EVALUATION OF A NEW DEER REPELLENT ON JAPANESE YEWS AT SUBURBAN HOMESTEADS

Jersey, an experimental deer repellent, was field tested against 2 commercial repellents on Japanese yews (Taxuscuspidata) near Ithaca, New York, during spring 1990. In Experiment 1, plots (nnn = 24) of 4 individually-potted yews were established, with 2 yews at each plot randomly treated with Jersey and 2 left as controls. Plots of 4 (1 x 4, n nn =12) and 16 (4 x 4, nnn = 2) plants were used in Experiment 2, with individual plants being treated with Jersey, Hinder”, or Big Game RepellentR (BGRR) or left as controls. Photographs with a grid matrix placed behind each h plant were taken from 2 m at the beginning of the experiment and after 10 weeks. These photographs were analyzed to produce a cover index of plant size. Plots were monitored weekly to record browsing. In Experiment 1 more control (46/48) than treated (7/48)plants were browsed (P \u3c 0.001). Controls were browsed earlier (x =1.7 wk) than treated yews (x = 4.4 wk, P \u3c 0.01). At the end of 10 weeks, control plants were reduced in size more than Jersey-treated plants (P \u3c 0.001). In Experiment 2, browsing rates did not differ among treatments in the 1 x 4 plots or 4 x 4 plots. However, controls were browsed more frequently than treated at both plot types (10/12 at 1 x 4, and 6/8 at 4 x 4 plots) (P \u3c 0.05). Browsing reduced control plants by 56.8% (n =10) in 1 x 4 plots and 47.2% (n = 6) in 4 x 4 plots. These results suggest that Jersey reduced deer damage to a shrub preferred by deer. Moreover, Jersey was as effective as BGRR and Hinder at reducing browsing. Experiments may need to be conducted under more severe conditions and over a longer time-period to separate efficacy of the 3 repellents

EVALUATION OF A NEW DEER REPELLENT ON JAPANESE YEWS AT SUBURBAN HOMESTEADS

Jersey, an experimental deer repellent, was field tested against 2 commercial repellents on Japanese yews (Taxuscuspidata) near Ithaca, New York, during spring 1990. In Experiment 1, plots (nnn = 24) of 4 individually-potted yews were established, with 2 yews at each plot randomly treated with Jersey and 2 left as controls. Plots of 4 (1 x 4, n nn =12) and 16 (4 x 4, nnn = 2) plants were used in Experiment 2, with individual plants being treated with Jersey, Hinder”, or Big Game RepellentR (BGRR) or left as controls. Photographs with a grid matrix placed behind each h plant were taken from 2 m at the beginning of the experiment and after 10 weeks. These photographs were analyzed to produce a cover index of plant size. Plots were monitored weekly to record browsing. In Experiment 1 more control (46/48) than treated (7/48)plants were browsed (P \u3c 0.001). Controls were browsed earlier (x =1.7 wk) than treated yews (x = 4.4 wk, P \u3c 0.01). At the end of 10 weeks, control plants were reduced in size more than Jersey-treated plants (P \u3c 0.001). In Experiment 2, browsing rates did not differ among treatments in the 1 x 4 plots or 4 x 4 plots. However, controls were browsed more frequently than treated at both plot types (10/12 at 1 x 4, and 6/8 at 4 x 4 plots) (P \u3c 0.05). Browsing reduced control plants by 56.8% (n =10) in 1 x 4 plots and 47.2% (n = 6) in 4 x 4 plots. These results suggest that Jersey reduced deer damage to a shrub preferred by deer. Moreover, Jersey was as effective as BGRR and Hinder at reducing browsing. Experiments may need to be conducted under more severe conditions and over a longer time-period to separate efficacy of the 3 repellents

Evaluation of a New Deer Repellent on Japanese Yews at Suburban Homesites

Jersey, an experimental deer repellent, was field tested against 2 commercial repellents on Japanese yews (Taxus cuspidata) near Ithaca, New York, during spring 1990. In Experiment 1, plots (n = 24) of 4 individually-potted yews were established, with 2 yews at each plot randomly treated with Jersey and 2 left as controls. Plots of 4 (1 x 4, n = 12) and 16 (4 x 4, n = 2) plants were used in Experiment 2, with individual plants being treated with Jersey, HinderR, or Big Game RepellentR (BGRR) or left as controls. Photographs with a grid matrix placed behind each plant were taken from 2 mat the beginning of the experiment and after 10 weeks. These photographs were analyzed to produce a cover index of plant size. Plots were monitored weekly to record browsing. In Experiment 1 more control (46/48) than treated (7/48) plants were browsed (P \u3c 0.001). Controls were browsed earlier (i = 1.7 wk) than treated yews (i = 4.4 wk, P \u3c 0.01). At the end of 10 weeks, control plants were reduced in size more than Jersey-treated plants (P ≤ 0.001). In Experiment 2, browsing rates did not differ among treatments in the 1 x 4 plots or 4 x 4 plots. However, controls were browsed more frequently than treated at both plot types (10/12 at 1 x 4, and 6/8 at 4 x 4 plots) (P \u3c 0.05). Browsing reduced control plants by 56.8% (n = 10) in 1 x 4 plots and 47.2% (n = 6) in 4 x 4 plots. These results suggest that Jersey reduced deer damage to a shrub preferred by deer. Moreover, Jersey was as effective as BGRR and HinderR at reducing browsing. Experiments may need to be conducted under more severe conditions and over a longer time-period to separate efficacy of the 3 repellents

Extent and Nature of Deer Damage to Commercial Nurseries in New York

We surveyed nursery producers in New York to determine the extent, nature and economic impact of deer damage to their operations, and to assess their attitudes towards deer. Seventy-three percent of the producers experienced deer damage to their crops in 1988. Average costs for replacement were nearly $6,000 per grower for those reporting damage estimates (and over$8,000 if 1 extreme value was included). Statewide damage estimates ranged from $500,000 to$1.2 million (depending on assumptions). Forty-six percent used damage control, which cost an average of about $2,000 per grower. More than 80% of the producers were classified as nonaccepting of deer damage and deer populations. We also reviewed several deer damage studies to compare economic and attitudinal impacts of deer damage to various agricultural constituencies. Nursery producers, orchardists, and Christmas tree growers appear to incur the greatest per capita deer damage costs. Of agriculturists, nursery producers and orchardists appear to be the least accepting of deer and deer damage. Deer managers and policy makers may need to consider the nursery producers in the same at risk category as orchardists

Enhancing Pace’s Informatics Platform though the Fine Arts Digital Photo Program.

To enable the enhancement of our digital photography platform: specifically, the purchase of four high-end digital single-lens-reflex cameras, twelve lesser digital cameras, a laptop with and software capable of working with large digital files, and various supplementary hardware and software. This equipment would expand the Thinkfinity platform by: 1) Exposing both Pace University students and faculty to the most up-to-date technology available, the same equipment used by professional photographers both in the contemporary fine arts and commercial fields. 2) Create an interdisciplinary informatics platform at Pace University. 3) Allowing the Fine Arts Department to create programs that introduce K-12 students to digital photography through creative teaching and community engagement

Human populations in the world's mountains: Spatio-temporal patterns and potential controls.

Changing climate and human demographics in the world's mountains will have increasingly profound environmental and societal consequences across all elevations. Quantifying current human populations in and near mountains is crucial to ensure that any interventions in these complex social-ecological systems are appropriately resourced, and that valuable ecosystems are effectively protected. However, comprehensive and reproducible analyses on this subject are lacking. Here, we develop and implement an open workflow to quantify the sensitivity of mountain population estimates over recent decades, both globally and for several sets of relevant reporting regions, to alternative input dataset combinations. Relationships between mean population density and several potential environmental covariates are also explored across elevational bands within individual mountain regions (i.e. "sub-mountain range scale"). Globally, mountain population estimates vary greatly-from 0.344 billion (31%) in 2015. A more detailed analysis using one of the population datasets (GHS-POP) revealed that in ∼35% of mountain sub-regions, population increased at least twofold over the 40-year period 1975-2015. The urban proportion of the total mountain population in 2015 ranged from 6% to 39%, depending on the combination of population and urban extent datasets used. At sub-mountain range scale, population density was found to be more strongly associated with climatic than with topographic and protected-area variables, and these relationships appear to have strengthened slightly over time. Such insights may contribute to improved predictions of future mountain population distributions under scenarios of future climatic and demographic change. Overall, our work emphasizes that irrespective of data choices, substantial human populations are likely to be directly affected by-and themselves affect-mountainous environmental and ecological change. It thereby further underlines the urgency with which the multitudinous challenges concerning the interactions between mountain climate and human societies under change must be tackled

A New High-Resolution Map of World Mountains and an Online Tool for Visualizing and Comparing

Answers to the seemingly straightforward questions “what is a mountain?” and “where are the mountains of the world?” are in fact quite complex, and there have been few attempts to map the mountains of the earth in a consistent and rigorous fashion. However, knowing exactly where mountain ecosystems are distributed on the planet is a precursor to conserving them, as called for in Sustainable Development Goals 6 and 15 of the United Nations 2030 Agenda for Sustainable Development. In this article we first compare 3 characterizations of global mountain distributions, including a new, high-resolution (250 m) map of global mountains derived from terrain characteristics. We show how differences in conceptual definition, methodology, and spatial resolution of source data can result in differences in the extent and location of lands classed as mountains. For example, the new 250-m resource documents a larger global mountain extent than previous characterizations, although it excludes plateaus, hilly forelands, and other landforms that are often considered part of mountain areas. We then introduce the Global Mountain Explorer, a new web-based application specifically developed for exploration, visualization, and comparison of these maps. This new open-access tool is an intuitive and versatile resource suitable for a broad range of users and applications

Bioregions in marine environments: Combining Biological and Environmental Data for Management and Scientific Understanding

Bioregions are important tools for understanding and managing natural resources. Bioregions should describe locations of relatively homogenous assemblages of species occur, enabling managers to better regulate activities that might affect these assemblages. Many existing bioregionalization approaches, which rely on expert-derived, Delphic comparisons or environmental surrogates, do not explicitly include observed biological data in such analyses. We highlight that, for bioregionalizations to be useful and reliable for systems scientists and managers, the bioregionalizations need to be based on biological data; to include an easily understood assessment of uncertainty, preferably in a spatial format matching the bioregions; and to be scientifically transparent and reproducible. Statistical models provide a scientifically robust, transparent, and interpretable approach for ensuring that bioregions are formed on the basis of observed biological and physical data. Using statistically derived bioregions provides a repeatable framework for the spatial representation of biodiversity at multiple spatial scales. This results in better-informed management decisions and biodiversity conservation outcomes.Peer reviewe

An Assessment of the Representation of Ecosystems in Global Protected Areas Using New Maps of World Climate Regions and World Ecosystems

Representation of ecosystems in protected area networks and conservation strategies is a core principle of global conservation priority setting approaches and a commitment in Aichi Target 11 of the Convention on Biological Diversity. The 2030 Sustainable Development Goals (SDGs) explicitly call for the conservation of terrestrial, freshwater, and marine ecosystems. Accurate ecosystem distribution maps are required to assess representation of ecosystems in protected areas, but standardized, high spatial resolution, and globally comprehensive ecosystem maps have heretofore been lacking. While macroscale global ecoregions maps have been used in global conservation priority setting exercises, they do not identify distinct localized ecosystems at the occurrence (patch) level, and instead describe large ecologically meaningful areas within which additional conservation planning and management are necessary. We describe a new set of maps of globally consistent climate regions and ecosystems at a much finer spatial resolution (250 m) than existing ecological regionalizations. We then describe a global gap analysis of the representation of these ecosystems in protected areas. The new map of terrestrial World Ecosystems was derived from the objective development and integration of 1) global temperature domains, 2) global moisture domains, 3) global landforms, and 4) 2015 global vegetation and land use. These new terrestrial World Ecosystems do not include either freshwater or marine ecosystems, but analog products for the freshwater and marine domains are in development. A total of 431 World Ecosystems were identified, and of these a total of 278 units were natural or semi-natural vegetation/environment combinations, including different kinds of forestlands, shrublands, grasslands, bare areas, and ice/snow regions. The remaining classes were different kinds of croplands and settlements. Of the 278 natural and semi-natural classes, 9 were not represented in global protected areas with a strict biodiversity conservation management objective (IUCN management categories I-IV), and an additional 206 were less than 8.5% protected (half way to the 17% Aichi Target 11 goal). Forty four classes were between 8.5% and 17% protected (more than half way towards the Aichi 17% target), and only 19 classes exceeded the 17% Aichi target. However, when all protected areas (IUCN management categories I-VI plus protected areas with no IUCN designation) were included in a separate global gap analysis, representation of ecosystems increases substantially, with a third of the ecosystems exceeding the 17% Aichi target, and another third between 8.5% and 17%. The overall protection (representation) of global ecosystems in protected areas is considerably less when assessed using only strictly conserved protected areas, and more if all protected areas are included in the analysis. Protected area effectiveness should be included in further evaluations of global ecosystem protection. The ecosystems with the highest representation in protected areas were often bare or sparsely vegetated and found in inhospitable environments (e.g. cold mountains, deserts), and the eight most protected ecosystems were all snow and ice ecosystems. In addition to the global gap analysis of World Ecosystems in protected areas, we report on the representation results for the ecosystems in each biogeographic realm (Neotropical, Nearctic, Afrotropical, Palearctic, Indomalayan, Australasian, and Oceania)