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Building on previous ENETWILD assessments, we updated continental-scale models for European wild ruminants using harmonised occurrence records from the Global Biodiversity Information Facility (GBIF), environmental predictors, hunting yield data and density estimates. Species distribution occurrence models were developed using Maxent for five widespread species (moose Alces alces, roe deer Capreolus capreolus, red deer Cervus elaphus, fallow deer Dama dama and reindeer Rangifer tarandus), while hunting-yield models based on generalized linear models were developed for roe deer and red deer using harmonised European harvest data. Using these outputs, we also model the density of ruminants across Europe, using updated density estimates provided by the European Observatory of Wildlife (EOW) and recent literature. Species distribution models revealed marked differences among the five ungulate species. Generalist species, particularly Capreolus capreolus and Cervus elaphus, showed widespread suitability across central and northern Europe, driven mainly by productivity and heterogeneous land cover. Although habitat suitability was related to roe deer density, the relationship was insufficiently strong to accurately predict density across Europe. Dama dama exhibited more fragmented suitable habitat, largely influenced by altitude. In contrast. Alces alces and Rangifer tarandus were largely restricted to northern Europe, where forest–wetland and tundra habitats predominated. MESS analyses indicated limited extrapolation for generalist species but substantial uncertainty for the two specialists outside their core environmental ranges. Hunting-yield models successfully reproduced broad continental-scale abundance patterns for both roe and red deer, although predictive performance differed between species. Importantly, this study provides the first independent validation of continental hunting-yield models by comparing predicted national hunting yields with reported national harvest statistics, confirming the ability of hunting-yield models to capture broad-scale abundance patterns across Europe. For red deer hunting yields showed a significant relationship with independently estimated densities, supporting their use as a proxy for abundance. In contrast, the lack of calibration for roe deer indicates that additional ecological and management factors are required before hunting yield can be reliably translated into population density for this species.These results demonstrate the value of integrating citizen-science occurrence data, harmonised hunting information and EOW monitoring to provide standardized, continental-scale wildlife indicators supporting wildlife disease surveillance and risk assessment across Europe.

Occurrence Nets (ON) are directed acyclic graphs that represent causality and concurrency information concerning a single execution of a system. Structured Occurrence Nets (SONs) extend ONs by adding new relationships, which provide a means of recording the activities of multiple interacting, and evolving, systems. Although the initial motivations for their development focused on the analysis of system failures, their structure makes them a natural candidate as a model for expressing the execution traces of interacting systems. These traces can then be exhibited as the provenance of the data produced by the systems under observation. In this paper we present a number of patterns that make use of SONs to provide principled modelling of provenance. We discuss some of the benefits of this modelling approach, and briefly compare it with others that have been proposed recently. SON-based modelling of provenance combines simplicity with expressiveness, leading to provenance graphs that capture multiple levels of abstraction in the description of a process execution, are easy to understand and can be analysed using the partial order techniques underpinning their behavioural semantics.

Structured occurrence nets consist of multiple occurrence nets - each recording causality and concurrency in an execution of a component of a concurrent system. These occurrence nets are linked together by means of various types of relationships, aimed at representing dependencies between communicating and evolving sub-systems. In this paper, we investigate causality in the basic class of communication structured occurrence nets cso-nets). We start by introducing the corresponding system-level model of communication structured Place Transition Nets (cspt-nets) which extend Place Transition Nets with an explicit structuring into communicating sub-systems and process interaction based on a combination of synchronous and asynchronous communication. After that we develop a cso-net based process semantics for cspt-nets showing that causality in cso-nets is underpinned by stratified order structures extending causal partial orders with weak causality.

This lecture, in honour of the late Carl Adam Petri, tells of my early interactions with him and summarizes a small sequence of research projects at Newcastle University from 1977 onwards that relate to occurrence nets, ending with a description of a planned new project on “structured occurrence nets”. The areas of actual or planned application include deadlock avoidance, error recovery, atomicity, failure analysis, system synthesis and system verification.

Occurrence Nets (ONs) are directed acyclic graphs that represent causality and concurrency information concerning a single execution of a system. Structured Occurrence Nets (SONs), consist of multiple ONs associated together by means of various types of formal relationship, and are intended for recording information about either (i) the actual or envisaged behaviour of complex systems, as they interact and evolve, or (ii) evidence that is being gathered and analysed concerning the past behaviour of complex evolving systems. The present report is intended as an addition to our prior work, focussed on the problem of supporting the analysis of evidence about the activities of complex (hardware, software and human) systems that were involved in cybercrime, or are implicated in major accidents, situations in which all that is likely to be to hand is incomplete, contradictory and uncertain evidence.

This paper introduces the concept of a `structured occurrence net', which as its name indicates is based on that of an `occurrence net', a well-established formalism for an abstract record that represents causality and concurrency information concerning a single execution of a system. Structured occurrence nets consist of multiple occurrence nets, associated together by means of various types of relationship, and are intended for recording either the actual behaviour of complex systems as they communicate and evolve, or evidence that is being gathered and analysed concerning their alleged past behaviour. We provide a formal basis for the new formalism and show how it can be used to gain better understanding of complex fault-error-failure chains (i) among co-existing communicating systems, (ii) between systems and their sub-systems, and (iii) involving systems that are controlling, creating or modifying other systems. We then go on to discuss how, perhaps using extended versions of existing tools, structured occurrence nets could form a basis for improved techniques of system failure prevention and analysis.

This paper introduces the concept of a ‘structured occurrence net’, which as its name indicates is based on that of an ‘occurrence net’, a well-established formalism for an abstract record that represents causality and concurrency information concerning a single execution of a system. Structured occurrence nets consist of multiple occurrence nets, associated together by means of various types of relationship, and are intended for recording or predicting, either the actual behaviour of complex systems as they communicate and evolve, or evidence that is being gathered and analysed concerning their alleged past behaviour. We provide a formal basis for the new formalism and show how it can be used to gain better understanding of complex fault-error-failure chains (i) among co-existing communicating systems, (ii) between systems and their sub-systems, and (iii) involving systems that are controlling, creating or modifying other systems. We then go on to discuss how, with appropriate tools support, perhaps using extended versions of existing tools, structured occurrence nets could form a basis for improved techniques of system failure prevention and analysis. (This is a revised and significantly extended version of TR-1120.)

The paper describes the design and implementation of an independent, third party contract monitoring service called Contract Compliance Checker (CCC). The CCC is provided with the specification of the contract in force, and is capable of observing and logging the relevant business-to-business (B2B) interaction events, in order to determine whether the actions of the business partners are consistent with the contract. A contract specification language called EROP (for Events, Rights, Obligations and Prohibitions) for the CCC has been developed based on business rules, that provides constructs to specify what rights, obligations and prohibitions become active and inactive after the occurrence of events related to the execution of business operations. The system has been designed to work with B2B industry standards such as ebXML and RosettaNet.

This paper introduces the concept of a `structured occurrence net', which as its name indicates is based on that of an `occurrence net', a well-established formalism for an abstract record that represents causality and concurrency information concerning a single execution of a system. Structured occurrence nets consist of multiple occurrence nets, associated together by means of various types of relationship, and are intended for recording either the actual behaviour of complex systems as they interact and evolve, or evidence that is being gathered and analyzed concerning their alleged past behaviour. We provide a formal basis for the new formalism and show how it can be used to gain better understanding of complex fault-error-failure chains (i) among co-existing interacting systems, (ii) between systems and their sub-systems, and (iii) involving systems that are controlling, supporting, creating or modifying other systems. We then go on to discuss how, perhaps using extended versions of existing tools, structured occurrence nets could form a basis for improved techniques of system failure prevention and analysis.

Membrane systems (with promoters and inhibitors) are a computational model inspired by the way living cells are divided by membranes into compartments where chemical reactions may take place. We consider synchrony and asynchrony between executed reactions in the computations of such systems using Petri nets and their processes as a formal behavioural model. We first discuss different definitions of individual computational steps, and show how they can be rendered within the Petri net domain by assigning all transitions localities corresponding to the compartments, and using activator and inhibitor arcs. The non-sequential semantics of the resulting nets is formalised through processes based on occurrence nets augmented with additional information about localities and activator/inhibitor arcs. Such processes provide a convenient tool for analysing synchrony and asynchrony in the executions of membrane systems and shed light on the causal relationships between the reactions taking place.

We consider a class of Petri nets suitable for the modelling and behavioural analysis of globally asynchronous locally synchronous (GALS) systems. The proposed model of PTL-nets is basically that of Place/Transition-nets (PT-nets) equipped with an explicit notion of locality. Each locality identifies a distinct set of transitions which may only be executed synchronously, i.e. in a maximally concurrent manner. We investigate how to capture the non-sequential semantics of PTL-nets using techniques similar to those used in the standard treatment of PT-nets. As a result, we postulate that processes based on occurrence nets augmented with additional information about localities and enabledness of non-fired transitions can provide a satisfactory basis for a causality semantics of PTL-nets.

GLOBAL OCEAN is an independently developed four-dimensional ocean reconstruction, forecasting, environmental-risk, biological-exposure, and scientific decision-support framework spanning longitude, latitude, depth, and time. The framework integrates physical, chemical, biogeochemical, geochemical, biological, ecological, climatic, and spatiotemporal ocean information within a common global-to-regional architecture. This record presents a pre-specified regional application of GLOBAL OCEAN to the Canadian Northeast Pacific (45–55°N, 145–123°W) for the 2026 forecast period, evaluated relative to a month-matched 1993–2025 GLOBAL OCEAN modeled reference period. GLOBAL OCEAN produced a strong, internally coherent, and highly reproducible multi-variable 2026 regional signal. The framework integrates physical, biogeochemical, and climatic ocean-state information into an extensible multi-layer environmental-risk architecture and a downstream biological habitat-exposure system. The architecture is intentionally extensible rather than defined by a fixed number of risk components, allowing additional governed environmental-risk dimensions to be incorporated as the GLOBAL OCEAN research program develops. The primary scientific result is a persistent dissolved-oxygen deficit at 200–1000 m. Dissolved oxygen is below the month-matched 1993–2025 modeled climatology in every 2026 forecast month. The median standardized oxygen anomaly is approximately −1.25, the maximum absolute standardized anomaly is approximately 1.41, and several forecast months extend beyond the modeled historical range. The primary oxygen result is anchored by strict held-out regional/depth validation for the 200–1000 m layer, with N = 91, R² = 0.9831, and Pearson r = 0.9943, demonstrating very strong predictive agreement for the principal component underlying the regional interpretation. The oxygen signal is accompanied by coherent nutrient and carbonate-system structure. Persistent silicate and phosphate departures occur within the regional depth-resolved analysis, together with a negative deep bicarbonate departure from the month-matched modeled reference state. These chemically distinct model-derived responses provide additional evidence that the 2026 regional signal is multi-variable rather than an isolated single-parameter anomaly. GLOBAL OCEAN extends the reconstructed and forecast ocean state through a governed, extensible environmental-risk architecture that integrates multiple oxygen-state, carbonate-system, buffering, compound, and multistressor dimensions. The public record describes this architecture at the system level rather than fixing it to an internal numerical layer count. This preserves the scientific extensibility of GLOBAL OCEAN while maintaining the governed internal provenance of the individual environmental-risk components. The environmental-risk system is linked downstream to a biological habitat-exposure framework for fish and elasmobranch targets. The underlying GLOBAL OCEAN biological framework contains more than 1.25 million governed habitat–environment matching records across its broader biological system. For the present Canadian Northeast Pacific application, the authenticated regional execution contains 2,784 habitat–environment template rows across 11 biological targets, with regional support spanning all twelve calendar months. Before regional execution, the pre-existing biological-exposure operator was recovered, authenticated, and reproduced exactly against its authoritative reference summaries. The reproduction achieved zero metadata mismatches, zero numerical mismatches, and a maximum absolute numerical difference of 0.0 in the final parity comparison. The regional biological result therefore represents a controlled application of the established GLOBAL OCEAN exposure operator rather than the introduction of a new exposure formula or newly fitted model. The strongest integrated nonthermal multistressor biological-exposure signal occurs for the lanternfish group, with an annual mean exposure index of approximately 2.64, a 90th-percentile value of approximately 3.80, and approximately 55.4% of weighted habitat–environment exposure at or above the pre-existing class-3 threshold. Oxygen-specific exposure components provide complementary ecological structure: skates/rays, Pacific cod, Alaska pollock, Pacific herring, and Pacific sardine show comparatively strong modeled deoxygenation-exposure components. These differences demonstrate that integrated multistressor exposure and oxygen-specific exposure resolve distinct dimensions of the modeled regional environmental-risk field. The 2026 biological-exposure analysis uses the governed nonthermal oxygen–carbon forecast family. Biological exposure is interpreted as modeled habitat–environment co-occurrence within the GLOBAL OCEAN framework. The present record does not convert these exposure indices into claims of observed mortality, population decline, or confirmed ecological damage. The 2026 Canadian Northeast Pacific result is examined within the large-scale climatic context of the developing 2026/27 El Niño. External scientific, governmental, and climate-assessment sources are introduced only after the GLOBAL OCEAN result was frozen and are used solely for independent scientific context, comparison, and process interpretation. They are not used as model inputs, substitutes for GLOBAL OCEAN variables, or post hoc calibration information. Independent Canadian and Northeast Pacific literature provides strong contextual support for the broader regional deoxygenation setting and identifies interacting roles for circulation, ventilation, upwelling, source-water properties, biological oxygen utilization, and climate variability. The persistence of the GLOBAL OCEAN oxygen deficit through the 2026 forecast year, including months preceding the formal establishment of El Niño conditions, indicates that the modeled regional response contains broader ocean-state structure in addition to the later-2026 ENSO context. A dedicated future attribution analysis can formally partition contributions from ENSO, lagged teleconnections, circulation, upwelling, source-water variability, marine-heatwave conditions, longer-timescale deoxygenation, and related regional processes. The principal scientific interpretation of this record is therefore that GLOBAL OCEAN produced a strong, internally coherent, and highly reproducible multi-variable 2026 Canadian Northeast Pacific signal, anchored by a very high-skill independently validated subsurface-to-deep dissolved-oxygen component and reinforced by coherent nutrient, carbonate-system, environmental-risk, and biological-exposure structure. This Zenodo deposit establishes a persistent and independently timestamped scientific record of this Canadian Northeast Pacific 2026 application of GLOBAL OCEAN. It is a distinct regional research object within the continuing GLOBAL OCEAN research program and is scientifically related to previously archived foundational and Pacific regional GLOBAL OCEAN records. This restricted-file deposit contains scientific results and non-enabling research materials. Proprietary GLOBAL OCEAN source code, unreleased algorithms, computational architecture, implementation details, feature-engineering procedures, internal model objects, unpublished enabling technical material, and other protected intellectual property are not included in this record. Access to the deposited files does not grant permission to reproduce, redistribute, modify, adapt, implement, commercialize, sublicense, or create derivative works from the deposited materials or the underlying GLOBAL OCEAN framework without prior written permission from the author. Author and developer: Mehdi HeidariFramework: GLOBAL OCEANRecord scope: Canadian Northeast Pacific 2026 biogeochemical, environmental-risk, and biological-exposure assessmentVersion: v4.0Publication year: 2026