Multiscale Analyses of Mammal Species Composition – Environment Relationship in the Contiguous USA

Relationships between species composition and its environmental determinants are a basic objective of ecology. Such relationships are scale dependent, and predictors of species composition typically include variables such as climate, topographic, historical legacies, land uses, human population levels, and random processes. Our objective was to quantify the effect of environmental determinants on U.S. mammal composition at various spatial scales. We found that climate was the predominant factor affecting species composition, and its relative impact increased in correlation with the increase of the spatial scale. Another factor affecting species composition is land-use–land-cover. Our findings showed that its impact decreased as the spatial scale increased. We provide quantitative indication of highly significant effect of climate and land-use–land-cover variables on mammal composition at multiple scales

5. Forest Conservation

Expert assessors Rhett Harrison, Consultative Group on International Agricultural Research, Zambia Keith Kirby, University of Oxford, UK Gillian Petrokofsky, Biodiversity Institute Oxford, UK Rebecca K. Smith, University of Cambridge, UK William J. Sutherland, University of Cambridge, UK Tom Swinfield, Royal Society for the Protection of Birds, UK Scope of assessment: for the conservation of forest habitat (not specific species within forests), including tropical forests, temperate forests, w..

Recognizing animal personhood in compassionate conservation

Compassionate conservation is based on the ethical position that actions taken to protect biodiversity should be guided by compassion for all sentient beings. Critics argue that there are 3 core reasons harming animals is acceptable in conservation programs: the primary purpose of conservation is biodiversity protection; conservation is already compassionate to animals; and conservation should prioritize compassion to humans. We used argument analysis to clarify the values and logics underlying the debate around compassionate conservation. We found that objections to compassionate conservation are expressions of human exceptionalism, the view that humans are of a categorically separate and higher moral status than all other species. In contrast, compassionate conservationists believe that conservation should expand its moral community by recognizing all sentient beings as persons. Personhood, in an ethical sense, implies the individual is owed respect and should not be treated merely as a means to other ends. On scientific and ethical grounds, there are good reasons to extend personhood to sentient animals, particularly in conservation. The moral exclusion or subordination of members of other species legitimates the ongoing manipulation and exploitation of the living worlds, the very reason conservation was needed in the first place. Embracing compassion can help dismantle human exceptionalism, recognize nonhuman personhood, and navigate a more expansive moral space

What Works in Conservation 2018

This book provides an assessment of the effectiveness of 1277 conservation interventions based on summarized scientific evidence. The 2018 edition contains new chapters covering practical global conservation of primates, peatlands, shrublands and heathlands, management of captive animals as well as an extended chapter on control of freshwater invasive species. Other chapters cover global conservation of amphibians, bats, birds and forests, conservation of European farmland biodiversity and some aspects of enhancing natural pest control, enhancing soil fertility and control of freshwater invasive species. It contains key results from the summarized evidence for each conservation intervention and an assessment of the effectiveness of each by international expert panels. The accompanying website www.conservationevidence.com describes each of the studies individually, and provides full references

Integrating data-cleaning with data analysis to enhance usability of biodiversity big-data

Biodiversity big-data (BBD) has the potential to provide answers to some unresolved questions – at spatial and taxonomic swathes that were previously inaccessible. However, BBDs contain serious error and bias. Therefore, any study that uses BBD should ask whether data quality is sufficient to provide a reliable answer to the research question. We propose that the question of data quality and the research question could be addressed simultaneously, by binding data-cleaning to data analysis. The change in signal between the pre- and post-cleaning phases, in addition to the signal itself, can be used to evaluate the findings, their implications, and their robustness. This approach includes five steps: Downloading raw occurrence data from a BBD. Data analysis, statistical and / or simulation modeling in order to answer the research question, using the raw data after the necessary basic cleaning. This part is similar to the common practice. Comprehensive data-cleaning. Repeated data analysis using the cleaned data. Comparing the results of steps 2 and 4 (i.e., before- and after data-cleaning). This comparison will address the issue of data quality, as well as answer the research question itself. The results of step 2 alone may be misleading, due to the error and bias in the data. Even the results of step 4 may not be trustworthy, since data-cleaning is never complete, and some of the error and much bias remain in the data. However, the changes in the results before- and after cleaning are important keys to answer the research question. If cleaned data reveal a stronger and clearer signal than raw data, then the signal is most likely trustworthy, and the respective hypothesis is confirmed. Conversely, if the cleaned data show a weaker signal than obtained from the raw data, then the respective hypothesis, even if confirmed by original data, needs to be rejected. Lastly, if there is a mixed trend, whereby in some cases the signal is stronger and in others it is weaker – the data is probably inadequate and findings cannot be considered conclusive. Thus, we propose that data-cleaning and data analysis should be conducted jointly. We present a case study on the effects of environmental factors on species distribution, using GBIF data of all Australian mammals. We used the performance of a species distribution model (SDM) as a proxy for the strength of environmental factors in determining gradients of species richness. We implemented three different SDM algorithms for 190 species in several different grid cells, that vary in their species richness. We examined the correlations between species richness and 10 different SDM performance indices. Species-environment affinity was weaker in species-rich areas, across all SDM algorithms. The results support the notion that the impact of environmental factors on species distribution at a continental scale decreases with increasing species richness. Seemingly, the results also support the continuum hypothesis, namely that in species-poor areas, species have strong affinities to particular niches, but this structure breaks in species-rich communities. Furthermore, a much stronger signal was revealed after data-cleaning. Thus, a joint study of a research question and data-cleaning provides a more reliable means for using BBDs

Integrating data-cleaning with data analysis to enhance usability of biodiversity big-data

Biodiversity big-data (BBD) has the potential to provide answers to some unresolved questions – at spatial and taxonomic swathes that were previously inaccessible. However, BBDs contain serious error and bias. Therefore, any study that uses BBD should ask whether data quality is sufficient to provide a reliable answer to the research question. We propose that the question of data quality and the research question could be addressed simultaneously, by binding data-cleaning to data analysis. The change in signal between the pre- and post-cleaning phases, in addition to the signal itself, can be used to evaluate the findings, their implications, and their robustness. This approach includes five steps: Downloading raw occurrence data from a BBD. Data analysis, statistical and / or simulation modeling in order to answer the research question, using the raw data after the necessary basic cleaning. This part is similar to the common practice. Comprehensive data-cleaning. Repeated data analysis using the cleaned data. Comparing the results of steps 2 and 4 (i.e., before- and after data-cleaning). This comparison will address the issue of data quality, as well as answer the research question itself.  The results of step 2 alone may be misleading, due to the error and bias in the data. Even the results of step 4 may not be trustworthy, since data-cleaning is never complete, and some of the error and much bias remain in the data. However, the changes in the results before- and after cleaning are important keys to answer the research question. If cleaned data reveal a stronger and clearer signal than raw data, then the signal is most likely trustworthy, and the respective hypothesis is confirmed. Conversely, if the cleaned data show a weaker signal than obtained from the raw data, then the respective hypothesis, even if confirmed by original data, needs to be rejected. Lastly, if there is a mixed trend, whereby in some cases the signal is stronger and in others it is weaker – the data is probably inadequate and findings cannot be considered conclusive. Thus, we propose that data-cleaning and data analysis should be conducted jointly. We present a case study on the effects of environmental factors on species distribution, using GBIF data of all Australian mammals. We used the performance of a species distribution model (SDM) as a proxy for the strength of environmental factors in determining gradients of species richness. We implemented three different SDM algorithms for 190 species in several different grid cells, that vary in their species richness. We examined the correlations between species richness and 10 different SDM performance indices. Species-environment affinity was weaker in species-rich areas, across all SDM algorithms. The results support the notion that the impact of environmental factors on species distribution at a continental scale decreases with increasing species richness. Seemingly, the results also support the continuum hypothesis, namely that in species-poor areas, species have strong affinities to particular niches, but this structure breaks in species-rich communities. Furthermore, a much stronger signal was revealed after data-cleaning. Thus, a joint study of a research question and data-cleaning provides a more reliable means for using BBDs

Conservation planning under uncertainty in urban development and vegetation dynamics

<div><p>Systematic conservation planning is a framework for optimally locating and prioritizing areas for conservation. An often-noted shortcoming of most conservation planning studies is that they do not address future uncertainty. The selection of protected areas that are intended to ensure the long-term persistence of biodiversity is often based on a snapshot of the current situation, ignoring processes such as climate change. Scenarios, in the sense of being accounts of plausible futures, can be utilized to identify conservation area portfolios that are robust to future uncertainty. We compared three approaches for utilizing scenarios in conservation area selection: considering a full set of scenarios (all-scenarios portfolio), assuming the realization of specific scenarios, and a reference strategy based on the current situation (current distributions portfolio). Our objective was to compare the robustness of these approaches in terms of their relative performance across future scenarios. We focused on breeding bird species in Israel’s Mediterranean region. We simulated urban development and vegetation dynamics scenarios 60 years into the future using DINAMICA-EGO, a cellular-automata simulation model. For each scenario, we mapped the target species’ available habitat distribution, identified conservation priority areas using the site-selection software MARXAN, and constructed conservation area portfolios using the three aforementioned strategies. We then assessed portfolio performance based on the number of species for which representation targets were met in each scenario. The all-scenarios portfolio consistently outperformed the other portfolios, and was more robust to ‘errors’ (e.g., when an assumed specific scenario did not occur). On average, the all-scenarios portfolio achieved representation targets for five additional species compared with the current distributions portfolio (approximately 33 versus 28 species). Our findings highlight the importance of considering a broad and meaningful set of scenarios, rather than relying on the current situation, the expected occurrence of specific scenarios, or the worst-case scenario.</p></div