Predicting Oxythermal Stress Conditions for Coldwater Fish in a Northern Wisconsin Lake - Emma A. Blackford; Madeline R. Magee; Paul Block (Environmental Modeling & Assessment, 2026)
This study combines long-term lake water quality data with statistical modeling to predict coldwater fish habitat conditions from a season-ahead lead. We select a case-study lake in Northern Wisconsin with forty-two years of biological, chemical, and physical data to calculate three summertime oxythermal stress metrics for cisco ( Coregonus artedi ) conditioned on bi-weekly temperature and dissolved oxygen profiles. Significant intra-and-interannual variability are identified in seasonal oxythermal habitat conditions. Springtime global and local climate variables generally indicate a stronger relationship than within lake variables when correlated with summertime oxythermal habitat, suggesting a potentially robust link between climate patterns and lake water quality in Wisconsin. Leveraging both climate and within lake springtime observations, a principal component regression approach was applied to probabilistically predict summertime oxythermal stress metrics. Models showed skillful prediction of multiple oxythermal habitat metrics across summer months of highest stress, with the best fitting model achieving R 2 = 0.42 and RPSS = 0.55. We conclude that characterization and tailored season-ahead prediction of oxythermal habitat conditions may provide prospects for proactive lake management in support of coldwater fish habitat.

A classification system for wounds and scars observed on white sharks (Carcharodon carcharias) - Scot D. Anderson; Paul E. Kanive; Taylor K. Chapple; Samantha Andrzejaczek; Barbara A. Block; Salvador J. Jorgensen (Frontiers in Marine Science, 2025)

Connectivity between white shark populations off Central California, USA and Guadalupe Island, Mexico - Paul E. Kanive; Jay J. Rotella; Taylor K. Chapple; Scot D. Anderson; Mauricio Hoyos-Padilla; Abbott Peter Klimley; Felipe Galván-Magaña; Samantha Andrzejaczek; Barbara A. Block; Salvador J. Jorgensen (Frontiers in Marine Science, 2023)
Marine animals often move beyond national borders and exclusive economic zones resulting in a need for trans-boundary management spanning multiple national jurisdictions. Highly migratory fish vulnerable to over-exploitation require protections at international level, as exploitation practices can be disparate between adjacent countries and marine jurisdictions. In this study we collaboratively conducted an analysis of white shark connectivity between two main aggregation regions with independent population assessment and legal protection programs; one off central California, USA and one off Guadalupe Island, Mexico. We acoustically tagged 326 sub-adult and adult white sharks in central California (n=210) and in Guadalupe Island (n=116) with acoustic transmitters between 2008-2019. Of the 326 tagged white sharks, 30 (9.20%) individuals were detected at both regions during the study period. We used a Bayesian implementation of logistic regression with a binomial distribution to estimate the effect of sex, maturity, and tag location to the response variable of probability of moving from one region to the other. While nearly one in ten individuals in our sample were detected in both regions over the study period, the annual rate of trans-regional movement was low (probability of movement = 0.015 yr -1 , 95% credible interval = 0.002, 0.061). Sub-adults were more likely than adults to move between regions and sharks were more likely to move from Guadalupe Island to central California, however, sex, and year were not important factors influencing movement. This first estimation of demographic-specific trans-regional movement connecting US and Mexico aggregations with high seasonal site fidelity represents an important step to future international management and assessment of the northeastern Pacific white shark population as a whole.

Seasonal prediction of crop yields in Ethiopia using an analog approach - Meijian Yang; Guiling Wang; Shu Wu; Paul Block; Rehenuma Lazin; Sarah Alexander; Jonathan Lala; Muhammad Rezaul Haider; Zoi Dokou; Ezana Amdework Atsbeha; Marika Koukoula; Xinyi Shen; et al. (Agricultural and Forest Meteorology, 2023)

Large scale seasonal forecasting of peak season algae metrics in the Midwest and Northeast U.S. - Maxwell R.W. Beal; Grace M. Wilkinson; Paul J. Block (Water Research, 2023)

Rainy season precipitation forecasts in coastal Peru from the North American Multi‐Model Ensemble - Stephen J. Vavrus; Fuyao Wang; Paul Block (International Journal of Climatology, 2022)
We assessed operational forecasts of rainy season (January–March [JFM]) precipitation over coastal Peru from seven global climate models (GCMs) in the North American Multi‐Model Ensemble (NMME) from 1982 to 2020. Similar to previous representations by GCMs, regional climate models, and atmospheric reanalyses, the NMME models produce excessive precipitation (more than twice the observed values) over all of coastal Peru. Based on a set of commonly used metrics (correlation, mean error, RMSE, and Heidke skill score), there is a wide range in accuracy among the NMME models. As expected, forecast accuracy degrades with increasing lead time from December to July, but the rate of decline varies greatly by region and among models. The cause(s) of differences in model performance remains unclear, but resolution does not appear to be a significant factor. Highest forecast skill is found for southwestern Peru (correlations with observations are significant at all lead times), probably because of its stronger relationship between rainfall and El Niño–Southern Oscillation (ENSO), and lowest skill occurs for west‐central Peru (insignificant correlations at all lead times). The distribution of interannual rainy season precipitation in northwestern Peru is extremely positively skewed (skewness >2), making evaluation of forecasts difficult from only a few years that deviate strongly from the mean. Improved correlations with observations across regions are generally found by subsetting multi‐model mean forecasts using the better‐performing models. Over northwest Peru at short lead times, NMME rainy season precipitation forecasts outperform a simple statistical correlation between JFM rainfall and ENSO, but not at longer leads, while NMME's forecasts for west‐central Peru underperform statistical correlations at all lead times. Over southwest Peru, the NMME models are consistently more skilful than a statistical correlation, and these differences become larger with increasing lead time.

Mismatches in scale between highly mobile marine megafauna and marine protected areas - Melinda G. Conners; Nicholas B. Sisson; Pierre D. Agamboue; Philip W. Atkinson; Alastair M. M. Baylis; Scott R. Benson; Barbara A. Block; Steven J. Bograd; Pablo Bordino; W. D. Bowen; Paul Brickle; Ignacio M. Bruno; et al. (Frontiers in Marine Science, 2022)
Marine protected areas (MPAs), particularly large MPAs, are increasing in number and size around the globe in part to facilitate the conservation of marine megafauna under the assumption that large-scale MPAs better align with vagile life histories; however, this alignment is not well established. Using a global tracking dataset from 36 species across five taxa, chosen to reflect the span of home range size in highly mobile marine megafauna, we show most MPAs are too small to encompass complete home ranges of most species. Based on size alone, 40% of existing MPAs could encompass the home ranges of the smallest ranged species, while only < 1% of existing MPAs could encompass those of the largest ranged species. Further, where home ranges and MPAs overlapped in real geographic space, MPAs encompassed < 5% of core areas used by all species. Despite most home ranges of mobile marine megafauna being much larger than existing MPAs, we demonstrate how benefits from MPAs are still likely to accrue by targeting seasonal aggregations and critical life history stages and through other management techniques.

Multi-Decadal High-Resolution Data Reveal the Cryptic Vertical Movement Patterns of a Large Marine Predator Along the Californian Coast - Samantha Andrzejaczek; Taylor K. Chapple; Salvador J. Jorgensen; Scot D. Anderson; Michael Castleton; Paul E. Kanive; Timothy D. White; Barbara A. Block (Frontiers in Marine Science, 2022)
Over the last two decades, satellite tagging of adult and sub-adult white sharks Carcharodon carcharias off the west coast of North America has revealed a predictable onshore-offshore migratory cycle. Our current understanding of the vertical movements exhibited by white sharks while in their coastal foraging phase in the California Current, however, remains limited. Here, we used recovered datasets from 31 archival satellite tags to quantify vertical habitat use. Tags were deployed on individuals between 2000 and 2018 and recorded depth and temperature data at continuous 1–120 s intervals before being recovered up to a year after deployments. Four satellite-tagged individuals were concurrently tagged with acoustic tags, providing precise location data when detected by acoustic receivers that allowed us to explore how reported vertical habitat use varied spatially. While in the coastal shelf waters, white sharks moved at a mean depth ± SD of 14.3 ± 4.0 m and occupied significantly deeper depths during the day than the night. High individual, temporal and spatial variation was evident in vertical movements, while consistent diel and lunar effects emphasized the importance of light-level driving vertical behavior around hunting sites. The vertical movement behaviors reported here provide knowledge of how white sharks may directly and indirectly interact with their mammalian prey in a dynamic three-dimensional system during their capital foraging phase. Temporal patterns in vertical behavior, for instance, indicated that surface waters during early morning hours are the riskiest place for prey. Combining these novel findings with higher-resolution biologging techniques in future studies will allow us to further contextualize fine-scale vertical movement behaviors of white sharks and examine the specific foraging events that could not yet be isolated in the tagging data.

Long‐range reservoir inflow forecasts using large‐scale climate predictors - Amir M. Moradi; Alireza B. Dariane; Guang Yang; Paul Block (International Journal of Climatology, 2020)
Identifying significant large‐scale climate indicators has the potential to improve long‐range streamflow forecasts. In this research, we develop streamflow forecasts for Lake Urmia basin, Iran, specifically for inflow into the Boukan and Mahabad reservoirs. In doing so, two types of inflow forecast models are considered: a single site univariate model ignoring the cross correlation between streamflow at different stations, and a multi‐site multivariate forecast model which takes into consideration the cross correlations among stations. Predictor selection is performed through a principal component analysis and an adaptive‐network‐based fuzzy inference system is used to forecast streamflow. Forecast performance is investigated by employing different combinations of large‐scale climatic information and hydrologic data. We found that gridded ocean‐atmospheric circulation variables, including surface precipitation rate and omega (pressure vertical velocity), have the highest correlations (about 0.7) with annual streamflow. In general, multivariate models are able to better preserve the annual cross‐correlations between streamflow at different stations, as expected, without sacrificing forecast skill as compared to the univariate forecast model approach. Additionally, as compared with the baseline feed‐forward artificial neural network‐ and traditional multiple linear regression‐forecast models, the results were approximately the same. This similarity in the forecast performance between the linear and nonlinear models is likely due to the short of data (44‐sample record).

Development of an interdisciplinary, multi-method approach to seasonal climate forecast communication at the local scale - Sarah Alexander; Ezana Atsbeha; Selam Negatu; Kristen Kirksey; Dominique Brossard; Elizabeth Holzer; Paul Block (Climatic Change, 2020)

Integrating climate prediction and regionalization into an agro-economic model to guide agricultural planning - Ying Zhang; Liangzhi You; Donghoon Lee; Paul Block (Climatic Change, 2020)

Animal-Borne Telemetry: An Integral Component of the Ocean Observing Toolkit - Rob Harcourt; Ana M. M. Sequeira; Xuelei Zhang; Fabien Roquet; Kosei Komatsu; Michelle Heupel; Clive McMahon; Fred Whoriskey; Mark Meekan; Gemma Carroll; Stephanie Brodie; Colin Simpfendorfer; et al. (Frontiers in Marine Science, 2019)

Fungal and herbivore elicitation of the novel maize sesquiterpenoid, zealexin A4, is attenuated by elevated CO2 - Shawn A. Christensen; Alisa Huffaker; James Sims; Charles T. Hunter; Anna Block; Martha M. Vaughan; Denis Willett; Maritza Romero; J. Erik Mylroie; W. Paul Williams; Eric A. Schmelz (Planta, 2018)

Efficacy of tendency and linear inverse models to predict southern Peru's rainy season precipitation - Shu Wu; Michael Notaro; Stephen Vavrus; Eric Mortensen; Rob Montgomery; José de Piérola; Paul Block (International Journal of Climatology, 2018)
Southern Peru receives over 60% of its annual climatological precipitation during the short period of January–March. This rainy season precipitation exhibits strong inter‐annual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. Improving existing seasonal prediction models of summertime precipitation could aid in water resource planning and allocation across this water‐limited region. While various underlying mechanisms modulating inter‐annual variability have been proposed by past studies, operational forecasts continue to be largely based on rudimentary El Niño‐Southern Oscillation (ENSO)‐based indices, such as Niño3.4, justifying further exploration of predictive skill. To bridge the gap between understanding precipitation mechanisms and operational forecasts, we perform systematic studies on the predictability and prediction skill of southern Peru's rainy season precipitation by constructing statistical forecast models using best available weather station and reanalysis data sets. We construct a simple regression model, based on the principal component (PC) tendency of tropical Pacific sea surface temperatures (SST), and a more advanced linear inverse model (LIM), based on the empirical orthogonal functions of tropical Pacific SST and large‐scale atmospheric variables from reanalysis. Our results indicate that both the PC tendency and LIM models consistently outperform the ENSO‐only based regression models in predicting precipitation at both the regional scale and for individual station, with improvements for individual stations ranging from 10 to over 200%. These encouraging results are likely to foster further development of operational precipitation forecasts for southern Peru.

Evaluating the impact and uncertainty of reservoir operation for malaria control as the climate changes in Ethiopia - Julia Reis; Teresa B. Culver; Paul J. Block; Matthew P. McCartney (Climatic Change, 2016)

Estimating apparent survival of sub-adult and adult white sharks (Carcharodon carcharias) in central California using mark-recapture methods - Paul E. Kanive; Jay J. Rotella; Salvador J. Jorgensen; Taylor K. Chapple; Scot D. Anderson; A. Peter Klimley; Barbara A. Block (Frontiers in Marine Science, 2015)

The role of discounting and dynamics in determining the economic efficiency of time-area closures for managing fishery bycatch - Paul R. Armsworth; Barbara A. Block; Josh Eagle; Joan E. Roughgarden (Theoretical Ecology, 2011)

Seasonal influence on heat‐resistant proteolytic capacity of Pseudomonas lundensis and Pseudomonas fragi , predominant milk spoilers isolated from Belgian raw milk samples - Sophie Marchand; Kim Heylen; Winy Messens; Katleen Coudijzer; Paul De Vos; Koen Dewettinck; Lieve Herman; Jan De Block; Marc Heyndrickx (Environmental Microbiology, 2009)
Summary Psychrotolerant bacteria and their heat‐resistant proteases play a major role in the spoilage of UHT‐processed dairy products. Summer and winter raw milk samples were screened for the presence of such bacteria. One hundred and three proteolytic psychrotolerant bacteria were isolated, characterized by API tests, rep‐PCR fingerprint analysis and evaluated for heat‐resistant protease production. Twenty‐nine strains (representing 79% of the complete collection) were further identified by 16S rRNA gene sequencing, rpoB gene sequencing and DNA–DNA hybridizations. A seasonal inter‐ and intra‐species influence on milk spoilage capacity (e.g. growth rate and/or protease production) was demonstrated. Moreover, this polyphasic approach led to the identification of Pseudomonas fragi and Pseudomonas lundensis (representing 53% of all isolates) as predominant producers of heat‐resistant proteases in raw milk. The role of Pseudomonas fluorescens , historically reported as important milk spoiler, could not unequivocally be established. The use of more reliable identification techniques and further revision of the taxonomy of P. fluorescens will probably result in a different perspective on its role in the milk spoilage issue.

Identification of large scale climate patterns affecting snow variability in the eastern United States - Jennifer Morin; Paul Block; Balaji Rajagopalan; Martyn Clark (International Journal of Climatology, 2008)

Forestry Education: Adapting to the Changing Demands on Professionals - V. Alaric Sample; Paul C. Ringgold; Nadine E. Block; James W. Giltmier (Journal of Forestry, 1999)
Recent evolution in the practice of forestry can be linked to changes in the public's perception of sustainability and to developments in science, communications, and global markets. In response, when hiring graduates of professional forestry programs, forestry employers have changed the set of skills and competencies sought. A nationwide survey of forestry employers, educators, and recent graduates, conducted to assess how well US forestry programs are preparing professionals to practice forestry today, reveals some discrepancies between what employers want and what the schools provide. External financial and political pressures on higher education make it difficult for educators to close these gaps, but opportunities exist for employers to help.