New Hampshire Fish and Game Department

ABSTRACTWe developed a particle tracking model for the transport and dispersion of American lobster (Homarus americanus) larvae for the Gulf of Maine (GoM) that considers both passively drifting larval stages and their swimming ability during the postlarval (pre‐settlement) stage. The model takes into account two characteristics of the postlarval stage: directional swimming toward shallower regions and diel fluctuations of their swimming speed. Diffusivity (lateral mixing), based on surface GPS drifter observations, was also considered and used to validate this model. Numerical experiments, using the hydrodynamics predicted by the Gulf of Maine Operational Forecasting System (GoMOFS) model, were conducted to test the potential impact of postlarval swimming ability on successful settlement on suitable habitat (defined as regions shallower than 20 m). The results of this experiment showed that elevated swimming ability (18 cm/s) in postlarvae significantly improves settlement success compared to trials modeling postlarvae with a diminished swimming ability (7 cm/s), or no swimming ability (control) at all. In contrast, diel swimming behavior in postlarvae slightly decreased successful settlement. In addition, ocean drifter observations and numerical simulations consistently revealed that successful settlement decreases the further the initial release point of larvae is from the coast. Combined, these experiments indicate that both the swimming ability of postlarvae and the distance they are from shore when they hatch are critical factors that influence their potential to reach viable settlement locations in the GoM. These data also suggest that if the GoM continues to warm, and females move further offshore to avoid warmer inshore waters, the settlement success of their larvae may be compromised.

Abstract Regrowth of forests across the northeastern United States in recent decades has allowed for range expansion of many forest‐dependent mammals in the region. However, these contemporary forests have smaller patch sizes, putting humans in closer proximity to previously remote forested areas, and different species compositions and structure compared to historical forests. These conditions pose an increased risk of exposure to anthropogenic disturbances and contaminants for forest‐dwelling wildlife. We evaluated the relationship between contemporary forest conditions and anticoagulant rodenticides (ARs) on recent population trends for fishers (Pekania pennanti). We acquired a snapshot of recent population trends for fishers across New York, Vermont, and New Hampshire, USA, using standardized catch‐per‐unit‐effort (CPUE) data from the regulated fall harvest spanning 2016‐2020, when harvest regulations remained consistent across the states. We used regression models to relate the probability of CPUE increase to measures of forest habitat (age, structure, productivity), length of winter snow cover, and human disturbance (land use patterns and AR detection). The top model indicated that the probability of CPUE increase declined 43% for every 10% increase in the percent of the population exposed to at least 1 AR and by 24% for every 1‐day increase in the length of winter snow cover, while measures of forest conditions had no significant relationship. The relationship between AR detection and suppressed CPUE increase is compelling, as few mammalian studies effectively link the detection of AR residues in animal tissues to population‐level performance in the wild. Nevertheless, longer time frames are required to fully understand population trends and their drivers across heterogeneous landscapes. We recommend the fisher as a model species for longer‐term monitoring of AR detections and for comparing outcomes of measures potentially designed to reduce AR impacts on wildlife in this region.

Abstract Through the end of the century, biodiversity is expected to markedly decline around the world due to climate change, habitat loss, and other factors. Because of this, there is an increasing need for more efficient, effective, and collaborative conservation efforts worldwide. While many global and national threat assessment databases exist, they may only have limited utility at regional and local scales where on-the-ground conservation is best implemented. To better identify species risk at a more regional scale, the 14 state fish and wildlife agencies in the northeastern United States worked with taxonomic experts across the Northeast to create the most recent (2023) list of Regional Species of Greatest Conservation Need (RSGCN). We compared a preliminary list compiled from global and national datasets to the finalized list after the process documented here. We found that the global and national datasets only identified ∼55% of the 806 species statuses accurately for the region, demonstrating the importance of current local and regional expertise and data. The process of identifying RSGCN for the northeastern U.S. has resulted in specific actionable information leading to beneficial outcomes, such as dedicated funding for regional initiatives, data-sharing, and coordination among regional, state, and local conservation organizations - enabling beneficial conservation outcomes across the region for many of the species and habitats of the region. The methods described may serve as a framework for other regions to help identify conservation targets using the best available and more localized landscape- and watershed-scale information.

Abstract It is now well-recognized that urbanization strongly impacts wildlife communities and populations. However, we typically do not know which feature(s) affect individual species most strongly, and this lack of understanding impedes theory development and effective planning for conservation and management goals. To address this knowledge gap, we evaluated how the abundance of ten mammal species responded to six different features of urbanization quantified at five spatial scales using data from 112 camera traps deployed for two years across a gradient of urbanization in New Hampshire, USA. We fit Bayesian abundance models to measure response to each feature and scale. There was no singular urban feature or spatial scale in the best model for all species. Rather, species responded uniquely to features across scales, and the scale of urban features in the best model also varied. Within a species, the magnitude and direction of response varied across features and scales, with only black bear (Ursus americanus), gray fox (Urocyon cinereoargenteus), and Virginia opossum (Didelphis virginiana) exhibiting a consistently significant unidirectional relationship with a single feature across all scales. Our results emphasize that species respond to specific urban features, thus a failure to include certain features can cause misleading inference about wildlife response to “urbanization”. Therefore, researchers must carefully justify the choice of urban feature and spatial scale at which it is represented for each species of interest. An expanded inclusion of multiple urban features in wildlife research will inform management decisions and help attain conservation goals for species impacted by urbanization.

The American lobster, Homarus americanus, supports the most valuable single-species fishery in North America; however to-date, a reliable and robust method to determine age does not exist, and thus some of the more traditional catch-at-age stock assessment methods cannot be used to determine status. In lieu of this, the Atlantic States Marine Fisheries Commission’s American Lobster Stock Assessment Model uses a probabilistic growth transition matrix to determine how lobsters of different sizes will grow and recruit into the fishery. Developing and updating the growth transition matrix to reflect current growth dynamics requires estimates of molt increment and interval. Tagging studies are an inexpensive method to collect this type of information, but relying on fishing industry reported recapture data and buy-in can be challenging, resulting in varied return rates and uncertainty in measurement accuracy. Here, we report on a subset of recaptured lobsters released in the Gulf of Maine and Georges Bank regions between 2015 and 2020, where in addition to recapture locations, harvesters were encouraged to submit images of recaptured lobsters alongside a standard lobster gauge as a scale to estimate carapace length. Some fishermen were able to provide both direct measurements and images. Images were analyzed in ImageJ to estimate individual lobster carapace length (CL); and each image was assigned a quality score. For all groups, image-derived lengths were correlated with measured observations, regardless of the overall image quality, with only a slight underestimation at the maximum end of CL. This image-based method for length estimation provides high-quality length predictions regardless of image quality and can significantly increase the likelihood of harvesters contributing data with broad potential to engage commercial fisherman in collaborative science. Using this method we were able to expand the dataset of length records from this project by 251 or 33 %, providing additional data at a rate similar to tagging an additional 6000 lobsters.

Declining oxygen concentrations in the deep waters of lakes worldwide pose a pressing environmental and societal challenge. Existing theory suggests that low deep-water dissolved oxygen (DO) concentrations could trigger a positive feedback through which anoxia (i.e., very low DO) during a given summer begets increasingly severe occurrences of anoxia in following summers. Specifically, anoxic conditions can promote nutrient release from sediments, thereby stimulating phytoplankton growth, and subsequent phytoplankton decomposition can fuel heterotrophic respiration, resulting in increased spatial extent and duration of anoxia. However, while the individual relationships in this feedback are well established, to our knowledge, there has not been a systematic analysis within or across lakes that simultaneously demonstrates all of the mechanisms necessary to produce a positive feedback that reinforces anoxia. Here, we compiled data from 656 widespread temperate lakes and reservoirs to analyze the proposed anoxia begets anoxia feedback. Lakes in the dataset span a broad range of surface area (1-126,909 ha), maximum depth (6-370 m), and morphometry, with a median time-series duration of 30 years at each lake. Using linear mixed models, we found support for each of the positive feedback relationships between anoxia, phosphorus concentrations, chlorophyll a concentrations, and oxygen demand across the 656-lake dataset. Likewise, we found further support for these relationships by analyzing time-series data from individual lakes. Our results indicate that the strength of these feedback relationships may vary with lake-specific characteristics: For example, we found that surface phosphorus concentrations were more positively associated with chlorophyll a in high-phosphorus lakes, and oxygen demand had a stronger influence on the extent of anoxia in deep lakes. Taken together, these results support the existence of a positive feedback that could magnify the effects of climate change and other anthropogenic pressures driving the development of anoxia in lakes around the world.

Climate change is likely to have a dramatic impact on many animals, including American lobsters (Homarus americanus). Lobsters are very sensitive to water temperature, and they will move to avoid water that is too warm or too cold. While this behavior has served them well for 1,000’s of years, as the oceans warm up, they might seek new habitats, which might disrupt aspects of their life cycle and lead to a decline in their numbers. In this article, we aim to explain some facts about American lobsters and tell you about several ways that climate change might have an impact on these fascinating and valuable creatures.

Abstract Anticoagulant rodenticides (ARs) are increasingly recognized as a threat to non-target species including native wildlife. Fishers (Pekania pennanti) are generally considered deep forest inhabitants that are not expected to have high exposure to ARs. To evaluate the distribution and levels of ARs in fishers, we analyzed liver samples from fisher carcasses (N = 45) opportunistically trapped across Vermont and New Hampshire between 2018 and 2019. Liquid chromatography-mass spectrometry was used to detect and quantify 11 different ARs in the liver tissue of each fisher at the time of trapping. All but one sample analyzed were positive for exposure to ARs, and 84% were positive for more than one type of AR. The most prevalent ARs detected were diphacinone (96%) and brodifacoum (80%). No samples had detectable levels of coumachlor, coumafuryl, difenacoum, pinodone, or warfarin. These results are mostly consistent with findings for fishers in the Pacific Northwest as well as with a variety of rodent specializing avifauna throughout the Northeastern USA, but show a higher prevalence of exposure and a different distribution of AR types than other studies. These results help establish current baseline exposure to ARs in fishers in the Northeast USA and suggest that ARs could pose a threat to wild mesocarnivore species in this region.

The Jemez Mountains salamander (Plethodon neomexicanus; hereafter JMS) is an endangered salamander restricted to the Jemez Mountains in north‐central New Mexico, United States. This strictly terrestrial and lungless species requires moist surface conditions for activities such as mating and foraging. Threats to its current habitat include fire suppression and ensuing severe fires, changes in forest composition, habitat fragmentation, and climate change. Forest composition changes resulting from reduced fire frequency and increased tree density suggest that its current aboveground habitat does not mirror its historically successful habitat regime. However, because of its limited habitat area and underground behavior, we hypothesized that geology and topography might play a significant role in the current distribution of the salamander. We modeled the distribution of the JMS using a machine learning algorithm to assess how geology, topography, and climate variables influence its distribution. The best habitat suitability model indicates that geology type and maximum winter temperature (November to March) were most important in predicting the distribution of the salamander (23.5% and 50.3% permutation importance, respectively). Minimum winter temperature was also an important variable (21.4%), suggesting this also plays a role in salamander habitat. Our habitat suitability map reveals low uncertainty in model predictions, and we found slight discrepancies between the designated critical habitat and the most suitable areas for the JMS. Because geological features are important to its distribution, we recommend that geological and topographical data are considered, both during survey design and in the description of localities of JMS records once detected.