Six Common Mistakes in Conservation Priority Setting

A vast number of prioritization schemes have been developed to help conservation navigate tough decisions about the allocation of finite resources. However, the application of quantitative approaches to setting priorities in conservation frequently includes mistakes that can undermine their authors’ intention to be more rigorous and scientific in the way priorities are established and resources allocated. Drawing on well‐established principles of decision science, we highlight 6 mistakes commonly associated with setting priorities for conservation: not acknowledging conservation plans are prioritizations; trying to solve an ill‐defined problem; not prioritizing actions; arbitrariness; hidden value judgments; and not acknowledging risk of failure. We explain these mistakes and offer a path to help conservation planners avoid making the same mistakes in future prioritizations. Seis Errores Comunes en la Definición de Prioridades de Conservación     

Knowledge Production and Learning for Sustainable Landscapes: Seven Steps Using Social–Ecological Systems as Laboratories

There are multiple challenges regarding use and governance of landscapes’ goods, functions and intangible values for ecosystem health and human well-being. One group of challenges is to measure and assess principal sustainability dimensions through performance targets, so stakeholders have transparent information about states and trends. Another group is to develop adaptive governance at multiple levels, and management of larger geographical areas across scales. Addressing these challenges, we present a framework for transdisciplinary research using multiple landscapes as place-based case studies that integrates multiple research disciplines and non-academic actors: (1) identify a suite of landscapes, and for each (2) review landscape history, (3) map stakeholders, use and non-use values, products and land use, (4) analyze institutions, policies and the system of governance, (5) measure ecological, economic, social and cultural sustainability, (6) assess sustainability dimensions and governance, and finally (7) make comparisons and synthesize. Collaboration, communication and dissemination are additional core features. We discuss barriers bridges and bridges for applying this approach.     

Fact and value in invasion biology

Some invasion biologists contend their science has reached a consensus on 4 facts: cost estimates of the effects of nonindigenous species provided in papers by Pimentel et al. are credible; invasive species generally, not just predators, pose significant extinction threats; characteristic biological differences distinguish novel from native species, ecosystems, communities, and processes; and ontological dualism, which distinguishes between natural and anthropogenic processes and influences, plays a useful role in biological inquiry. I contend there is no convincing empirical evidence for any of these propositions. Leading invasion biologists cite their agreement about these propositions as evidence for them and impugn the motives of critics who believe consensus should be based on evidence not the other way around.     

Effects of stewardship on protected area effectiveness for coastal birds

Evaluation of protected area effectiveness is critical for conservation of biodiversity. Protected areas that prioritize biodiversity conservation are, optimally, located and managed in ways that support relatively large and stable or increasing wildlife populations. Yet evaluating conservation efficacy remains a challenging endeavor. We used an extensive community science data set, eBird, to evaluate the efficacy of protected areas for birds across the Gulf of Mexico and Atlantic coasts of the United States. We modeled trends (2007–2018) for 12 vulnerable waterbirds that use coastal areas during breeding or wintering. We compared two types of protected areas—sites where conservation organizations implemented active stewardship or management or both to reduce human disturbance (hereafter stewardship sites) and local, state, federal, and private protected areas managed to maintain natural land cover (hereafter protected areas)—as well as unprotected areas. We evaluated differences in trends between stewardship, protected, and unprotected areas across the Gulf and Atlantic coasts as a whole. Similar to a background sample, stewardship was known to occur at stewardship sites, but unknown at protected and unprotected areas. Four of 12 target species—Black Skimmer (Rynchops niger), Brown Pelican (Pelecanus occidentalis), Least Tern (Sternula antillarum), and Piping Plover (Charadrius melodus)—had more positive trends (two to 34 times greater) at stewardship sites than protected areas. Furthermore, five target species showed more positive trends at sites with stewardship programs than unprotected sites during at least one season, whereas seven species showed more positive trends at protected than unprotected areas. No species had more negative trends at stewardship sites than unprotected areas, and two species had more negative trends at protected than unprotected areas. Anthropogenic disturbance is a serious threat to coastal birds, and our findings demonstrate that stewardship to reduce its negative impacts helps ensure conservation of vulnerable waterbirds.     

Using decision science to evaluate global biodiversity indices

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Towards better integration of environmental science in society: Lessons from BONUS,the joint Baltic Sea environmental research and development programme

Integration of environmental science in society is impeded by the large gap between science and policy that is characterised by weaknesses in societal relevance and dissemination of science and its practical implementation in policy. We analyse experiences from BONUS, the policy-driven joint Baltic Sea research and development programme (2007–2020), which is part of the European Research Area (ERA) and involves combined research funding by eight EU member states. The ERA process decreased fragmentation of Baltic Sea science and BONUS funding increased the scientific quality and societal relevance of Baltic Sea science and strengthened the science-policy interface. Acknowledging the different drivers for science producers (academic career, need for funding, peer review) and science users (fast results fitting policy windows), and realising that most scientists aim at building conceptual understanding rather than instrumental use, bridges can be built through strategic planning, coordination and integration. This requires strong programme governance stretching far beyond selecting projects for funding, such as coaching, facilitating the sharing of infrastructure and data and iterative networking within and between science producer and user groups in all programme phases. Instruments of critical importance for successful science-society integration were identified as: (1) coordinating a strategic research agenda with strong inputs from science, policy and management, (2) providing platforms where science and policy can meet, (3) requiring cooperation between scientists to decrease fragmentation, increase quality, clarify uncertainties and increase consensus about environmental problems, (4) encouraging and supporting scientists in disseminating their results through audience-tailored channels, and (5) funding not only primary research but also synthesis projects that evaluate the scientific findings and their practical use in society – in close cooperation with science users − to enhance relevance, credibility and legitimacy of environmental science and expand its practical implementation.     

Value Plurality among Conservation Professionals

Abstract: Debate on the values that underpin conservation science is rarely based on empirical analysis of the values conservation professionals actually hold. We used Q methodology to investigate the values held by international conservation professionals who attended the annual Student Conference in Conservation Science at the University of Cambridge (U.K.) in 2008 and 2009. The methodology offers a quantitative means of examining human subjectivity. It differs from standard opinion surveys in that individual respondents record the way they feel about statements relative to other statements, which forces them to focus their attention on the issues they believe are most important. The analysis extracts the diverse viewpoints of the respondents, and factor analysis is used to reduce the viewpoints to a smaller set of factors that reflect shared ways of thinking. The junior conservation professionals attending the conference did not share a unifying set of core values; rather, they held a complex series of ideas and a plurality of opinions about conservation and how it should be pursued. This diversity of values empirically challenges recent proposals for conservation professionals to unite behind a single philosophy. Attempts to forge an artificial consensus may be counterproductive to the overall goals conservation professionals are pursuing.     

Evaluating the best available social science for natural resource management decision-making

Increasing recognition of the human dimensions of natural resource management issues, and of social and ecological sustainability and resilience as being inter-related, highlights the importance of applying social science to natural resource management decision-making. Moreover, a number of laws and regulations require natural resource management agencies to consider the “best available science” (BAS) when making decisions, including social science. Yet rarely do these laws and regulations define or identify standards for BAS, and those who have tried to fill the gap have done so from the standpoint of best available natural science. This paper proposes evaluative criteria for best available social science (BASS), explaining why a broader set of criteria than those used for natural science is needed. Although the natural and social sciences share many of the same evaluative criteria for BAS, they also exhibit some differences, especially where qualitative social science is concerned. Thus we argue that the evaluative criteria for BAS should expand to include those associated with diverse social science disciplines, particularly the qualitative social sciences. We provide one example from the USA of how a federal agency − the U.S. Forest Service − has attempted to incorporate BASS in responding to its BAS mandate associated with the national forest planning process, drawing on different types of scientific information and in light of these criteria. Greater attention to including BASS in natural resource management decision-making can contribute to better, more equitable, and more defensible management decisions and policies.     

毒死蜱在香蕉、土壤中的残留动态及安全性评价

为了监测香蕉中毒死蜱的残留量,科学、安全地使用毒死蜱防治香蕉上的害虫,确保产品质量,采用气相色谱法及田间试验方法,研究了毒死蜱在香蕉及其土壤中的残留降解动态.结果表明:在香蕉中,毒死蜱的原始沉积量较高,残留降解规律符合一级动力学关系,推荐剂量和加倍剂量处理的降解动态方程分别为y=1.0724e-014149t和y=1.7840e-0.1468t,半衰期(T1/2)为4.8 d,降解99％所需要的时间(T0.99)为31.4～31.8d;在土壤中,毒死蜱的残留降解动态方程为y=6.4365e-1412t,T1/2为4.9d,T0.99为32.7 d.距第2次施药后60～68d,最终在香蕉与土壤中均未检出毒死蜱残留量(检出限为0.005mg/kg).研究表明,漳州蕉区在香蕉生产上科学、合理地施用毒死蜱,其最终残留符合NY/T 750-2011规定的MRL要求,不会对蕉园土壤环境造成污染.