Predicting Habitat Suitability for the Endangered Puerto Rican Crested Toad (Peltophryne lemur Cope, 1869): A Comparative Modelling Approach Integrating Ecological and Spatial Forecasts

ABSTRACTSustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.

Synthesizing forecasts to inform decision‐making and advance ecological theory

Comparative approaches to understand metal bioaccumulation in aquatic animals

A comparative modeling approach is proposed for a decision support framework enabling users to have access to different modeling techniques within a single system (depending on the problem and the resources available). Three modeling techniques were selected to illustrate a marketing strategic decision support system: analytic, heuristic, and data-driven. The preimplementation choice and the trade-offs of each technique are explained qualitatively. Subsequently, the unification of the three techniques is discussed, and the benefits and problems associated with the comparative modeling approach are evaluated.

Harnessing seasonal GCM forecasts for crop yield forecasting through multivariate forecast post-processing methods

Habitat suitability models of five keynote Bulgarian Black Sea fish species relative to specific abiotic and biotic factors

Topic modeling and text mining are subsets of natural language processing (NLP) with relevance for conducting meta-analysis (MA) and systematic review (SR). For evidence synthesis, the above NLP methods are conventionally used for topic-specific literature searches or extracting values from reports to automate essential phases of SR and MA. Instead, this work proposes a comparative topic modeling approach to analyze reports of contradictory results on the same general research question. Specifically, the objective is to identify topics exhibiting distinct associations with significant results for an outcome of interest by ranking them according to their proportional occurrence in (and consistency of distribution across) reports of significant effects. Macular degeneration (MD) is a disease that affects millions of people annually, causing vision loss. Augmenting evidence synthesis to provide insight into MD prevention is therefore of central interest in this article. The proposed method was tested on broad-scope studies addressing whether supplemental nutritional compounds significantly benefit macular degeneration. Six compounds were identified as having a particular association with reports of significant results for benefiting MD. Four of these were further supported in terms of effectiveness upon conducting a follow-up literature search for validation (omega-3 fatty acids, copper, zeaxanthin, and nitrates). The two not supported by the follow-up literature search (niacin and molybdenum) also had scores in the lowest range under the proposed scoring system. Results therefore suggest that the proposed method's score for a given topic may be a viable proxy for its degree of association with the outcome of interest, and can be helpful in the systematic search for potentially causal relationships. Further, the compounds identified by the proposed method were not simultaneously captured as salient topics by state-of-the-art topic models that leverage document and word embeddings (Top2Vec) and transformer models (BERTopic). These results underpin the proposed method's potential to add specificity in understanding effects from broad-scope reports, elucidate topics of interest for future research, and guide evidence synthesis in a scalable way. All of this is accomplished while yielding valuable and actionable insights into the prevention of MD.

Context The threatened subspecies of thick-billed grasswren, Amytornis modestus raglessi, occupies dense chenopod shrublands on the lower slopes and peripheral drainages of the North Flinders Ranges, South Australia. A decline in grasswren numbers was observed around 2012, after two preceding years of exceptionally high rainfall. Profound reduction in observed numbers was also evident in 2019, the second successive year of exceptionally hot and dry conditions, in areas where they had previously been numerous. Aims To identify environmental factors influencing A. m. raglessi habitat suitability using habitat suitability modelling, and to better understand possible drivers of grasswren decline and distributional changes between pre- and post-2012 periods. Methods Random forest modelling was used to predict grasswren habitat suitability in response to mapped environmental variables including remotely sensed vegetation, soil and landscape properties. Habitat suitability maps were produced for two separate periods, 1994–2011 and 2012–2023, and compared. An ornithological field survey was undertaken to validate the modelling, and vegetation time-series used to examine areas showing contrasting habitat suitability changes. Key results Mapped soil properties and the minimum green vegetation cover value were the most important habitat suitability predictors. The overall predicted area of habitat (suitability >50%) declined by 25% between the 1994–2011 and 2012–2023 periods. Changes included an expansion of high-suitability habitat in the west, and habitat contraction in south-eastern areas of the distribution. Time-series vegetation data showed that lower bare ground cover and higher non-green vegetation cover occurred in an area with marked reduction in predicted habitat suitability. Conclusions Habitat suitability modelling successfully identified key environmental drivers and demonstrated habitat shifts between periods. Soil properties and minimum green vegetation cover confirmed that water stress responses are fundamental to grasswren distribution. Modelling identified areas of habitat contraction that highlight conservation priorities, while also demonstrating management effectiveness in improving habitat quality. Implications These methods provide spatially explicit guidance for prioritizing conservation efforts for this subspecies and thick-billed grasswrens broadly. Demonstrated habitat improvement following reduced grazing at Witchelina illustrates the practical value of this modeling approach. Such methods are increasingly essential for land managers to understand biodiversity responses and species distributions under climate change.

The aim of this study is to assess, through a comparative modeling approach, the production and energy recovery potential of residual biomethane from the Akouédo landfill, which was closed earlier in 2018, located in Abidjan (Côte d'Ivoire), using the LandGEM 3.02 and IPCC 2006 first-order decay models. The study adopts a comparative approach to estimate the methane volumes generated over a 40-year post-closure period following the official closure of the landfill in 2018. The methodology involves using the physical and mass characteristics of municipal solid waste (MSW) to input into the models and estimate the volumes of biogas produced. The results show that biogas production remains significant for several decades, with a peak of 288 GWh/year in 2019, allowing the installation of a cogeneration plant with a maximum capacity of 24.8 MW according to the IPCC model and 10.8 MW according to the LandGEM model. Even by 2038, the available biogas could generate approximately 6 MW, confirming the long-term viability of a valorization system. This study underscores the importance of modeling post-closure emissions and highlights the urgency of installing an on-site cogeneration unit to capture, valorize, and control residual methane emissions. In the context of sustainable urban development, this approach can reconcile the energy transition, greenhouse gas emission reduction, environmental security of the urban infrastructure, and the development of a sustainable urban environment. This study also highlights the broader implications for energy policy and climate mitigation in Sub-Saharan Africa. By demonstrating the potential for energy recovery from landfill methane, it provides valuable insights into integrating waste-to-energy solutions into sustainable urban development strategies. Such initiatives not only support the energy transition and reduce greenhouse gas emissions but also promote environmental security, offering a comprehensive approach to addressing key challenges in the region's long-term energy and climate goals.

An automated method for constructing 3D models of class II MHC structures that uses constraint logic programming to select side-chain conformations is described. This method follows a comparative modeling approach in basing the model structures on experimentally determined MHC-peptide structures, but it uses constraints to ease open the peptide binding groove so that the modeled MHC structure is a less specific fit for the co-crystallized peptide in the starting structure. The resulting models are used by a "peptide threading" program that attempts to predict peptides from a protein sequence that will bind strongly to particular MHC alleles. Our results indicate that MHC models that have been constructed in this way enable the peptide threading program to make binding predictions that are comparable with those obtained when using experimentally determined MHC structures, suggesting that a combined modeling and peptide threading approach is worth pursuing for MHC molecules for which experimentally determined structures are not available.

The CISNET breast cancer program is a consortium of seven research groups modeling the impact of various cancer interventions on the national trends of breast cancer incidence and mortality. Each of the modeling groups participated in a CISNET breast cancer base case analysis with the objective of assessing the impact of mammography and adjuvant therapy on breast cancer mortality between 1975 and 2000. The comparative modeling approach used to address this question allowed for a unique view into the process of modeling. Results shown here expand on those recently reported in the New England Journal of Medicine (Berry et al., N Engl J Med 2005;353:1784-92) by presenting mortality impact in several different ways to facilitate comparisons between models. Comparisons of each group's results in the context of modeling assumptions made during the process gave insight into how specific model assumptions may have affected the results. The median estimate for the percent decline in breast cancer mortality due to mammography was 15% (range of 8%-23%), and the median estimate for the percent decline in mortality due to adjuvant treatment was 19% (range of 12%-21%). A detailed discussion of the differences in modeling approaches and how those differences may have influenced the mortality results concludes the chapter.

River ecosystems are driven by linked physical, chemical, and biological subsystems, which operate over different temporal and spatial domains. This complexity increases uncertainty in ecological forecasts, and impedes preparation for the ecological consequences of climate change. We describe a recently developed “multi-modeling” system for ecological forecasting in a 7600 km2 watershed in the North American Great Lakes Basin. Using a series of linked land cover, climate, hydrologic, hydraulic, thermal, loading, and biological response models, we examined how changes in both land cover and climate may interact to shape the habitat suitability of river segments for common sport fishes and alter patterns of biological integrity. In scenario-based modeling, both climate and land use change altered multiple ecosystem properties. Because water temperature has a controlling influence on species distributions, sport fishes were overall more sensitive to climate change than to land cover change. However, community-based biological integrity metrics were more sensitive to land use change than climate change; as were nutrient export rates. We discuss the implications of this result for regional preparations for climate change adaptation, and the extent to which the result may be constrained by our modeling methodology.

Ecological forecasting in Chesapeake Bay: Using a mechanistic–empirical modeling approach

List of Tables Acknowledgments Introduction: PART ONE: THE POLITICAL ECONOMY OF PUERTO RICO 1. The Political Economy of Puerto Rico in the Twentieth Century and Puerto Rican Postnational Strategies 2. World Cities in the Caribbean: Miami and San Juan PART TWO: PUERTO RICAN MIGRATION AND THE CARIBBEAN DIASPORA IN THE UNITED STATES 3. Migration and Geopolitics in the Greater Antilles: From the Cold War to the Post--Cold War 4. Puerto Ricans in the United States: A Comparative Approach 5. Coloniality of Power and Racial Dynamics: Notes on a Reinterpretation of Latino Caribbeans in New York City (with Chloe S. Georas) PART THREE: CARIBBEAN COLONIAL MIGRANTS IN WESTERN EUROPE AND THE UNITED STATES 6. Colonial Caribbean Migrations to France, the Netherlands, Great Britain, and the United States 7. Cultural Racism and Colonial Caribbean Migrants in Core Zones of the Capitalist World-Economy Appendix References Index

This article discusses Puerto Rican migration within the broader context of Caribbean migration to the USA. The first part discusses the theoretical framework. The labour market incorporation of migrants is conceptualised as a result of the sociopolitical modes of incorporation which among other dimensions include the US foreign policy/geo‐politics in the region; public opinion/perception of the migrants within the host society; the presence or absence of a migrant community as well as the class/racial composition of the migrants and the the sending country's core‐periphery relationships. The historical origins of Caribbean migration to the USA are examined next, whereupon the article moves on to discuss the class origin of the post‐1960s Caribbean migrants. Modes of incorporation to the host society are then explored, as is the foundational myth that forms part of US narratives about the nation and the way this affected the mode of incorporation for Puerto Ricans. Finally, the article discusses the challenges which the identification strategies adopted by Puerto Ricans pose to traditional conceptualisations of identity. In sum, this article portrays the multiple complex dimensions that determine the Puerto Rican migrants’ particular mode of incorporation in the host society.