein, forming the basis of many connective and support tissues. It is a fibrous structural protein, with a distinctive structure. It has been postulated that polypeptide chain scissions (direct effect) predominate when collagen is irradiated in a dry state due to the direct effect of ionizing radiation, and this, in turn, dramatically increases collagen solubility in vitro and the rate of bone matrix resorption in vivo. It has been found, however, that a crosslinking reaction (indirect effect) appears during the irradiation of collagen in the presence of water (indirect effect), probably due to the action of highly reactive, short lived hydroxyl radicals (• OH) resulting from water radiolysis The Figure 6 shown the simplified scheme illustrating the direct and indirect effects of gamma irradiation on bone molecules.


3.1.4. Random Forest

The random forest (RF) is an algorithm that integrates multiple trees through the concept of integrated learning. Its basic unit is a decision tree, and its essence goes with an important branch of machine learning–integration learning [83]. As shown in Figure 6, from an intuitive point of view, each decision tree is a decision maker. N trees will have N decision. The random forest integrates all the decision and designates the consequence with the most decision as the final output.

**Figure 6.** Architecture of the machine learning models.


**Abstract**

This chapter describes results of a recent investigation aiming to assess the potential of quantum computing and suitably designed algorithms for future computational fluid dynamics applications. For quantum computers becoming available in the near future, it can be expected that applications of quantum computing follow the quantum coprocessor model, where selected parts of the computational task for which efficient quantum algorithms exist are executed on the quantum hardware. For example, in computational fluid dynamics algorithm, this hybrid quantum/classical approach is discussed, and in particular it is shown how the approximate quantum Fourier transform (AQFT) can be used in the Poisson solvers of the considered method for the incompressible-flow Navier-Stokes equations. The analysis shows that despite the inevitable errors introduced by applying AQFT, the method produces meaningful results for three-dimensional example problems. A second example of a quantum algorithm for flow simulations is then described. This method based on kinetic modeling of the flow was developed to reduce the information transfer between quantum and classical hardware in the quantum coprocessor model. It is shown that this quantum algorithm can be executed fully on quantum hardware during a simulation. The conclusion summarizes further challenges for algorithm developments and future work.

**Keywords:** quantum algorithms, computational fluid dynamics, rarefied flows, kinetic modeling


**2.2 Photoluminescence of porous silicon layers**

*Solar Cells - Theory, Materials and Recent Advances*

**2. Photoluminescence study**

applied on the surface of the sample.

range [10–300 K].

**2.1 Experimental details**

temperature studies) (**Figure 1**).

optical characterization technique for nanomaterials.

substrate with a resistivity of 0.001–0.02 Ω cm.

current density, and anodization time) [26]. The present study conclusively suggested that in order to prepare porous silicon samples, we need to determine the optimal conditions that lead to increase the optical efficiency. Herein, we need to study the correlation between the results extracted from the PL analysis and those obtained by ellipsometry. The study of the evolution of the intensities of the emission spectra obtained by the measurement of PL as a function of the porosity and the thickness determined by the ellipsometry of the layers for silicon substrates oriented P-100 of low resistivity is made to precisely clarify the evolution of optical parameters.

The PL measurements were carried out by a solid laser 447 nm and detected through a Jobin Yvon 250-mm HR mono-chromator, with a GaAs photomultiplier associated to standard lock-in technique. Of note, the laser power of 7.66 mW was

The optical characterization used in our work is based on "the radiationmatter interaction"; it tells us about the optical properties of the material. Photoluminescence spectroscopy is widely used to study the electronic structure of materials and the processes of radiative recombinations. It is a nondestructive

In this part, using photoluminescence (PL) spectroscopy, we will determine the optical properties of porous silicon samples produced by electrochemical anodization. First, we analyze the PL spectra of SiP at room temperature. Second, we study the variation of the intensity, and the integrated intensity of PL in the temperature

Serial of porous silicon samples were prepared of *p*-type (100)-oriented silicon

The experimental set-up consists first of all of a pulsed laser, the emission wavelength of which can be changed. The laser we have allows us to take measurements with an excitation of 447 nm. The laser beam is then conveyed to the sample by sets of mirrors. The sample is on a sample holder that can be placed in a cryostat (for low

**110**

**Figure 1.**

*Experimental device used for the measurement of photoluminescence.*


*Article* **Analysis of Air–Oil Flow and Heat Transfer inside a Grooved Rotating-Disk System**

**Chunming Li 1, Wei Wu 2,\*, Yin Liu 2, Chenhui Hu 2 and Junjie Zhou 2**

Received: 18 August 2019; Accepted: 11 September 2019; Published: 18 September 2019

**Abstract:** An investigation on the two-phase flow field inside a grooved rotating-disk system is presented by experiment and computational fluid dynamics numerical simulation. The grooved rotating-disk system consists of one stationary flat disk and one rotating grooved disk. A three-dimensional computational fluid dynamics model considering two-phase flow and heat transfer was utilized to simulate phase distributions and heat dissipation capability. Visualization tests were conducted to validate the flow patterns and the parametric effects on the flow field. The results indicate that the flow field of the grooved rotating-disk system was identified to be an air–oil flow. A stable interface between the continuous oil phase and the two-phase area could be formed and observed. The parametric analysis demonstrated that the inter moved outwards in the radial direction, and the average oil volume fraction over the whole flow field increased with smaller angular speed, more inlet mass flow of oil, or decreasing disk spacing. The local Nusselt number was remarkably affected by the oil volume fraction and the fluid flow speed distributions in this two-phase flow at different radial positions. Lastly, due to the change of phase volume fraction and fluid flow speed, the variation of the average Nusselt number over the whole flow field could be divided into three stages.

**Keywords:** CFD; two-phase flow; volume of fluid; Nusselt number; grooved disk; flow pattern


**4. Reflective Transformative Design process**

The Reflective Transformative Design process (RTD process) is especially created to address the changing field of design and design education as discussed in chapter two. It supports designing disruptive innovative products and intelligent, open systems. Moreover, it does not only aim at supporting the creation such designs, but also aim at supporting novice designers to learn and develop while becoming a designer. In this chapter, we first explain the model, before elucidating how it supports the changing field of design and education.


**Abstract**

Adenosine triphosphate (ATP) and other nucleotides and nucleosides, such as adenosine, are signaling molecules involved in many physiological and pathophysiological processes. The group of cell and tissue responses mediated by these molecules is known as purinergic signaling. Ecto-nucleotidases are ectoenzymes expressed at the cell membrane that act sequentially to efficiently hydrolyze extracellular ATP into adenosine, and they are key elements of this signaling. There is growing interest in studying these enzymes in relation to various pathologies, especially those with an inflammatory component such as cancer. This review summarizes the main protocols for the study of the expression and in situ activity of ectoenzymes in tissue slices and cultured cells.

**Keywords:** nucleotidase, in situ histochemistry, CD39, CD73, lead staining


**1. Introduction**

#### **1.1. The "research-teaching nexus": are research and teaching two sides of the same coin?**

Until the nineteenth century, teaching was considered to be the primary function of universities [1]. In the West, the view that research should become part of the purpose of universities was first articulated and implemented in Europe, specifically in German universities:

"… universities should treat learning as not yet wholly solved problems and hence always in research mode." Wilhelm von Humboldt on the future University of Berlin (1810), cited in Ref. [2, p. 110].

The ideal of universities as institutions of learning, teaching *and* research spread to the US and elsewhere in the late nineteenth and early twentieth century [3]. Academics in higher education began spending increasing proportions of their time researching. Henceforth, it was commonly assumed that teaching and research were mutually supportive activities and that the expertise and insight gained during research improved learning and teaching quality for students [4]. A number of putative relationships between teaching and research were consequently proposed and described using various terms and definitions [5]. A useful and inclusive term that describes a range of modes by which students interface with research in their learning and development is that of the "research-teaching nexus" [6, 7]. According to Haslett [8]:

"The positioning of teaching within the nexus is determined by the role students play (i.e., students as research participants, or as an audience for research) in relation to the research emphasis (i.e., emphasis on research content or on research processes and problems). The nexus has proved useful to institutions and academics in helping them strategically consider the links between research and teaching in their own curricula and teaching." [8, p. 1].

Initially, the mutualism between teaching and research in the "research-teaching nexus" seemed self-evident: academic researchers bring their critical and reflective approach to their discipline and their hands-on experience of research methods and professional communities into the classroom, acting as a prism through which students are exposed to research in all of its many facets. Moreover, there was an assumption that research active staff aware of the current developments in their field would be able to enhance their student's learning experience. However, several authors have since challenged this relationship as one that remains unsubstantiated by research and scholarly evidence [9–11]. Indeed, comprehensive analyses of available data show that the quality and/or quantity of research output of a lecturer does not correlate strongly with undergraduate students' perception of teaching quality [9–12]. Undergraduate students do report, however, that being taught by active researchers can enhance their engagement and experience at university [13]. This indicates that teaching quality and student learning are not *necessarily* improved by the research activity of the teachers themselves via a passive mechanisms of osmosis, but rather that students must be actively involved in research to create learning experiences that go beyond those typically understood to be part of the "research-teaching nexus" to create what we will here refer to as "research-involved teaching" (RIT). Directly involving students in research-related activities—if done effectively—can result in a number of reported benefits to their development of scientific skills and knowledge as well as their confidence in carrying out research [14, 15].

### **1.2. Teaching in higher education and the role of undergraduate research: one goal, many approaches**

**1. Introduction**

112 Global Voices in Higher Education

Ref. [2, p. 110].

**1.1. The "research-teaching nexus": are research and teaching two sides of the same coin?**

Until the nineteenth century, teaching was considered to be the primary function of universities [1]. In the West, the view that research should become part of the purpose of universities

"… universities should treat learning as not yet wholly solved problems and hence always in research mode." Wilhelm von Humboldt on the future University of Berlin (1810), cited in

The ideal of universities as institutions of learning, teaching *and* research spread to the US and elsewhere in the late nineteenth and early twentieth century [3]. Academics in higher education began spending increasing proportions of their time researching. Henceforth, it was commonly assumed that teaching and research were mutually supportive activities and that the expertise and insight gained during research improved learning and teaching quality for students [4]. A number of putative relationships between teaching and research were consequently proposed and described using various terms and definitions [5]. A useful and inclusive term that describes a range of modes by which students interface with research in their learning and

"The positioning of teaching within the nexus is determined by the role students play (i.e., students as research participants, or as an audience for research) in relation to the research emphasis (i.e., emphasis on research content or on research processes and problems). The nexus has proved useful to institutions and academics in helping them strategically consider

Initially, the mutualism between teaching and research in the "research-teaching nexus" seemed self-evident: academic researchers bring their critical and reflective approach to their discipline and their hands-on experience of research methods and professional communities into the classroom, acting as a prism through which students are exposed to research in all of its many facets. Moreover, there was an assumption that research active staff aware of the current developments in their field would be able to enhance their student's learning experience. However, several authors have since challenged this relationship as one that remains unsubstantiated by research and scholarly evidence [9–11]. Indeed, comprehensive analyses of available data show that the quality and/or quantity of research output of a lecturer does not correlate strongly with undergraduate students' perception of teaching quality [9–12]. Undergraduate students do report, however, that being taught by active researchers can enhance their engagement and experience at university [13]. This indicates that teaching quality and student learning are not *necessarily* improved by the research activity of the teachers themselves via a passive mechanisms of osmosis, but rather that students must be actively involved in research to create learning experiences that go beyond those typically understood to be part of the "research-teaching nexus" to create what we will here refer to as "research-involved teaching" (RIT). Directly involving students in research-related activities—if done effectively—can result in a number of reported benefits to their development of scientific skills and knowledge as well as their confidence in carrying out research [14, 15].

was first articulated and implemented in Europe, specifically in German universities:

development is that of the "research-teaching nexus" [6, 7]. According to Haslett [8]:

the links between research and teaching in their own curricula and teaching." [8, p. 1].

Undergraduate research and inquiry can be defined as "student engagement from induction to graduation, individually and in groups, in research and inquiry into disciplinary, professional and community-based problems and issues, including involvement in knowledge exchange activities" [16, p. 16]. In this context, Linn et al. [17] distinguish between more individualized and bespoke "Undergraduate Research Experiences" (UREs) and "Course-Based Undergraduate Research Experiences" (CUREs; see also Ref. [15]):

"UREs feature individual students in faculty research laboratories and provide the opportunity for one-on-one mentoring. Typically, students spend one or more semesters in labs, although the type of activity and form of mentoring varies substantially […]. In contrast, CUREs have a curriculum and are open to most students. CUREs put high demands on mentors to guide many students." [17, p. 2].

Typically, UREs (e.g., research internships, work placements, etc.) are bespoke and individualized research experiences that affect only a few students and that can be highly competitive because of the limited availability of URE opportunities at any given time. The time invested by students in this type of experience is often high and, because of the resources and time required for delivering UREs, in many cases falls outside their regular taught curriculum (e.g., summer term breaks). The comparatively large amounts of staff, space, and material resources required for the delivery of individualized UREs (especially in applied scientific disciplines) that often require one-to-one research supervision make large-scale provision of URE's impractical at even the most research-intensive institutions [17, 18]. In contrast, CUREs are embedded in the regular curriculum and therefore open to all students of a module/programme, while at the same time being much less individualized, less resource intensive and therefore typically easier to deploy [17].

Though assessing the impacts of direct research experiences on student outcomes is challenging and has not been a regular feature of deploying interventions to facilitate UREs and CUREs [17], the available evidence suggests that engaging undergraduate students in research activities improves student engagement and retention and can attract students into postgraduate research careers [15, 18–23]. These benefits have led to calls for expanding RIT in various forms in undergraduate teaching both in North America and Europe [24, 25].

Women and minority undergraduate students seem to particularly benefit from research experiences [26, 27]. This is encouraging in light of student populations in the UK and worldwide tending to become more diverse in both social background and academic ability as higher education becomes more inclusive [28, 29]. The data also suggest, however, that the students who mostly benefit from undergraduate research opportunities are those who already have a high level of engagement and interest in postgraduate research [15, 22]. We therefore argue that a central aim of involving students in research (and of teaching in general) should be to provide opportunities precisely for those students who are not already enthused and determined to embark on research careers.

Thus, for RIT strategies to ultimately deliver benefits for all students, they must be integrated fully into university teaching programmes and curricula with forethought to maximize their impact [15, 23]. This is particularly important considering the potential cost of investing in RIT strategies at a time of increased marketization and cuts to funding in higher education [30]. Innovative models have been developed for feasibly delivering and assessing the impacts of CUREs and UREs on a large scale in high-subscribing higher education programmes [15, 31–33]. For example, Rowland et al. [34], in recognition of the importance of student choice and the diversity among participants in higher education, created a two-stream undergraduate chemistry module. One stream provided a traditional learning experience in laboratory techniques and methods and the other offered students with a desire to carry out research the opportunity to do so by carrying out a scaffolded undergraduate research project in the laboratory instead [34]. Though they identified challenges to staff and resourcing in providing and supervising a large number of undergraduate research projects, the authors found streaming the teaching better supports students with diverse needs [34]. Desai et al. [18] approached the issue of limited resources differently: they devised a tiered supervision system based on the concept of a "research-intensive community" (p. 137) at Texas A&M University. The system allowed efficient small group supervision of the research activity of undergraduate students.

CUREs can be used to provide research-involved experiences for most or all students [17]; where provision of UREs is limited, all students should be given an equal opportunity to compete for these opportunities and benefit from them even if they are not directly involved. While models for involving undergraduate students in research continue to be developed for individual learning experiences, curriculum elements, and modules, the greater challenge is to develop undergraduate research opportunities across entire undergraduate curricula and programmes that are interlinked and build on each other throughout the student journey. Moreover, involving undergraduate students in knowledge exchange and collaboration with researchers in industry and elsewhere outside of the university in the context of research has not received as much attention as involving students in institutional academic research. All of these aspects of student development are essential, however, for developing research-ready graduates that have the skills required by employers who hire graduates [35].

#### **1.3. Undergraduate research improves undergraduate employability**

Employability encompasses what graduates know, what they can do, their job specific skills, their transferable skills, and their attitude and behavior [36]. The required skills are often specific to an employer or specific sector of the economy [37]. Graduates, especially from biological science programmes, therefore enter into a highly competitive job market where research skills are often critical to prospective employers. A recent consultation undertaken by the Association of the British Pharmaceutical Industry [38] identified that graduates often lack the required skills or knowledge important for employment within the life science industry and that newly employed graduates routinely need extensive graduate training, which can represent a major commitment from the employer [39]. This skills gap can be significantly reduced, however, where there is suitable employer engagement within the degree programme. For example, the involvement of Siemens with the University of Lincoln engineering degree has allowed Siemens to reduce the length of their graduate training programme by 9 months [40]. A key element of higher education engagement with industry is in shaping graduate behavior and attitude such that the graduates understand their choices and employer expectations, and are positioned to meet those expectations. Two specific challenges need to be addressed to produce industry-ready graduates: to impart relevant job specific and appropriate transferable skills, including research-specific skills, and to provide the appropriate industry engagement and an understanding of the responsibilities and practice of those working in the respective industry. Involving undergraduate students in research and knowledge exchange can help meet the first of these challenges through the general benefits it provides to the cognitive development and skills development of students [41]. More importantly, however, participation in undergraduate research can break down student misconceptions about scientific research, careers in science and the day-to-day activities of research scientists [42].

Thus, for RIT strategies to ultimately deliver benefits for all students, they must be integrated fully into university teaching programmes and curricula with forethought to maximize their impact [15, 23]. This is particularly important considering the potential cost of investing in RIT strategies at a time of increased marketization and cuts to funding in higher education [30]. Innovative models have been developed for feasibly delivering and assessing the impacts of CUREs and UREs on a large scale in high-subscribing higher education programmes [15, 31–33]. For example, Rowland et al. [34], in recognition of the importance of student choice and the diversity among participants in higher education, created a two-stream undergraduate chemistry module. One stream provided a traditional learning experience in laboratory techniques and methods and the other offered students with a desire to carry out research the opportunity to do so by carrying out a scaffolded undergraduate research project in the laboratory instead [34]. Though they identified challenges to staff and resourcing in providing and supervising a large number of undergraduate research projects, the authors found streaming the teaching better supports students with diverse needs [34]. Desai et al. [18] approached the issue of limited resources differently: they devised a tiered supervision system based on the concept of a "research-intensive community" (p. 137) at Texas A&M University. The system allowed efficient small group supervision of the research activity of undergradu-

CUREs can be used to provide research-involved experiences for most or all students [17]; where provision of UREs is limited, all students should be given an equal opportunity to compete for these opportunities and benefit from them even if they are not directly involved. While models for involving undergraduate students in research continue to be developed for individual learning experiences, curriculum elements, and modules, the greater challenge is to develop undergraduate research opportunities across entire undergraduate curricula and programmes that are interlinked and build on each other throughout the student journey. Moreover, involving undergraduate students in knowledge exchange and collaboration with researchers in industry and elsewhere outside of the university in the context of research has not received as much attention as involving students in institutional academic research. All of these aspects of student development are essential, however, for developing research-ready

Employability encompasses what graduates know, what they can do, their job specific skills, their transferable skills, and their attitude and behavior [36]. The required skills are often specific to an employer or specific sector of the economy [37]. Graduates, especially from biological science programmes, therefore enter into a highly competitive job market where research skills are often critical to prospective employers. A recent consultation undertaken by the Association of the British Pharmaceutical Industry [38] identified that graduates often lack the required skills or knowledge important for employment within the life science industry and that newly employed graduates routinely need extensive graduate training, which can represent a major commitment from the employer [39]. This skills gap can be significantly reduced, however, where there is suitable employer engagement within the degree programme. For example, the involvement of Siemens with the University of Lincoln engineering degree has allowed Siemens to reduce the length of

graduates that have the skills required by employers who hire graduates [35].

**1.3. Undergraduate research improves undergraduate employability**

ate students.

114 Global Voices in Higher Education


We applied ten different modeling algorithms and two different prevalence-independent (discrimination) performance metrics, the threshold-independent Relative Operating Characteristic (ROC), and the threshold-dependent True Skill statistic (TSS) scores with 10 permutations to test the importance of each variable relevant for the modeled response variable within the Biomod2 environment (section Modeling Approach). Single models were run with repeated random data splits; hence, 70% of the data were used to train the models, while the residual 30% of data were used to validate model performance. Due to the cross-validation procedure, each of the 10 algorithms was replicated 20 times for the model calibration, amounting to a total number of 200 modeled results. Hydroacoustic data were weighted by 0.75 and image data by 1 (section Species Distribution Modeling).

#### Model Validation

The area under the ROC curve (AUC) validation statistic is a commonly used threshold independent accuracy index that ranges from 0 to 1 (1 = highly accurate prediction, 0.5 = prediction no better than random) for assessing the capacity of species distribution models. The ROC index defines the probability that an SDM will rank a presence locality higher than an absence (Pearce and Ferrier, 2000; Liu et al., 2009) and is therefore not well-suited for modeling presence only data (van Proosdij et al., 2016). According to Pearce and Ferrier (2000), rates higher than 0.9 indicate excellent discrimination because the sensitivity rate is high, relative to the false positive rate.

TABLE 2 | Macroalgae progression over simulated time spans (1991-2026) depending on varying SPM intensities (\*Probability of macroalgal occurrence >75%; +after Quartino and Boraso de Zaixso, 2008 applying a mean summer production of 5.55 t ha−1 ).

The deviance between observations and predictions (the subtraction of the output score from 1 for presences and from 0 for absences) throughout the whole cove may reflect the areas where commission error (negative deviance) and omission error (positive deviance) spatially coincide (Lobo et al., 2008). The deviances between observations and predictions are expressed as under- and overestimations as follows: underestimation is defined as −97 to −50% deviance of measured to predicted value (e.g., measured 1 for presence and measured 0.25), moderate underestimation −50 to −25%, good −25 to 25%, moderate overestimation 25 to 50%, overestimation 50 to 91% (e.g., measured 0 for absence and measured 0.75). Besides over and underestimation, this itemization also reveals the regional distribution of the errors; information, which is not provided by the TSS and the ROC scores.

As the AUC is used for SDM evaluation (e.g., Vorsino et al., 2014), but also has been criticized (e.g., Jiménez-Valverde, 2012), we also provide the evaluation criterion TSS**,** recommended by Allouche et al. (2006). The TSS is a measure of performance of species distribution models and prevalence independent and favors the combination of binary predictions which best separate presences from absences. It corresponds to the sum of sensitivity and specificity minus one. The "sensitivity" value denotes the proportion of presences correctly predicted, whereas the "specificity" value denotes the proportion of absences correctly predicted (Barbet-Massin et al., 2012). TSS statistic ranges from −1 to +1 and tests the concordance between the expected and observed distribution. A TSS value of +1 indicates perfect agreement between the observed and expected distributions, whereas the value 0 defines a model which has a predictive performance no better than random. An evaluation metric quality measure of 0.7 or higher indicates good or very good performance of the model (Thuiller et al., 2010).

#### Ensemble Modeling

The application on biomod2 is one of several current approaches to modeling species' distributions using presence/absence data and environmental data. Because of stochastic elements in the algorithm and underdetermination of the system (multiple solutions for the model optimization), no unique solution is produced. Such ensemble models have been used extensively in credit scoring applications and other areas because they are considered to be more stable and, more importantly, predict better than single classifiers (Lessmann et al., 2015). They are also known to reduce model bias and variance (Kim et al., 2006; Tsai and Hsiao, 2010). Ensemble classifiers pool the predictions of multiple base models. Much empirical and theoretical evidence has shown that model combination increases predictive accuracy (Paleologo et al., 2010; Finlay, 2011). Biomod2 proposes an ensemble modeling approach implying the synthesis of two or more SDMs of best fit to a single ensemble model (EM), thus to improve model accuracy by including an indication of fuzziness and to asses model congruence. This inclusive statistical procedure improves the stability and accuracy of predictive nonlinear models, because it integrates uncertainties in parameter values and model structure. The approach has already been applied for ecosystem degradation by Vorsino et al. (2014) and for Antarctic sea-level rise simulations by DeConto and Pollard (2016). EMs consider uncertainties such as dependence on the initial conditions and partially incomplete model formulation.

In this study, the EM was enhanced successively by the systematic parameter selection and an optimization of the TSS threshold by visual revision. The total number of alternative realizations (200) was scaled by a binomial GLM to ensure comparable results. The EM was calculated as the mean value of 135 alternative realizations with a high TSS value (>0.7) defined as "good" prediction accuracy (Thuiller et al., 2010).

# RESULTS

#### Model Quality Assessment

The algorithm-ranking by TSS and ROC scores for all alternative realizations (**Figure 3**) shows best performance of the RF algorithm, followed by CTA, GBM, MaxEnt, and GAM. All 20 realizations of these five algorithms were incorporated into the EM. Furthermore, the majority of MARS (16) and FDA (14) realizations had mean TSS scores clearly >0.7 and a ROC >0.9. Five ANN realizations barely achieved a TSS evaluation score between 0.70 and 0.72 and a ROC between 0.90 and 0.91. GLM and SRE performance was comparatively weak. Hence, these algorithms were not included in the EM. The lineup of the most suitable realizations for each algorithm (**Figure 4**) highlights the spatial distinctions between the results. Most of the models showed good or very good performance in terms of predictive power and accuracy, with highest ROC values of 0.980 and 0.941, obtained for the RF and CTA models, respectively. The poorest performance was shown by the SRE model which had a mean ROC of 0.768. Nevertheless, the mapped distribution patterns varied remarkably depending on the model used. For response curves see **Supplementary Figure 2**. The EM predicts the spatial

occurrence of macroalgae in Potter Cove with a high statistical reliability (TSS 0.833/ROC 0.975).

A validation across all 135 models revealed a ranking of environmental variables accounting for the "tom" status macroalgae distribution (**Figure 5A**) by mean variable importance values from high to low as follows: probability of hard substrate occurrence (36.62%), SPM (24.83%), distance to glacier front (12.35%), TOC (9.69%), bathymetry (9.08%), and slope (7.44%). The percent values were independently determined by the model and no interactions were taken into account between the variables.

## Simulation of Macroalgal Distribution in Potter Cove

In the "tom" status EM (2008–2015) in **Figure 5A**, macroalgae communities are concentrated on the northern and northwestern shores of Potter Cove, with diminishing occurrence probability toward the inner glacier-proximal cove section (northeast). The northwestern areas represent hard substrates exposed by glacial retreat. A second region of high occurrence is located close to the southeastern edge of the cove opening into Maxwell Bay (Peñón 1) which has a longer history as ice-free area. Areas of uncertainties and differences between the alternative realizations were determined based on standard deviation and under- and overestimated areas (**Figure 5B**). Small standard deviations were typical for the deeper parts of the cove where macroalgae are absent. The high scores of standard deviations in the northern part of Potter Cove resulted from small-scale variability in the binary-coded macroalgae input data set of presence (1) and absence (0), located in close vicinity to each other, as well as from areas of poor sample density. Where the standard deviation was high, the model revealed overestimations, preferably in areas of macroalgae. Here, the model has difficulties to represent the small-scale variability of input data (presence alternates with absence at very close range) and predicts macroalgae presence in areas of general absence of algae. In contrast, only few underestimations occurred in the inner cove (measured presence predicted as absence).

Macroalgae (water depth of >30 m, hard substrate) cover 62.88 ha of the 785.31 ha Potter Cove total bottom area, which equals 8.01% macroalgae coverage in Potter Cove. Applying the mean macroalgal summer production of 5.55 t ha−1 estimated from Quartino and Boraso de Zaixso (2008) the mean summer macroalgae production for the study area in Potter Cove is approximated to 348.98 t in the "tom" scenario (2008-2015). Of the total modeled macroalgal summer production, 5.22% are estimated for the newly ice-free areas due to glacier retreat (3.48 ha), which amounts to 19.31 t (**Supplementary Figure 3**).

The scenario maps derived from the "tom" EM display the potential macroalgae distributions, based on the assumptions of varying SPM transport dynamics into Potter Cove (see section Biomod2 Model Calibration and Validation) relating to different states of glacier retreat in past and future. Scenario 1 (simulating the state in 1991; **Figure 6A**) and scenario 2 (1998; **Figure 6B**) show more spatially extended macroalgae occurrence probability under reduced SPM input and thus higher light availability in the water column. Especially in the southern peripheral region and in the inner glacial proximal cove section of Potter Cove, macroalgal presence probability becomes significantly greater compared to the 2008–2015 "tom" status. We validated our modeling approach for the past by overlaying the macroalgae distribution obtained in scenario 1 (status 1991) with the digitized map of Klöser et al. (1996) and the original in situ data from 1994 to 1996 (Quartino et al., 2005), and with the coastline of Fourcade glacier from 1995 (Rückamp et al., 2011). Almost everywhere, the predicted areas match fully with the in situ data (**Figure 5**), which additionally confirms the validity of our model.

Scenarios 3 (year presumed 2019) and 4 (2026) in **Figures 6C,D** assume increased sediment stress in the near future and predict a decrease of areas inhabited by macroalgae compared to the "tom" (2008–2015) scenario. The most significant negative changes are predicted for the northwestern area close to the outer cove (Peñón de Pesca).

As a general trend, macroalgal coverage is decreasing between the past, over the "time of measurement," toward the future scenarios, under conditions of increasing SPM runoff into the cove, the basic assumption in our simulations (**Figure 6**). This results in a decrease of macroalgal summer production over time. In scenario 1 (1991) the macroalgae suitable habitat area (probability >75%) extends over 142.75 ha. Applying the mean macroalgal summer production estimate of 5.55 t ha−1 (Quartino and Boraso de Zaixso, 2008) this equals 792.26 t. In scenario 2 (1998) only 100.25 ha are covered by macroalgae equaling 556.39 t. In contrast, macroalgae area extension decreases to 25.39 ha ( ˆ= 140.91 t) in scenario 3 and to only 7.4 ha in scenario 4 ( ˆ= 41.07 t). Distribution maps for macroalgae under varying SPM conditions (**Figure 6**) are available at doi: 10.1594/PANGAEA.854410 (Jerosch et al., 2015).

their predictive quality according their True skill statistic (TSS) and Relative Operating Characteristic (ROC) scores. The probability of macroalgae occurrence is given in %. Note that none of the models generated with GLM (J) and SRE (K) scored for the implementation into the EM (TSS <0.7). The final mean Ensemble Model (I) is highlighted with a green frame.

# DISCUSSION

Enhanced spatial knowledge of suitable habitat conditions for macroalgal growth allowed for refined estimations of macroalgal summer production in Potter Cove on KGI, compared to previously published estimates based on straight forward up-scaling. We deem the integrative EM approach preferable over single SDM based approaches, as it accounts for uncertainty (e.g., initial conditions, imperfect model formulation), and model congruence (e.g., Vorsino et al., 2014; DeConto and Pollard, 2016). In agreement with the ecology and physiology of benthic macroalgae (Wiencke and Clayton, 2002; Hanelt et al., 2003;

Zacher et al., 2009; Hurd et al., 2014), the present EM identified hard substrate occurrence and SPM distribution in surface waters as the main drivers for macroalgae distribution and colonization in this Antarctic fjord. Sufficient light availability is, indeed, an important prerequisite for a positive carbon balance and the build-up of macroalgal biomass (Gomez et al., 1997; Deregibus et al., 2016). On the other hand, the model does not include important factors such as ice scouring in the inner part of the cove (Deregibus et al., 2017), sedimentation effects on algal grazers (Zacher et al., 2016), and it does not account for obvious interactions and feedback loops between different input variables. Newly opening hard substrate surfaces in glacial proximity can be smothered and buried under sediment deposits as glacial run-off continues, reducing macroalgae habitat availability.

To our knowledge, we here present the first correlative distribution model of macroalgae in polar regions. The SDM was evaluated reliably (TSS 0.833/ROC 0.975) by using independent field data for past and present scenarios. Both continuous performance metrics such as AUC and binary performance metrics such as TSS are independent of prevalence (i.e., proportion of spatial coverage). They are often employed as they are frequently implemented in software solutions such as Biomod2. Prevalence-independent metrics are, however, limited to measuring discrimination and cannot be used to assess calibration. Binary metrics often select models that have reduced ability to separate presences from absences, which can lead to uncertain estimates (Lawson et al., 2014). We applied 20 runs for each algorithms, generating 20 AUC and TSS values for each model, to provide a more informative evaluation of model accuracy than only one estimate. We used AUC and TSS, two evaluation methods found in many published examples (e.g., Hijmans, 2012; Vorsino et al., 2014), to compare the modeling results obtained with different algorithms for our data set. However, we agree with the recommendation of Lawson et al. (2014) and Muscarella et al. (2014) to use the wider application of prevalence-dependent continuous metrics, particularly likelihood-based metrics such as Akaike's Information Criterion (AIC), to assess the performance of presence–absence models. As AIC has not yet been implemented in Biomod2, combination with the R package ENMeval (Muscarella et al., 2014) is recommended for future SDM studies.

Furthermore, the model could be improved by applying structured, spatially segregated allocation of data to calibration and validation data sets instead of random splitting, to assess the ability of the model to predict more distant locations and to conduct spatially independent model evaluations (Dormann et al., 2007; Hijmans, 2012; Muscarella et al., 2014; Roberts et al., 2017). This is especially recommended in studies in which training and test data sets are not spatially independent in all areas as was the case in Potter Cove. This approach could have in particular improved the predictions in the northern part of the cove where under- and overestimations occur in close vicinity (**Figure 5**; Barnes and Clarke, 2011; Lawson et al., 2014; Muscarella et al., 2014; Boavida-Portugal et al., 2018).

The model considers two decisive but opposed effects influencing the distribution of macroalgae: new ice-free areas due to tidewater glacier retreat provide new potential habitats while increased sediment run-off reduces light availability, the prerequisite for algal growth (Quartino et al., 2013). The extrapolation of a linear regression on long-term SPM data (1991–2010) (after Schloss et al., 2012) to the year 2026 was used to simulated two past and two future scenarios with distinct SPM levels. We studied macroalgae distribution in the year of glacier transition on land; hence no more newly ice-free areas can be expected in future Potter Cove scenarios. However, apparently the time elapsed since ice retreat has not allowed for appreciable macroalgal growth in every section of the newly available habitat. Hence, the model predicts further alterations of the macroalgal habitat in Potter Cove as long as SPM concentrations increase during growing season and a likely decrease in macroalgae biomass, albeit with new colonization of recently ice-free and still not overgrown habitats in the inner cove.

The EM predicts decreasing macroalgal area coverage for particular scenarios with increasing sediment run-off through the past to the recent past ("tom" status) and into the future. Both, the predicted "tom" status (2008–2015) and the past 1991 scenario 1 could be confirmed by comparing to empirical knowledge of past macroalgal distribution, based on underwater video and photographic transects. Macroalgae presence/absence data (**Figure 2A**) reveal low macroalgae distribution along the south coast, which explains low probability values in the "tom" scenario, whereas future and past scenarios show ample macroalgae probabilities. This effect is caused by hard substrate availability and by local variation of SPM. Both, measured and then interpolated SPM data, as well as the satellite pixels, show slightly lower SPM values along the southern coast. However, SPM residence time and plume extension are highly variable in space and time. Nevertheless, we can predict low macroalgae probability on the southern coast because the currents push the SPM plume (Lim et al., 2013) toward this side, and because the sandy bottom is covered by smaller and looser stones and rubble rather than by rocks as on the north coast.

The modeled mean summer macroalgae production for 1991 is 23% greater than the production estimated for 1994– 1995 (790 vs. 608.65 t) based on phototransects (Klöser et al., 1996; Quartino and Boraso de Zaixso, 2008) and calculated for exactly the same spatial extent of study area. We attribute the main difference to a methodological underestimation due to the low data density. According to the modeled results, there is indication for a higher macroalgae coverage at the northern coast of the cove compared to the study of Klöser et al. (1996) (**Supplementary Figure 3**). However, decreasing production over time in our simulation is in good agreement with our recent results showing a slight decrease in production from 1991 to 1994–1995.

SPM significantly depends on the climate weather conditions, mainly air temperature and wind speed as well as the duration of these weather conditions. Our analysis showed that the weather conditions (wind speed and direction) 1 day or hours before the measurement correlate to the SPM amount in the water column. We are currently working on modeling the SMP dynamics in Potter Cove coupled with FESOM-C (Androsov et al., 2019), which will be used for future SDMs in Potter Cove. This new analysis shows that meteorological conditions on the day before the measurement strongly influence spatial SPM concentrations in Potter Cove surface waters (Neder and Fofonova, unpublished data).

The SPM snapshot we used in this study indicates decreasing SPM concentration from the head toward the outer cove. On the same transect, macroalgae maximum growth depth and the summer production increase reciprocally and macroalgal species composition changes (Quartino et al., 2013; Deregibus et al., 2016; Campana et al., 2018). Increasing turbidity, meaning less light availability may lead to an upward shift of the macroalgae at the coastline, because they do not maintain positive carbon balance at deeper depth under high SPM (Deregibus et al., 2016). An upward shift of macroalgae was also found for an Arctic fjord comparing data from the mid-90ies with 2012–2014 (Bartsch et al., 2016). Not only the decreased light availability for the algae, but also the sediment per se may reduce the macroalgal recruitment success by up to 100% (Zacher et al., 2016). This may be due to processes such as sediment scour and burial (Airoldi, 2003).

While scenarios 1 (1991) and 2 (1998) model macroalgal coverage of 143 ha and 100 ha, Klöser et al. (1996) made their approximation for 110 ha in 1993/94. The model describes the past and the "tom" status of macroalgal distribution in Potter Cove with a slight tendency of an underestimation. The arguably crude projection of macroalgal community distribution and inferred productivity in a future of increasing discharge of eroded sediments for 2019 (scenario 3) and 2026 (scenario 4) highlights a general trend toward a dramatically reduced macroalgal summer productivity inside Potter Cove as melting of the Fourcade glacier continues. This process is mitigated by an increased colonization and productivity by macroalgae inside the cove in shallow hard bottom areas formerly covered by the glacier (Quartino et al., 2013; Deregibus et al., 2016; Campana et al., 2018) and hence predicts a general increase in benthic primary production in polar coastal areas.

This rather drastic decline of productivity presumably represents an overestimation because modeled SPM values were reduced/increased equally in all areas of the cove and by assuming a linear relation between distance to the glacier and SPM change in time. Monien et al. (2017) stated, however, that up to 50% of the plume sediments are deposited in glacier proximal areas (the inner cove) and do not affect all areas equally as assumed in our model. This is also indirectly confirmed by light measurements at different sites of the cove, measuring higher light penetration in the outer cove compared to the inner cove (Quartino et al., 2013; Deregibus et al., 2016). Furthermore, the satellite image showing the SPM plume represents a snapshot of a single day, potentially failing to adequately represent the general situation. However, even if changes will be less pronounced, the negative effect of increased sedimentation on macroalgal distribution and production is clearly visible and reproducible with our applied data sets.

More spatial aspects that structure benthic communities in polar areas can be added to the model such as ice scouring and sea ice timing (Smale et al., 2008; Barnes, 2017; Deregibus et al., 2017). Clark et al. (2013) predicted that earlier ice break-up can shift shallow water ecosystems from invertebrate dominated to macroalgae dominated communities, in areas with hard substrate present. In recent years we observed higher frequency of ice-free winters in Potter Cove (doi: 10.1594/PANGAEA.773378, Gómez Izquierdo et al., 2009). In the outer areas of Potter Cove big icebergs are a major disturbance factor (Klöser et al., 1996) and iceberg incidence inside the cove has increased in recent years (Deregibus et al., 2017). Contrary, ice disturbance in the inner cove is produced by flow of ice blocks through the glacier front line (Falk et al., 2016), which may diminish in the future as the glacier front retreats further onto land. Ice scouring was signaled as a driver of increased macroalgal patchiness in shallower shelf areas (Clarke et al., 2007; Clark et al., 2015) and the coexistence of early and late successional stages (Quartino et al., 2005; Barnes, 2017). Results of ongoing experiments in Potter Cove will provide detailed information about ice disturbance influence on macroalgae (Deregibus et al., 2017) and as soon as transferred into a spatial data set, they can be included in the model.

Arguably, the productivity estimates presented here are limited to present day data availability. To get more reliable productivity measurements, it would be very important to take year-round data of the main biomass builders into account, as many Antarctic macroalgae show highly seasonal growth starting in late winter/spring (Wiencke and Clayton, 2002). However, late winter to early spring data on algal physiology and light climate are extremely rare and difficult to obtain for polar coasts. For the future we recommend to conduct an appropriate spatial habitat monitoring program for representative areas which could serve to improve productivity estimates. For improved biomass estimations, we need models for single macroalgal species, since decreasing productivity can also result from replacement of kelp by more robust but smaller macroalgae species. Furthermore, a monitoring programme should include biological feedback in terms of zoobenthos succession on newly ice-free hard substrates as shown by Lagger et al. (2017) and Campana et al. (2018).

## CONCLUSIONS

There is an urgent need to quantify and model geographic shifts of species and community distribution ranges in times of global change (Sahade et al., 2015; Singer et al., 2016; Urban et al., 2016). Quality assessed extrapolation of single measurements in coastal areas and coastal structures such as glacial coves highlights the ecologically relevance of local analyses and supports regional and global budgeting of carbon cycling. Correlative models can spatially simulate effects of climate change and allow for reproduction of the results. Species distribution models provide a statistical validation of the results and ranking of the model-relevant environmental variables for cause-effect assessments. Statistical models are robust and applicable to many groups of species; however, they do not yet consider ecosystem functions and feedback loops such as species interactions. The combination of robust statistical correlative models with mechanism-orientated modeling is a promising approach for future biomass approximations in coastal Antarctic fjord systems under climate change with the next generation of species distribution models (Urban et al., 2016). It remains however unclear whether the projections resulting from such models are more reliable (Singer et al., 2016). Here, we provide the first step for spatial-temporal ecosystem modeling of macroalgae in the Antarctic Peninsula region. An exceptional long-term and high-density database, rarely available in polar coastal environments, was used for model construction. The approach can be expanded to associated questions, such as the distribution of macroalgal-associated fauna, or to improve regional macroalgal distribution estimates for systems similarly influenced by glacial erosion. Factors to be considered would include sea ice coverage, hard substrate, and SPM data in the areas of interest. Significant reductions of macroalgal productivity as we are predicting for Potter Cove is an assumption that can be tested in the upcoming decades. As more data layers for ice scour and grazers become available, the model can be refined.

#### AUTHOR CONTRIBUTIONS

KJ, DA, and KZ contributed conception and design of the study. KJ, FS, and HP organized the database and ran the statistical analysis and the R codes. MQ and FS analyzed video transects recorded in 2011–2012. KJ wrote the first draft of the manuscript. DA, KZ, HP, UF, DD, GC, and MQ wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.

#### FUNDING

The research was supported by Grants JE 680/1-1 and ZA 753/1-1 of the priority programme SPP 1158 Antarctic Research with Comparative Investigations in Arctic Ice Areas of the German Research Foundation (DFG). The present manuscript also presents an outcome of the EU research network IMCONet funded by the Marie Curie Action IRSES, within the Seventh Framework Programme (FP7 IRSES, Action No. 318718).

#### ACKNOWLEDGMENTS

The work was performed based on data collected at Carlini (former Jubany) Station, Dallmann Laboratory, within the framework of the scientific collaboration existing between Instituto Antártico Argentino/Dirección Nacional del Antártico and Alfred Wegener Institute, Helmholtz Centre for Polar

#### REFERENCES

and Marine Research. Special thanks to Patrick Monien for providing unpublished TOC data and to Irene Schloss for the scenario specification of the SPM magnitude. Many thanks to Stephan Frickenhaus for constructive comments, which improved the manuscript.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo. 2019.00207/full#supplementary-material

The following are the supplementary data to this article: **Supplementary Material** supplies details about the modeling procedure, the input data processing and the model validation. The table in **Supplementary Table 1** provides precise information about the input data.

a dichotomy. J. Biogeogr. 39, 2119–2131. doi: 10.1111/j.1365-2699.2011. 02659.x

Bathymetry for Potter Cove, WAP, and Antarctica. Bremerhaven: PANGAEA. doi: 10.1594/PANGAEA.854410

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Jerosch, Scharf, Deregibus, Campana, Zacher, Pehlke, Falk, Hass, Quartino and Abele. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.