Aquilegia, Vol. 37 No. 5, Fall 2013, Newsletter of the Colorado Native Plant Society

https://epublications.regis.edu/aquilegia/1145/thumbnail.jp

An Analysis of Public Attitudes Toward the Insanity Defense

Results from a public opinion survey of knowledge, attitudes, and support for the insanity defense indicate that people dislike the insanity defense for both retributive and utilitarian reasons: they want insane law-breakers punished, and they believe that insanity defense procedures fail to protect the public. However, people vastly overestimate the use and success of the insanity plea. Several attitudinal and demographic variables that other researchers have found to be associated with people\u27s support for the death penalty and perceptions of criminal sentencing are also related to support for the insanity defense. Implications for public policy are discussed

Changes in the acoustic structure of Australian bird communities along a habitat complexity gradient

Avian vocalizations have evolved in response to a variety of abiotic and biotic selective pressures. While there is some support for signal convergence in similar habitats that is attributed to adaptation to the acoustic properties of the environment (the 'acoustic adaptation hypothesis', AAH), there is also evidence for character displacement as a result of competition for signal space among coexisting species (the 'acoustic niche partitioning hypothesis'). We explored the acoustic space of avian assemblages distributed along six different habitat types (from herbaceous habitats to warm rainforests) in southeastern Queensland, Australia. We employed three acoustic diversity indices (acoustic richness, evenness, and divergence) to characterize the signal space. In addition, we quantified the phylogenetic and morphological structure (in terms of both body mass and beak size) of each community. Acoustic parameters showed a moderately low phylogenetic signal, indicating labile evolution. Although, we did not find meaningful differences in acoustic diversity indices among habitat categories, there was a significant relationship between the regularity component (evenness) and vegetation height indicating that acoustic signals are more evenly distributed in dense habitats. After accounting for differences in species richness, the volume of acoustic space (i.e., acoustic richness) decreased as the level of phylogenetic and morphological resemblance among species in a given community increased. Additionally, we found a significantly negative relationship between acoustic divergence and divergence in body mass indicating that the less different species are in their body mass, the more different their songs are likely to be. This implies the existence of acoustic niche partitioning at the community level. Overall, while we found mixed support for the AAH, our results suggest that community-level effects may play a role in structuring acoustic signals within avian communities in this region. This study shows that signal diversity estimated by diversity metrics of community ecology based on basic acoustic parameters can provide additional insight into the structure of animal vocalizations.Funding provided by: Ministerio de Ciencia e InnovaciónCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004837Award Number: PID2021-123304NA-I0

The potential for acoustic individual identification in mammals

Many studies have revealed that animal vocalizations, including those from mammals, are individually distinctive. Therefore, acoustic identification of individuals (AIID) has been repeatedly suggested as a non-invasive and labor efficient alternative to mark-recapture identification methods. We present a pipeline of steps for successful AIID in a given species. By conducting such work, we will also improve our understanding of identity signals in general. Strong and stable acoustic signatures are necessary for successful AIID. We reviewed studies of individual variation in mammalian vocalizations as well as pilot studies using acoustic identification to census mammals and birds. We found the greatest potential for AIID (characterized by strong and stable acoustic signatures) was in Cetacea and Primates (including humans). In species with weaker acoustic signatures, AIID could still be a valuable tool once its limitations are fully acknowledged. A major obstacle for widespread utilization of AIID is the absence of tools integrating all AIID subtasks within a single package. Automation of AIID could be achieved with the use of advanced machine learning techniques inspired by those used in human speaker recognition or tailored to specific challenges of animal AIID. Unfortunately, further progress in this area is currently hindered by the lack of appropriate publicly available datasets. However, we believe that after overcoming the issues outlined above, AIID can quickly become a widespread and valuable tool in field research and conservation of mammals and other animals

The potential for acoustic individual identification in mammals

Many studies have revealed that animal vocalizations, including those from mammals, are individually distinctive. Therefore, acoustic identification of individuals (AIID) has been repeatedly suggested as a non-invasive and labor efficient alternative to mark-recapture identification methods. We present a pipeline of steps for successful AIID in a given species. By conducting such work, we will also improve our understanding of identity signals in general. Strong and stable acoustic signatures are necessary for successful AIID. We reviewed studies of individual variation in mammalian vocalizations as well as pilot studies using acoustic identification to census mammals and birds. We found the greatest potential for AIID (characterized by strong and stable acoustic signatures) was in Cetacea and Primates (including humans). In species with weaker acoustic signatures, AIID could still be a valuable tool once its limitations are fully acknowledged. A major obstacle for widespread utilization of AIID is the absence of tools integrating all AIID subtasks within a single package. Automation of AIID could be achieved with the use of advanced machine learning techniques inspired by those used in human speaker recognition or tailored to specific challenges of animal AIID. Unfortunately, further progress in this area is currently hindered by the lack of appropriate publicly available datasets. However, we believe that after overcoming the issues outlined above, AIID can quickly become a widespread and valuable tool in field research and conservation of mammals and other animals