paper update

paper.bib

@@ -77,7 +77,7 @@ @article{to-va:2023
 }
 
 @article{po-gu:2023,
-author = {Ponti, Luigi and Gutierrez, Andrew Paul},
+author = {Ponti, L. and Gutierrez, A.P.},
 year = {2023},
 month = {08},
 pages = {},
@@ -96,7 +96,7 @@ @article{g-y-n-e:1999
 issn = {0304-3800},
 doi = {https://doi.org/10.1016/S0304-3800(99)00144-1},
 url = {https://www.sciencedirect.com/science/article/pii/S0304380099001441},
-author = {Gutierrez, Andrew and Yaninek, John and Neuenschwander, Peter and Ellis, C.K.},
+author = {Gutierrez, A.P. and Yaninek, J. and Neuenschwander, P. and Ellis, C.K.},
 keywords = {Cassava, Mealybugs, Metabolic pool, Number and mass dynamics, Metapopulation dynamics, Mites, Parasitoids, Predators, Stochastic simulation},
 abstract = {The metapopulation dynamics of the African cassava food web is explored using a physiologically based tritrophic model. The interacting species are cassava, cassava mealybug and its natural enemies (two parasitoids, a coccinellid predator and a fungal pathogen), and the cassava greenmite and its natural enemies (two predators and a fungal pathogen). The metapopulation model is based on a single patch age-structured population dynamics model reported by Gutierrez et al. (Gutierrez, A.P., Wermelinger, B., Schulthess, F., Baumgärtner, J.U., Herren, H.R., Ellis, C.K., Yaninek, J.S., 1988b. Analysis of biological control of cassava pests in Africa: I. Simulation of carbon nitrogen and water dynamics in cassava. J. Appl. Ecol. 25, 901-920; Gutierrez, A.P., Neuenschwander, P. van Alphen, J.J.M., 1993. Factors affecting the establishment of natural enemies: biological control of the cassava mealybug in West Africa by introduced parasitoids. J. Appl. Ecol. 30, 706-721). The same model simulates the mass number dynamics of each plant or animal species in each patch and the movement of animals between patches. Movement is based on species specific supply–demand relations. The pathogen mortality rate is a simple function of rainfall intensity. The within-patch species composition, their initial densities, and the initial values of edaphic variables may be assigned stochastically. Sensitivity, graphical and multiple linear regression analyses are used to summarize the effects of spatial and resource heterogeneity on species dynamics. Important plant level effects on higher trophic levels are demonstrated, and recommendations are made as to the appropriate model for different ecological studies.}
 }
@@ -182,7 +182,7 @@ @article{meur:2017
 }
 
 @article{guti:2023,
-author = {Gutierrez, Andrew and Sabbatini Peverieri, Giuseppino and Ponti, Luigi and Giovannini, Lucrezia and Roversi, Pio and Mele, Alberto and Pozzebon, Alberto and Scaccini, Davide and Hoelmer, Kim},
+author = {Gutierrez, A.P. and Sabbatini Peverieri, G. and Ponti, L. and Giovannini, L. and Roversi, P. and Mele, A. and Pozzebon, A. and Scaccini, D. and Hoelmer, K.},
 year = {2023},
 month = {04},
 pages = {1-22},
@@ -193,7 +193,7 @@ @article{guti:2023
 }
 
 @article{guti:2020,
-author = {Gutierrez, Andrew and Ponti, Luigi and Kranthi, Keshav and Baumgärtner, Johann and Kenmore, Peter and Gilioli, Gianni and Boggia, Antonio and Cure, José and Rodríguez, Daniel},
+author = {Gutierrez, A.P. and Ponti, L. and Kranthi, K. and Baumgärtner, J. and Kenmore, P. and Gilioli, G. and Boggia, A. and Cure, J. and Rodríguez, D.},
 year = {2020},
 month = {12},
 pages = {139},
@@ -234,7 +234,7 @@ @article{dorm:2012
 }
 
 @inbook{gu-po:2022a,
-author = {Gutierrez, Andrew and Ponti, Luigi},
+author = {Gutierrez, A.P. and Ponti, L.},
 year = {2022},
 month = {12},
 pages = {50-73},
@@ -246,7 +246,7 @@ @inbook{gu-po:2022a
 }
 
 @inbook{gu-po:2022b,
-author = {Gutierrez, Andrew and Ponti, Luigi},
+author = {Gutierrez, A.P. and Ponti, L.},
 year = {2022},
 month = {12},
 pages = {260-281},
@@ -276,7 +276,7 @@ @book{dw-go:1978
 @article{guti:1974,
  ISSN = {00218901, 13652664},
  URL = {http://www.jstor.org/stable/2402002},
- author = {Gutierrez, A. and Nix, H. and Havenstein, D. and Moore, P.},
+ author = {Gutierrez, A.P. and Nix, H. and Havenstein, D. and Moore, P.},
  journal = {Journal of Applied Ecology},
  number = {1},
  pages = {21--35},

paper.md

@@ -51,9 +51,9 @@ The platform `psymple` is designed to facilitate the development of hybrid compl
 
 The development of `psymple` emerged from the complex system modelling requirements of ecological systems. Ecological niche models, also called species distribution models (SDMs) predict species distributions in response to environmental variables such as geographic and climatic features [@el-fr:2017; @el-le:2009]. Classically, these models are formed using correlative or statistical approaches which match observational data to a set of environmental variables to produce favourability ranges for each species.
 
-An alternative approach is mechanistic modelling, which instead of observational data use physiological data to capture the underlying mechanisms which drive species distribution, such as energy balance, population dynamics or response to climate [@ke-po:2009]. Mechanistic SDMs decouple the physiology of a species from their native geography or climate, and allow SDMs in new geographic or climatic regimes to be created in the absence of observational data [@john:2019].
+An alternative approach is mechanistic modelling, which instead of observational data use physiological data to capture the underlying mechanisms which drive species distribution, such as energy balance, population dynamics or response to climate [@ke-po:2009]. In contrast to correlative approaches, mechanistic SDMs decouple the physiology of a species from their native geography or climate, and allow SDMs in new geographic or climatic regimes to be created in the absence of observational data [@john:2019]. 
 
-An example mechanistic framework is physiologically-based demographic modelling (PBDM), which creates holistic ecosystem models based on the weather-driven biology of component species, allowing for predictive phenology, dynamics and distribution assessments, see [@gu-po:2022a; @gu-po:2022b] for an overview and further references. In contrast to correlative approaches, mechanistic modelling such as PBDM can account for tritrophic ecosystem interactions [@g-y-n-e:1999], or model the effects of climate change [@guti:2023].
+An example mechanistic framework is physiologically-based demographic modelling (PBDM), which creates holistic ecosystem models based on the weather-driven biology of component species, allowing for predictive phenology, dynamics and distribution assessments, see [@gu-po:2022a; @gu-po:2022b] for an overview and further references. With this approach, PBDM can account for tritrophic ecosystem interactions [@g-y-n-e:1999], or model the effects of climate change [@guti:2023].
 
 The schools of thought around correlative SDMs and mechanistic frameworks such as PBDM are seen as largely disjoint [@dorm:2012], but their development can be traced back to early common roots [@fi-ni:1970; @guti:1974; @dw-go:1978]. A component of the PBDM framework is the use of physiological data to parametrise "biodemographic" functions capturing biophysical or biochemical mechanisms, such as the development, mortality and fecundity rates of a species in response to environmental variables [@po-gu:2023].
 
@@ -71,7 +71,7 @@ In `psymple`, these ideas are extended to realise functions as ported objects, c
 
 FPOs and VPOs can be arbitrarily combined into composite ported objects (CPOs) through the concepts of directed wires, which capture partial substitution of functional information, and variable wires, which implement the functionality of resource sharing. CPOs can themselves read and expose information from input, output and variable ports to create fully modular and arbitrarily complex hybrid systems of both functional and dynamic components whose nested hierarchy reflects the model structure. 
 
-More generally, `psymple` is built to a specification shared by "next-generation" dynamical systems modelling frameworks, see [@baez:2023], including being *faceted*, where models can be considered one piece at a time; *modular*, where components naturally compose together; and *functorial*, where the data describing the model (its syntax) is systematically and reliably transformed into system behaviour (its semantics). These ideas allow for legible modelling of highly complex, specialised systems, and drive clear, adaptable and accessible modelling practices. For example, modular structures pave the way for cross-platform integrations and promote reuse and flexibility, while an abstracted data structure enables the creation of low- or no-code interfaces to improve utilisation amongst non-specialist users
+More generally, `psymple` is built to a specification shared by "next-generation" dynamical systems modelling frameworks, see [@baez:2023], including being *faceted*, where models can be considered one piece at a time; *modular*, where components naturally compose together; and *functorial*, where the data describing the model (its syntax) is systematically and reliably transformed into system behaviour (its semantics). These ideas allow for legible modelling of highly complex, specialised systems, and drive clear, adaptable and accessible modelling practices. For example, modular structures pave the way for cross-platform integrations and promote reuse and flexibility, while an abstracted data structure enables the creation of low- or no-code interfaces to improve utilisation amongst non-specialist users.
 
 Concretely, the faceted, modular structure provided by ported objects in `psymple` is complemented by a string-based authoring interface which enables no-code authoring from Python dictionary objects or `JSON` file formats. The data defining ported objects is internally processed through a composition process using the Python symbolic mathematics package `sympy` [@meur:2017]. This enables the automatic collection and simplification of equations, the elimination of errors from manually combining complex system equations, and clear inspection of a whole system, or its parts, with automatic outputs including \LaTeX format.